From 515fe23117049f7a47ddbada6cba3b13a9a4bf26 Mon Sep 17 00:00:00 2001
From: bjorn-stevens <64255981+bjorn-stevens@users.noreply.github.com>
Date: Thu, 11 Aug 2022 13:50:01 +0200
Subject: [PATCH] v0.3 release restructure and doctests
- doctests were corrected and now exist for most functions. `
- repository was restructured so example scripts are not outside of main
repository
---
.../examples-checkpoint.ipynb | 408 ++++++
.../saturation-water-vapor-checkpoint.ipynb | 1213 +++++++++++++++++
examples/data/cp-ice-vap.dat | 620 +++++++++
examples/data/cp-liq-vap.dat | 644 +++++++++
examples/data/psat.dat | 1000 ++++++++++++++
.../examples.ipynb | 10 +-
examples/saturation-water-vapor.ipynb | 1103 +++++++++++++++
.../saturation-water-vapor.ipynb | 1202 ----------------
setup.py | 2 +-
9 files changed, 4994 insertions(+), 1208 deletions(-)
create mode 100644 examples/.ipynb_checkpoints/examples-checkpoint.ipynb
create mode 100644 examples/.ipynb_checkpoints/saturation-water-vapor-checkpoint.ipynb
create mode 100644 examples/data/cp-ice-vap.dat
create mode 100644 examples/data/cp-liq-vap.dat
create mode 100644 examples/data/psat.dat
rename {moist_thermodynamics => examples}/examples.ipynb (99%)
create mode 100644 examples/saturation-water-vapor.ipynb
delete mode 100644 moist_thermodynamics/saturation-water-vapor.ipynb
diff --git a/examples/.ipynb_checkpoints/examples-checkpoint.ipynb b/examples/.ipynb_checkpoints/examples-checkpoint.ipynb
new file mode 100644
index 0000000..0c75eae
--- /dev/null
+++ b/examples/.ipynb_checkpoints/examples-checkpoint.ipynb
@@ -0,0 +1,408 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "b7c5c488-b68c-4504-85a0-bbe1575c7f65",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from moist_thermodynamics import functions as mt\n",
+ "from moist_thermodynamics import constants"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d38216a-175f-4c09-958b-56537aa8834f",
+ "metadata": {},
+ "source": [
+ "# Examples\n",
+ "\n",
+ "Usage of the moist thermodynamic functions is documented through a number of examples\n",
+ "\n",
+ "1. constructing a moist adiabat.\n",
+ "2. sensitivity of moist adiabat on saturation vapor pressure \n",
+ "3. lcl computations\n",
+ "\n",
+ "## 1. Constructing a moist adiabat\n",
+ "\n",
+ "This shows how simple it is to construct a moist adiabat. For the example it is constructed by assuming a constant $\\theta_\\mathrm{l}$ but the same answer (with the caveats of the next example) would arise if we were to define it in terms of constant $\\theta_\\mathrm{e}$ or $\\theta_\\mathrm{s}$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "0f765565-ed26-4cc7-a859-bebf9b020aea",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 504x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "es = mt.es_liq\n",
+ "p2q = mt.partial_pressure_to_specific_humidity\n",
+ "theta_l = mt.theta_l\n",
+ "Tl2T = np.vectorize(mt.T_from_Tl)\n",
+ "\n",
+ "Psfc = 102000.\n",
+ "Tsfc = 300.\n",
+ "Tmin = 190.\n",
+ "dP = 1000.\n",
+ "P = np.arange(Psfc,0.4e4,-dP)\n",
+ "\n",
+ "RH = 0.77\n",
+ "qt = p2q(RH*es(Tsfc),Psfc)\n",
+ "\n",
+ "sns.set_context('talk')\n",
+ "fig, ax = plt.subplots(figsize = (7,5), constrained_layout = True)\n",
+ "\n",
+ "Tl = theta_l(Tsfc,Psfc,qt)\n",
+ "TK = np.maximum(Tl2T(Tl,P,qt),Tmin)\n",
+ "ax.plot(TK,P/100.,label=f\"$\\\\theta_l$ = {Tl:.1f} K, $q_t = ${1000*qt:.2f} g/kg\")\n",
+ "\n",
+ "Plcl = mt.plcl(Tsfc,Psfc,qt).squeeze()/100.\n",
+ "\n",
+ "ax.hlines(Plcl,260,305.,ls='dotted',color='k')\n",
+ "ax.set_yticks([Psfc/100,Plcl,850.,700,500,200])\n",
+ "plt.gca().invert_yaxis()\n",
+ "plt.legend()\n",
+ "\n",
+ "ax.set_xlabel(\"$T$ / K\")\n",
+ "ax.set_ylabel(\"$P$ / hPa\")\n",
+ "#plt.grid()\n",
+ "sns.despine(offset=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2f6c280-e7b0-48ac-acc5-15053cabe4d0",
+ "metadata": {},
+ "source": [
+ "## 2. Sensitivity (small) of moist adiabat on saturation vapor pressure \n",
+ "\n",
+ "The derivation of the moist potential temperatures assumes a Rankine fluid, i.e., constant specific heats. Specific heats vary with temperature however, especially $c_i$. This variation is encoded in the best fits to the saturation vapor pressure, so that an adiabat defined in terms of a best fit saturation vapor pressure will differ depending on whether it assumes $\\theta_\\mathrm{e},$ $\\theta_\\mathrm{l},$ or $\\theta_\\mathrm{s}.$ This sensitivity vanishes (right plot, note $x$-axis scale) when we replace the more accurate saturation vapor pressures with less accurate expressions, albeit consistent with a Rankine fluid."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "321bddff-0bb6-4b3a-a3f0-1dae1c50c852",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x504 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "theta_e, theta_l, theta_s = mt.theta_e, mt.theta_l, mt.theta_s\n",
+ "Te2T, Tl2T, Ts2T = np.vectorize(mt.T_from_Te), np.vectorize(mt.T_from_Tl), np.vectorize(mt.T_from_Ts)\n",
+ "\n",
+ "Tmin = 190.\n",
+ "dP = 1000.\n",
+ "P = np.arange(Psfc,0.4e4,-dP)\n",
+ "\n",
+ "Psfc = 102000.\n",
+ "Tsfc = 300.\n",
+ "qt = 15.e-3\n",
+ "\n",
+ "sns.set_context('talk')\n",
+ "fig, ax = plt.subplots(1,2, figsize = (9,7), constrained_layout = True, sharey=True)\n",
+ "\n",
+ "es = mt.es_liq_analytic\n",
+ "TKl = np.maximum(Tl2T(theta_l(Tsfc,Psfc,qt,es=es),P,qt,es=es),Tmin)\n",
+ "TKe = np.maximum(Te2T(theta_e(Tsfc,Psfc,qt,es=es),P,qt,es=es),Tmin)\n",
+ "TKs = np.maximum(Ts2T(theta_s(Tsfc,Psfc,qt,es=es),P,qt,es=es),Tmin)\n",
+ "ax[1].plot(TKe-TKl,P/100.,label=f\"$\\\\theta_e-\\\\theta_l$\")\n",
+ "ax[1].plot(TKs-TKl,P/100.,label=f\"$\\\\theta_s-\\\\theta_l$\")\n",
+ "ax[1].set_title('es_liq_analytic')\n",
+ "\n",
+ "es = mt.es_liq\n",
+ "TKl = np.maximum(Tl2T(theta_l(Tsfc,Psfc,qt,es=es),P,qt,es=es),Tmin)\n",
+ "TKe = np.maximum(Te2T(theta_e(Tsfc,Psfc,qt,es=es),P,qt,es=es),Tmin)\n",
+ "TKs = np.maximum(Ts2T(theta_s(Tsfc,Psfc,qt,es=es),P,qt,es=es),Tmin)\n",
+ "ax[0].plot(TKe-TKl,P/100.,label=f\"$\\\\theta_e-\\\\theta_l$\")\n",
+ "ax[0].plot(TKs-TKl,P/100.,label=f\"$\\\\theta_s-\\\\theta_l$\")\n",
+ "ax[0].set_title('es_liq')\n",
+ "\n",
+ "plt.gca().invert_yaxis()\n",
+ "\n",
+ "ax[0].set_xlabel(\"$T$ / K\")\n",
+ "ax[1].set_xlabel(\"$T$ / K\")\n",
+ "\n",
+ "ax[0].set_ylabel(\"$P$ / hPa\")\n",
+ "ax[0].legend()\n",
+ "\n",
+ "sns.despine(offset=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b2fd8753-736f-459c-a435-0c50ad8eeae9",
+ "metadata": {},
+ "source": [
+ "## 3. Calculations of lifting condensation level\n",
+ "\n",
+ "We compare three different formulations of the lifting condensation level, one due to Romps (2017) is not included in the moist_thermodynamics library, but is included here for sake of comparision. The analysis shows that the simple bolton approximations work very well, as well as those of Romps if one uses the wagner saturation vapor pressure data. Had we performed this comparison with the analytic formula using the specific heats specified by Romps, the comparison would have been more favorable for the Romps formulation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a53539ae-7920-41b9-aa41-fed0031ce16b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Version 1.0 released by David Romps on September 12, 2017.\n",
+ "# \n",
+ "# When using this code, please cite:\n",
+ "# \n",
+ "# @article{16lcl,\n",
+ "# Title = {Exact expression for the lifting condensation level},\n",
+ "# Author = {David M. Romps},\n",
+ "# Journal = {Journal of the Atmospheric Sciences},\n",
+ "# Year = {2017},\n",
+ "# Volume = {in press},\n",
+ "# }\n",
+ "#\n",
+ "# This lcl function returns the height of the lifting condensation level\n",
+ "# (LCL) in meters. The inputs are:\n",
+ "# - p in Pascals\n",
+ "# - T in Kelvins\n",
+ "# - Exactly one of rh, rhl, and rhs (dimensionless, from 0 to 1):\n",
+ "# * The value of rh is interpreted to be the relative humidity with\n",
+ "# respect to liquid water if T >= 273.15 K and with respect to ice if\n",
+ "# T < 273.15 K. \n",
+ "# * The value of rhl is interpreted to be the relative humidity with\n",
+ "# respect to liquid water\n",
+ "# * The value of rhs is interpreted to be the relative humidity with\n",
+ "# respect to ice\n",
+ "# - ldl is an optional logical flag. If true, the lifting deposition\n",
+ "# level (LDL) is returned instead of the LCL. \n",
+ "# - min_lcl_ldl is an optional logical flag. If true, the minimum of the\n",
+ "# LCL and LDL is returned.\n",
+ "\n",
+ "def lcl(p,T,rh=None,rhl=None,rhs=None,return_ldl=False,return_min_lcl_ldl=False):\n",
+ "\n",
+ " import math\n",
+ " import scipy.special\n",
+ "\n",
+ " # Parameters\n",
+ " Ttrip = 273.16 # K\n",
+ " ptrip = 611.65 # Pa\n",
+ " E0v = 2.3740e6 # J/kg\n",
+ " E0s = 0.3337e6 # J/kg\n",
+ " ggr = 9.81 # m/s^2\n",
+ " rgasa = 287.04 # J/kg/K \n",
+ " rgasv = 461 # J/kg/K \n",
+ " cva = 719 # J/kg/K\n",
+ " cvv = 1418 # J/kg/K \n",
+ " cvl = 4119 # J/kg/K \n",
+ " cvs = 1861 # J/kg/K \n",
+ " cpa = cva + rgasa\n",
+ " cpv = cvv + rgasv\n",
+ "\n",
+ " # The saturation vapor pressure over liquid water\n",
+ " def pvstarl(T):\n",
+ " return ptrip * (T/Ttrip)**((cpv-cvl)/rgasv) * math.exp( (E0v - (cvv-cvl)*Ttrip) / rgasv * (1/Ttrip - 1/T) )\n",
+ " # The saturation vapor pressure over solid ice\n",
+ " def pvstars(T):\n",
+ " return ptrip * (T/Ttrip)**((cpv-cvs)/rgasv) * math.exp( (E0v + E0s - (cvv-cvs)*Ttrip) / rgasv * (1/Ttrip - 1/T)) \n",
+ "\n",
+ " # Calculate pv from rh, rhl, or rhs\n",
+ " rh_counter = 0\n",
+ " if rh is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rhl is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rhs is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rh_counter != 1:\n",
+ " print(rh_counter)\n",
+ " exit('Error in lcl: Exactly one of rh, rhl, and rhs must be specified')\n",
+ " if rh is not None:\n",
+ " # The variable rh is assumed to be \n",
+ " # with respect to liquid if T > Ttrip and \n",
+ " # with respect to solid if T < Ttrip\n",
+ " if T > Ttrip:\n",
+ " pv = rh * pvstarl(T)\n",
+ " else:\n",
+ " pv = rh * pvstars(T)\n",
+ " rhl = pv / pvstarl(T)\n",
+ " rhs = pv / pvstars(T)\n",
+ " elif rhl is not None:\n",
+ " pv = rhl * pvstarl(T)\n",
+ " rhs = pv / pvstars(T)\n",
+ " if T > Ttrip:\n",
+ " rh = rhl\n",
+ " else:\n",
+ " rh = rhs\n",
+ " elif rhs is not None:\n",
+ " pv = rhs * pvstars(T)\n",
+ " rhl = pv / pvstarl(T)\n",
+ " if T > Ttrip:\n",
+ " rh = rhl\n",
+ " else:\n",
+ " rh = rhs\n",
+ " if pv > p:\n",
+ " return N\n",
+ "\n",
+ "# Calculate lcl_liquid and lcl_solid\n",
+ " qv = rgasa*pv / (rgasv*p + (rgasa-rgasv)*pv)\n",
+ " rgasm = (1-qv)*rgasa + qv*rgasv\n",
+ " cpm = (1-qv)*cpa + qv*cpv\n",
+ " if rh == 0:\n",
+ " return cpm*T/ggr\n",
+ " aL = -(cpv-cvl)/rgasv + cpm/rgasm\n",
+ " bL = -(E0v-(cvv-cvl)*Ttrip)/(rgasv*T)\n",
+ " cL = pv/pvstarl(T)*math.exp(-(E0v-(cvv-cvl)*Ttrip)/(rgasv*T))\n",
+ " aS = -(cpv-cvs)/rgasv + cpm/rgasm\n",
+ " bS = -(E0v+E0s-(cvv-cvs)*Ttrip)/(rgasv*T)\n",
+ " cS = pv/pvstars(T)*math.exp(-(E0v+E0s-(cvv-cvs)*Ttrip)/(rgasv*T))\n",
+ " X = bL/(aL*scipy.special.lambertw(bL/aL*cL**(1/aL),-1).real)\n",
+ " Y = bS/(aS*scipy.special.lambertw(bS/aS*cS**(1/aS),-1).real) \n",
+ " \n",
+ " lcl = cpm*T/ggr*( 1 - X)\n",
+ " ldl = cpm*T/ggr*( 1 - Y)\n",
+ "\n",
+ " # Modifications of the code to output Plcl or Pldl\n",
+ " Plcl = PPa * X**(cpm/rgasm)\n",
+ " Pldl = PPa * X**(cpm/rgasm)\n",
+ " # Return either lcl or ldl\n",
+ " if return_ldl and return_min_lcl_ldl:\n",
+ " exit('return_ldl and return_min_lcl_ldl cannot both be true')\n",
+ " elif return_ldl:\n",
+ " return Pldl\n",
+ " elif return_min_lcl_ldl:\n",
+ " return min(Plcl,Pldl)\n",
+ " else:\n",
+ " return Plcl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "9b2830db-855d-467d-ac66-cc9154ab7caa",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/m219063/opt/miniforge3/lib/python3.9/site-packages/scipy/optimize/_minpack_py.py:175: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
+ " improvement from the last ten iterations.\n",
+ " warnings.warn(msg, RuntimeWarning)\n",
+ "/Users/m219063/opt/miniforge3/lib/python3.9/site-packages/scipy/optimize/_minpack_py.py:175: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
+ " improvement from the last ten iterations.\n",
+ " warnings.warn(msg, RuntimeWarning)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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1NZXZs2dz++23l/m+1NRUvvrqK0aM8H4CpOeW++ZuTuaZGRsBuHhAS645qa27AVXATcM7cCg9l4m/buPDRbtoXDeGe0Z1Pv4bQ0gwdAEU3vmX1wxYr9R+FSIiTwN3A78CY4wx/jPaS6kaEhdnT5t169b9bvt9991HeHg4N9100++2jxo1ivj4eBo0aMCuXbu47rrrfBarcse+1Gzu+HAZHgO9WsTxr3N7Bsyd9ANju3JR/5YAvPDzZr5ascfliPxLMCQAm5zHTqVfcKYItgMKgG0VPaCIPAfci60HMNYYk14DcSrld8477zxiYmK46aab+Oijj/j666+54IILePPNN5k8eTKdO//+junHH38kJSWFzMxMzjnnHMaMGeNS5MoXPB7DfZ+t5EhmHvG1I3n1yn4BNaBORHji/F6c5ExRvO+zVSzfqZUtCwVDAlA4DPuMMl4bCsQC8ysyA0Csl7HN/T8CZxljMmsqUKX8Tdu2bfn+++9p3LgxEyZM4KqrriIzM5PZs2dzySWXlPu+6OhorrrqKpYvX05ycrIPI1a+9M78ROZstv++T51/Ai3rB16XV2R4GK9c0Y+2DWuTm+/hpneXsv+oNuhC8CQAm4ARIlJUwswpBPSo8/S1Ettri0hXEWld8iDOgL/XgVuBacA5xpgsbwevlNtGjBjBwoULyc7OJjU1lWnTpjF06NBjvicvL48PPviApk2b/m72gAoem/en8dS0DQBc1L8lY3r6tspfTYqvHcWbEwZSNyaCg2k53P7BMvIKPMd/Y5AL+EGAxpgCEbkW+BmY6hTtScKWAu4BTAU+KPGWQdikYTa2sE+h/wNuALKAFcADZfRzzSpdKVCpUHLaaacRFhZGWFgYPXv2ZOrUqQHTH6wqrsBjuPfTleTke2hZvxb/d3Z3t0Oqtg6N6/DsxX24ccoSFice4ZkZG/nbmd3cDstVAZ8AABhj5onIYOAR7IU/Ftvnfz/wXAXWAQA7VgCgFvC3Y+w3qxqhKhWwEhMT3Q5B+ci78xNZuduOm/7vRb2pGxPpckQ1Y1T3ptx8Sgdem72Vib9uo3+b+j5fv8CfBEUCAGCMWQWMr8B+syijNrIxZgIwoabjUkqpQJKUmsUzP9ix1ZcObMXg9sHVxXPvGZ1ZtvMIi7Yf5r7PVnFCy3gSqlBDIxgEwxgApZRSNeSRr9eRnpNPozpRPDC2q9vh1LiIcLtyYf3akaRm5fGXT1fgCdFKgZoAKKWUAuCXjQeYvnYfAA+N60587SiXI/KOpvVieOqCEwD4bcsh3py73eWI3KEJgFJKKfIKPDz+na12PqR9Q87p3dzliLxrdI8ELhvUCoD/zNjAhn1Hj/OO4KMJgFJKKT5YuJMtB9IRsXf/oTC746Fx3WnXKJa8AsP9n60iP8SmBmoCoJRSIS41M4/nfioe+Ne9eWgs8Vw7KoKnzu8FwKrdqUz+LbS6AjQBUEop7Nz37LwCsvMK3A7F516cuZmUzDzqREdwz6gubofjU4PbN+SqE9sA8N8fNpGYnOFyRL6jCYBSSgEPf72Wrg9N5+6PV7gdik/tTcliyvwdANxyagca1412OSLfu39MF5rHxZCT7+Gvn68KmVkBmgAopRQQHmb7vPND5Mu/0Iszt5Bb4KFx3WiuO7nd8d8QhOrGRPK40xWwcPthPly80+WIfEMTAKWUAiLDnQQghAaC7TiUwadLdgFw+4iO1IoKnJX+atqILk04v28LAJ78fgMH0oJ/wSBNAJRSClsgBkKrBeB/P20m32NoEV+LS50pcaHsoXHdqV87kvSc/KKFkIKZJgBKKQVEOl0AobJK3JYDaXy5Yg8Afz69I9ERoXv3X6h+bBT3j7HVD79YtoelOw67HJF3aQKglAJgzpw5jBs3joSEBKKjo2natCnjxo3jyJEjbofmE0UtAAWh0QLwyqytGANtGtbmgn4t3Q7Hb1w8oBUntIwD4P++WktBELcIaQKglGLjxo2cfvrpxMXF8dZbbzFz5kzefPNNunXrRmxsrNvh+USEMwYgL4i/8AvtOpzJVyv2AnDLKR2Kkh9lB4M+ck4PANbuPcqHi4J3QGDQrAaolKq6n3/+mby8PM466yzGjh1btH3cuHEuRuVbEU4XQIEn+LsAJs3ZRoHHkFAvhvH9Wrgdjt/p27o+Fw9oySdLdvPMDxs5q1cz6scG37oImvYppbjgggvo2LEjV1xxBQkJCVx55ZXMnDnT7bB8KiIsNLoADqbl8PFiO/L/hmHttO+/HPeP6UrdmAhSMvN45oeNbofjFdoCoFRVFeTD0T1uR1GsXgsIr9op/eKLL5KQkMCHH35IUlISr776KiNHjuS5557jzjvvrOFA/VPhNMBgHwQ4+bft5OR7qF87kssGtXY7HL/VqE40d4/szL++XcdHi3dx7clt6dikrtth1ShNAJSqqqN74H8nuB1FsTtXQf02lX7b448/zuTJk1m9ejUNGzYE4KyzzqJXr1489thjRQlA27ZtOXDgAGFhYdSrV49LLrmEZ555hvDw4LiDDIVpgBk5+by3wFb9m3BSO2Kj9RJwLFcNacOU+YkkHsrk39M3MunqAW6HVKO0C0CpEHbgwAEeffRR7rjjjqKLP0BYWBgDBw7k0KFDeEr0ic+cOZP09HTmzJnDJ598wuTJk90I2ysKxwAEcxfA58t2k5adT3REGFcNqXyyGGoiw8OKpgX+uG4/i7YH17RATf+Uqqp6Lexdt7+oV/nBXNOnTycnJ4fRo0f/4bWkpCTatGlDWNgf7xM6dOjAySefzIoVK6oSqV+KdFoAgrULwOMxvPVbIgDn92tBgyAc1OYNY3sm0KdVPCt2pfDE9+v58taTgmapZE0AlKqq8IgqNbn7k+3b7fKnCQkJv9uemprK7Nmzuf3228t836ZNm5gzZw733nuv12P0lfCiWQDB2QIwa9MBtjsr3V0bojX/q0JE+PuZ3bh44nxW7Eph2pp9nNmrmdth1QjtAlAqhMXF2YIn69at+932++67j/DwcG666abfbR81ahR16tShS5cuDB48mNtuu81nsXpbsA8CnDw3EYBhnRrRuWlwDWbztkHtGjCqe1MA/jN9Q9D8jWgCoFQIO++884iJieGmm27io48+4uuvv+aCCy7gzTffZPLkyXTu3Pl3+//444+kpaXx9ddfs3z5ctLS0lyKvOYVTQMMwhaAjfvSmLslGSBkV/yrrr+O6UKYQOKhzKApDqQJgFIhrG3btnz//fc0btyYCRMmcNVVV5GZmcns2bO55JJLynyPiHD22WczcuRIHn/8cR9H7D0R4cE7CPD9hXbkf7tGsZzSubHL0QSmjk3qcslAu2DSy79sITuvwOWIqk8TAKVC3IgRI1i4cCHZ2dmkpqYybdo0hg4detz33XfffbzxxhskJyf7IErvKxoEGGSVADNy8vlima1XccXg1oSFBccANjfcflonIsOF/Udz+GBh4LcCaAKglKqSrl27csopp/C///3P7VBqROE0QGOCayDg1yv3kp6TT1REmC76U00t4mtx6UBbPOmVWVvJyg3sVgCdBaCUqpDExMQ/bPvuu+98H4iXFHYBAOR7PISHBX6BI2NMUeGfcUFaz97Xbh3RgY+X7CI5PYf3FuzgxuHt3Q6pyrQFQCmlKB4ECMEzDmDV7lTW7j0KwBUnatnfmtAsrhaXOyWUX5u9lczcfJcjqjpNAJRSilItAEGSABQO/uuaUJd+reu7HE3wuOXUDkRHhHEoI5cp83e4HU6VaQKglFIUDwKE4BgImJ6TzzcrkwC4fHDroKle5w+a1ovhyhNtEbCJs7eSnhOYrQCaACilFMWDACE4WgC+X5VEVl4BURFhnNu78mWi1bHdfEoHYiLDOJKZVzTOItBoAqCUUpRqAQiCSm+fLt0FwOgeCcTVjnQ5muDTuG40lw+yrQBvzt0ekHUBNAFQqgLCwsIoKAi8E9wfFBQUlLmgkL8JL9ECEOjTABOTM1iceASAi/rr1D9vuXF4OyLDhYNpOXy2dLfb4VSa/5+VSvmBmJgYcnJyOHw4uJYD9bbDhw+Tk5NDTEyM26EcV+lpgIGs8GLULC6Gkzs2cjma4NUsrhbj+9rulYm/biU/wFqOtA6AUhXQqFEjcnJy2L9/PykpKYSHB/4ccW8rKCggJyeHunXr0qiR/1+EIsNKdgEEbgtAgcfw+TKbAFzQr+XvWjZUzbv5lA58unQ3uw5n8e2qJM7rGzjjLbQFQKkKEBFatGhBo0aNiIzU/tSKiIyMpFGjRrRo0SIgRqAHyzTAeVuTSUrNBuBCbf73uvaN63BmT7s88KuztuIJoO4jbQFQqoJEhMaNdSGVYBUs0wC/XG7r/g9oU5+2jWJdjiY03HJqB75bncTG/WnM3HCAkc7Swf5OWwCUUorgmAaYlVvAjDX7AAKqKTrQ9WwRV7TK4iuztmBMYPz9aAKglFL8fhZAoA4C/HnDfjJyC4gIE87s1cztcELKrad2AGDZzhSW7jjicjQVowmAUkphu3gKWwECtQVg6vK9AJzSuTENdOEfnxrUrgG9W8UD8Mac7e4GU0GaACillKNwIGAgtgCkZOYye9MBAM7V5n+fExFuGNoOgBnr9rHjUIbLER2fJgBKKeUonAoYiNMAv1+9j7wCQ+2ocEZ1C4xBaMFmbM8EWsTXwhh467dEt8M5Lk0AlFLKUdQCEIAJwFcr7Oj/0T0SqBWldSrcEBEexoST2gLwyZJdpGbluRvQcWgCoJRSjghnKmCgdQEcSMtmUaKtUnlO7+YuRxPaLhnUitiocDJzC/ho0U63wzkmTQCUUsoRqIMAZ6zdjzFQNyZCS/+6rF5MJJcMbA3A2/MS/XphKU0AlFLKEaiDAKetTgJgVPemREXo17rbrj25LWECSanZfO/82/gj/UtRSilHIA4CPJSew4JthwCKStIqd7VqUJsxPRMAu1Swv9IEQCmlHMWDAAOnBeCHdfvxGKgTHcHQTtr87y+ud6YErtqdyopdKe4GUw5NAJRSXmOMYeWuFA6m5bgdSoVEhBUOAgycFoDCJubTuzUhJlJH//uLfq3r06N5PQCmzEt0N5hyaAKglKpxB9NymPTrNkY//yvnvvwbnyzZ5XZIFRLptAAEShfAkYxc5m21zf9jtfnfr4gIVw9pA8C3q5JITve/JFhXA1RK1Yi8Ag8zNxzg0yW7mbXxwO/uov21CbS0wvUACgJkEOCP6/dT4LHFf07toitV+ptzerfgie83kJqVx8eLd3HbiI5uh/Q7mgAopapl56FM3lu4g8+X7uZQRm7R9noxEZzbpwUXDWhJrxZxLkZYcYV1AAKlBaBw9P+Irtr8749qRYVzycBWvP7rNt5fsIM/DW9f9DfmDzQBUEpVmsdjmL35IO/O38EvGw9QuPqpCAzr1JiL+rdkVPemAXdRigygaYCpWXnM3ZIM6Oh/f3bl4DZMmrONvanZ/LT+QNHsAH+gCYBSqsJSM/P4dOku3luwg8RDmUXbm8XFcPmg1lw4oCXN4mq5GGH1FA0CDIAWgJ/X7yevwBATGabN/36sdcPajOjShJkbDjBlfqImAEqpwLLrcCZvzt3Ox4t3kZVXULT9pA4NuXpIW0Z2a+JXTZtVFUiDAKev2QfYpX9jo/Wr3J9dNaQNMzccYN7WQ2w5kEbHJnXdDgnQBEApdQxr96by+q/b+HZVEgXOoL7YqHAu6N+Sq05sQ6em/vFFVlOKpwH6dxdAdl4Bczbb5v9R3f3njlKV7ZROjWnTsDY7DmUyZf4O/nVuT7dDAjQBUEqVYoxh/tZDvDp7a9FFBiChXgzXD23HpYNaUTcm0sUIvSe8aAyAf7cAzN92iKy8AkRghDb/+72wMOHKwW14/Pv1fLlsD38b280vVmwM/DY7h4j0EpEvRCRZRLJEZK2I3CcilUpyRGS4iMwQkSMikiEiS0Tkem/FrZS/MMYwe9NBxr8yj8vfWFh08e/UpA5PX3gCv94/ghuHtw/aiz9AZFhgVAL8ef1+APq3rk/DOtEuR6Mq4oL+LYkKDyMtJ59vV+11OxwgSFoAROQk4CcgHPgE2AucBfwHOElEzjfGHDelF5ELnPdnAB8BacAFwBsi0ssYc5d3fgOl3GOM4bcth3jup00s3XGkaPvAtvW5+ZQOjOjShDDnwhjsipYD9uMxAMYYfl5/AIDTuzV1ORpVUQ1ioxjTM4GvV+7lw0U7uWhAK7dDCvwEQETCgclALeBMY8w0Z/tD2KTgPOBy4P3jHCcOmAjkAcONMSuc7f8CFgF3isinxpjfvPObKOV787Ym89yPm1icWHzhH9K+IXeN7MTg9g1djMwdRYMA/bgLYO3eoySlZgMwslsTl6NRlXHZoNZ8vXIvy3amsGHfUbom1HM1nmDoAhgBdAF+Kbz4AxhjcoGHnKc3V+A4FwENgQ8KL/7OcVKBJypxHKX83spdKVz2+gIun7Sw6OI/qF0DPrzxRD686cSQvPhDyWmA/tsFUHj337pBbTo2qeNyNKoyTmzfgHaNYgH4aJH75bEDvgUAmwAA/FDGa3OxzflDRCTaGHOsYszHOs4M5/HUKkWolJ/YeSiTp3/YyDcri/sgB7Spz92jOnNSh4aIhEZTf3kiAmAa4M8bbP//6d2ahPy/V6ARES4b1Ionvt/AF8t289cxXV0dDBgMCUBn53Fz6ReMMQUish3oCbQH1lfxOPtEJB1oKSK1jTGZpfcRkZTjxBkHpB5nH6W84khGLi/O3MK7CxKLLm7dmtXjgbFdGd6pkV9fSHx5bkX4+VoA+49ms2q3/VVHav9/QLqgX0uenrGRo9n5fL86iQv6t3QtlmBIAAqLjJf3BXDUeYyvgePUcfb7QwKglD/Kzivg7XmJvPzLFtKy8wFoHhfDX87owvi+LUJmcF9FFQ0C9NMxADM32Ob/utERDGzbwOVoVFU0rBPN6B4JfLsqiQ8X7dQEwEdq6owu8zjGmPhjvakCdzFK1aif1+/nkW/WsfOwzVfrRkdw64iOXHty24Cq0e/Lc6twGmCen44BKJz+d0qXxkRFBMMQrtB0+aDWfLsqiSU7jrBpfxqdXSqoFQwJQOEde3nLjdUrtV91j3O0nNeV8guJyRn869t1RXeLEWHCVUPacMdpnWgQG+VydP7Nn6cBZucVFC3+o83/ge3E9g1p27A2iYcy+XjxLh4a192VOKqcAIjIAGAwUJ8/ziYwxphHqxNYJWxyHjuVfsGZItgOKAC2VeA4A5zjLC11nARs8//usvr/lfIHmbn5vPzLFib9up1c5w52aMdGPHxOd7+pPe7vIvx4GuBvW5LJzvMQHia6+E+ACwsTLhrQiqdnbGTq8j08MLYrkS6spVHpBEBEagFfAGcAgm0SL+xINCW2+SoB+AX4uxPPU6VeGwrEAnOPMwOg8DiXO8f5qNRro53HWdWKVCkvMMYwfc0+/vXtuqL54S3ia/HQuG6M7pHg1wP8/E2kH08DnL3pIAD9WscTX1tbcgLd+f1a8N8fNnIoI5eZGw4wuofv13SoSsrxf9iL5OPYqXMCXAOMBeYAiwFftmf8gr17HyEiYws3ikgUxUnIayW21xaRriLSutRxPgUOAZeLSJ8S+8dhEwywhYKU8htJqVncOGUpt7y/jKTUbKIiwvjz6Z346Z5TGNOzmV78Kym8aBaA/7UAFCYAp3TWu/9g0CyuFsM62X/LT5e4UxOgKgnAhcCnxpj/A9Y42/YYY2YAI4EoYELNhHd8xpgC4FogG5gqIlNE5N/AMmAYMBX4oMRbBmGnA04pdZxUbKGfSOBXEXldRP4LrMROEXzBGDPXy7+OUhVS4DG8My+RUc/+yk/OwLARXRrz092ncM+ozn6x0EgRY8BPp9WVVrwcsH/Fm5icwY5DtvfxlM5a/S9YXDTAzgD4ZeNBDqRl+/zzq5IAtAJmO/9duDB4FIAxJh/4ELi0+qFVnDFmHnY8wvfYNQDuxP5u9wMXVWQdAOc4nwGnAQuAS4BbsK0CNwJ31XjgSlXBxn1pXPjaPP759VrSc/JpVCeKFy/ry+QJA2ndsLbb4RXLOATzXoSXBsD6r92OpkL8dRrgr5vt3X/D2Ch6NHe3fKyqOSO7NSWuViQFHsPU5Xt8/vlVGQSYVuJ9aYAHaF7i9VTA550ZxphVwPgK7DeL4jELZb0+m+IERym/kZNfwEszt/DqrK1FF6iLB7Tk72d2858+YWMgcS4sfdte9Aty7falb0OP81wMrGIiilYD9LMEwGn+H9apkdZuCCIxkeGc26c5U+bv4NMlu7lxWHufdttVJQHYilM1z6m0txbbLTBZbOTnA+4XOVYqiKzbe5R7PlnBhn1pALRtWJsnzu/FSR0auRyZI/MwrPjAXugPlSimGV0PTrgE+k9wK7JKKRyJ7U9dALn5HuZtPQTY+f8quFw8oBVT5u9g84F0VuxKoW/r+j777KokAD8B14nIXU7/+0TgJRHZih39347iQXNKqWrIL/Dw2uyt/O/nzeQVGMIEbhregbtGdnK/mI8xsHM+LHkL1k0tvtsHaDnQXvR7jIeoWLcirLTCQYD+1AWwZMdhMnNtb2vhoDEVPHo0r0fXhLps2JfGp0t3+30C8BTwLk4zujHmFRGJAa7EjgmYBPynxiJUKkRtOZDOXz5ZwUqn9nv7RrE8c3Fv+vnwC6JMWUdg5cew9C04uKF4e3Q9OOFie+FP6OVaeNXhjy0AhaP/e7aoR6M60S5Ho2qaiK0J8Oi36/hmxV7+b1x3nyX3lU4AjDHpwMZS254Fnq2poJQKZR6PYfJv23l6xkZy8u2F6NqT23L/aBdXDjMG9iyFJZNhzeeQX2LEcvN+MOBa6HlBQN3tlyXaKa+bm+8/CcCvm2z1v+F69x+0zuvTnCe/X09aTj4/rtvP2b2bH/9NNaBSCYCINMauqpdsjNnqnZCUCl37j2Zzzycr+G2L7fNtEV+Lpy86wb2+/pw0WP2pvfDvW128PaoO9LrIXvib9XYnNi8oTABy8j0YY1yvo7D/aDbrk2z1cZ3/H7wa1onm1C5N+Gn9fqYu3+NfCYCIhAGvADfgNP2LyHxgvDHmoPfCUyp0/Lx+P/d+upIjmXkAXDKgFf8Y1426MZG+D2bfanvRX/UJ5KYXb2/ay170T7gYooOvvHB0iabX3AIP0RHujrMoHP1fJzqCfm1c7vpRXjW+bwt+Wr+f2ZsOcig9h4Y+6O6paAvA7cBNwF5gPrZe/knYAYDneyc0pUJDdl4BT03bwNvzEgGIqxXJvy84gTE9fTybNi8L1n5pL/y7Fxdvj4ixzfsDroMW/SGIqwtGl1hhLyffDxKAzbb5/6QODV2pFa985/RuTagbHUFaTj7frkrimpPaev0zK5oAXI2tnneiMSYNQEQmARNEJN4Yk+Kl+JQKapv3p3HHh8uLpvcNateA5y/pQ/P4Wr4L4tBWe9Ff8b4d4FeoUWd70e99KdQKjbvPmMjii2x2XgH13Gh9cRR4DHOcAkDDtfk/6MVEhjO2VwKfLNnNl8v3+FUC0AX4V+HF3/EicD22JsCimg5MqWBmjOHjxbt4+Ju1RSu83XV6J24d0bFoKppXFeTDxu9hyZuwbVbx9rBI6Ha2vfC3HRrUd/tlKXnHn5Pn7kDANXtSSXG6g7T/PzSM79uST5bsZsWuFLYnZ9CukXcH1VY0AYjFNv+XtLfEa0qpCsrKLeDBL1fzhVP6s0V8LV64rA/92zTw/ocfTYJl78DSdyCtxCkd18pO3+t3NdQJ3VrzpbsA3PTbVtv836ZhbVo18KMSz8prBrdrQLO4GJJSs5m6fA93j+rs1c+rzCyA0pUxCp+H1i2CUtWw7WA6t7y3jI37bWPamB4J/PvCE4ir5cWmZmMgcQ4sfgPWfwumcAkPgU6jYMD19jHMjxYQcsnvWgDyC46xp/fNc2aC+E21R+V1YWHCuX1a8NrsrUxdsYe7Rnby6kyUyiQAZ4pIyVFJtbFJwEUll891GGPMc9UNTqlgMm11Evd9tor0nHzCw4S/je3K9UPbee8Ez06FlR/ZC3/ypuLttRvaO/3+E6B+W+98doCKjvSPFoDsvAIWJx4G4OSODV2LQ/ne+L42AdhxKJNlO1Po78XZH5VJAC53fkr7UxnbDKAJgFLYqnJPTdvAm3O3A9C0XjQvXd6PgW291OS/bw0snmSn8OVlFm9vdSIMvAG6nwMRWlGuLL/rAnBxDMCynUeKEpAh7TUBCCVdEurSrVk91icdZeryPX6RAIzwWgRKBbEDR7O59f1lLNlhR9cPad+QFy7rS+O6NXwBzs+1q+8tmgS7FhRvj4y1c/YHXh+w5Xl9SUSIiggjN9/jahdAYfN/t2b1fDIfXPmX8/u24PGko3yzai8PjetOVIR3poBWKAFwlshVSlXCyl0p3PTuEvYfzQHg1lM7cM+ozkVrzteI1D22Jv/SdyDjQPH2Rp3t3X7vSyEmruY+LwREOwlAtostAIUDAE/uoHf/oeicPs15Ytp6UjLzmL3pIKO6N/XK51RlMSCl1HF8sWw3D3yxmtx8D3WiI3jukj41dxIXDupbNAk2fFc8qE/CoeuZMPBGaDc85Kbw1ZToiHDSyHetBSAtO49VzgJQJ3fUAYChqGm9GE7u0Ii5W5KZunyPfyUAIjIEWx2wE9CQP84EMMaYDtWMTamAk1/g4d/TNzBpju3vb9uwNm9cM4COTWqgbG5OWvGgvpKr8MU2tgP6+l8LcS2q/zkhruR6AG5YuO0wBR5DRJgwqJ0PpoYqv3Re3xbM3ZLMj+v3czQ7zytFqSqdAIjI1cBbQB6wCdhZ00EpFYhSM/O4/cNlzHHKtw7v3JgXL+1LXO1qnrjJW+ygvhUfQM7R4u2tBtu7fR3UV6MKqwG6lQAUNv/3aRVPbLQ20oaqMT0TePDL1eTke/hh7X4u7N+yxj+jKn9dD2KXAx5pjCldHEipkLTlQBo3TlnK9uQMAP40vD33j+la9ap+ngLY/CMseh22/ly8PSIGel0Ig24KqlX4/ElhLYCcPHe6AIrm/2vzf0irEx3B6d2a8P3qfXy7aq/fJABtgPv04q+U9duWZG5+bylp2flERYTx7wt6Mb5vFU/WrBRY/p694z+SWLw9vrW92+97JdTWZmFvinaxBeBgWk5RkSgdAKjGndCc71fvY+7mZI5k5FI/NqpGj1+VBGA3oO2NSgGfLN7F379cTb7H0KRuNJOuHkDvVvGVP9CBDbBoou3jLzl3v/0IGPwn6HSGVurzETfHAMxzmv9rRYbTt3VoLMCkyjeiSxNqR4WTmVvA9LX7uGxQ6xo9flUSgNeAK0TkOWOMu7UylXKJx2N45oeNvDJrKwBdE+oyecLAyq3i5ymATTNg4WuwvcRM26g60Psy28zf2Lu1wNUfudkFUNj8P7BdA6/N/VaBo1ZUOKO6N+WrFXv5dtVe3ycAIjK81KYlwAXAIhF5GdgO/OFMMcb8WiMRKuVnsvMK+MunK/luVRJgV2p76fK+1K3oKN3CZv5Fr0PKjuLtDdrDoD9Bn8shpl7NB64qxNUWgG06/1/93rgTmvPVir3M33qIg2k5NVpErCItALP440JAhSOb3ijnNQNoe6UKOofSc7hxyhKW7UwB4IrBrXnknB4VK+6TvBkWTrSj+fMyird3HAmDb4YOp0OY3vW5LSbSaQHwcR2APSlZ7DqcBcAQTQCUY3jnRtSNiSAtO59pa5K4ekjbGjt2RRKAa2vs05QKYNuTM7hm8iJ2Hs5EBB48s9vxF/PxeGDrTFj4Kmz5qXh7ZKy90x/8J2jUyfvBqworagHwcSXARdtt83+d6Ai6N9MWIGVFR4RzRvcEPl+2m29X+jgBMMa8U2OfplSAWrU7hWvfWsyhjFxiIsN4/pK+jOmZUP4bcjPsgL6Fr/1+Jb74Nvai3/dKLdHrp9yaBbBou139r3+b+jVbLloFvLN7N+PzZbtZvOMw+1KzSYiLqZHjapUJpY7j100Hufm9pWTmFhBfO5LJEwbSr7wR2qm7bYnepW9Ddkrx9rbD4MRboPMYHc3v54oGAfq4C2DhNpsADG6v0zzV753csRH1a0dyJDOP71Yncf3QdjVyXE0AlDqGqcv3cO+nK8n3GFrE1+Kd6wbRsUmdP+64ewnMfxnWfVVcmz88Gk64CAbfAgk9fRu4qrLCLgBfLgZ04Gg225wiUoO1/K8qJTI8jDE9E/hw0S6+WblXEwClvO2NOdt47Lv1gJ3m9851g2har0TTW0E+bPjGXvh3Ly7eXqepXYmv/7VQp7GPo1bV5UYLwKJEe/cfExlGrxbxPvtcFTjOPqE5Hy7axYpdKew6nEmrBrWrfUxNAJQqxeMxPDltfdGCPoPaNWDS1QOIq+VM88tOhWVTYOHrkFpiKYyEE2DIbdBjvNbmD2BurAVQ2Pzfr3V9nf+vyjS4fUMa1YkmOT2H71YncfMp1V9vTxMApUrIL/DwwBer+WzpbgDG9Ejg+Uv72KlhRxJhwWuw/F3ITXfeIdDlTBhyK7Q5WZfgDQJuzAIoHAA4uJ1O/1NlCw8TzuyVwJT5O/h21V5NAJSqSbn5Hu76eDnfr94HwOWDW/PouT0J37MY5r8E678B41wUImPtSP7Bf4KGuvJ1MIn2cR2Awxm5RfX/dflfdSzjTmjOlPk7WLPnKNuTM2jXKLZax6tQAiAiu4Cpzs8sLQGsgk1WbgG3vL+UWRsPAnDL8Lbc32YzMvke2L2oeMd6LexFv981UCvenWCVV/m6EuBip/8/KjyMvq3jffKZKjANaFOfpvWi2X80h2lrkrj11I7VOl5FO5u+Bs4DfgQOisi7IjJeRKo/CkEpl6Vl53HNW4uYtfEgtchmSs/l/HXTZcin1xRf/Jv1hvPfgDtXwsl36sU/iBUPAvRNAlDY/9+7VVxRFUKlyhIWJozt2QyAaU5LZXVUqAXAGHMbcJuIDALGY5OBK4BsEfkR+BL4xhhzqNoRKeVDRzJymfDWIvbu3sFfIn7gppiZRG85WrxD57Fw0u3avx9CiqcB+qahc1Gi/drU/n9VEWN7JvD2vERW70mt9myASo0BMMYsAhYBfxORrhQnA28CHhGZi00GvjLG7Cj3QEr5gQNp2Tw48TMuS/mU8dFziZZ8yAciYuxqfENu0zK9Iah4LQDvtwAczc5j3V6bcGr/v6qIAW0bFM0GmLYmiZuGV30MUpXnmxhjNhhjnjTGDAZaA3djVwV8BtgmIstEZEyVI1PKW4zh0NpZbHl+HJPSb+PSiFn24l+rAZzyANy1Bs5+Xi/+IaqwFHCBx5Bf4N0kYGniETzGjvDu36ac6pJKlRAeJozp2RSgaMByVdXILABjzB7gJeAlEYkHzsG2DPQEptfEZyhVbZ4C2Pg9ub8+R8OkpZzkbM6s04bap/wZel8OUTqsJdRFl5iHn5Pv8Wpd/gXOAkA9W8QRG62TslTFnNmzGe8t2MmKXSnsTcmieXytKh2nxv/ijDEpwBTnRyn35WXDqo9g3otwaAtRzuYVpiPRw++m24jLtD6/KlI4CBBsAhDrxZpOhfP/T9Tmf1UJg9o1oEFsFIczcpm+Zh/XVbE0sJacUsErOxXmPAvP94Jv7oRDWwD4qaAvVxT8k8wrp9Pt9Cv14q9+5/ctAN4bCJiZm8/q3amA9v+ryokID2N0D9sNMG1NUtWPU1MBKeU30vbBgldg8WTItQVWTFgk38kwns8cw97INkyeMJAT2+uoa/VHhWMAwLvVAJfuOEK+xyBiB3YpVRln9mrGlgPpnNWrGcYYpAqzlDQBUMHj0Fb47X+w8kMoyLXboupwtMeVXL2uPytSY4mNCuftawfpHZcqV0yJLoBsL7YAFDb/d0uoV7zOhFIVNKxTY4Z1qt5iY5oAqMCXtNI29a/7CjB2W+1GcOLN7OpwBRdPWU9SajZ1oiN4+9qBereljqlWVHECkJnrvQSgsADQ4Pb696iqqCAPwquePFY4ARCR64GLgHrAQuBpY8zeKn+yUtVhDOyYB3P+C1t/Lt4e3xpO+jP0vZLd6YZLJy4ouvi/c90gnWqljis6IowwAY+BjJx8r3xGdl4BK3alADBYW6NUVexdDl/eDCMehO7nVOkQFV0L4EZgYolNJwKXisjJxphtVfpkparCGNj8g73w71pYvL1Jdxh6N/Q4H8Ij2JeazRVvLGBPSha1o8J557qBevFXFSIi1ImO4Gh2vtcSgBW7Ush1agwM1BYpVRkFebbF89f/gCcfvr8POp0BkTGVPlRFWwBuBXYBFwO7gTHAs8B/sdUAlfIuT4Ft4p/zLOxfXby9xQAY9hfoPAbC7OCtA2nZXP7GAnYcyiQmMozJEwbSv41+yaqKK0wA0nO80wVQ2P/fqUkdGtbx4jxDFVwOboQv/2Tv/gEadYHxr1Xp4g8VTwA6AP8yxhTecr0pIvWAf4tIrDEmo0qfrtTxFOTB6k/thf/Q5uLt7UfAsHug7bDf1eg/nJHLlW8sZNvBDKIiwnjjah3tryqvsCiPt1oAFjoFgLT/X1WIp8DObJr5GORnA2JLlZ/2D4isWhEgqHgCUAco3d8/HdsC0AVYVuUIlCpLfg4sfw9+ex5SdhZv7zrOXvhb9P/DW1Iy7cV/0/50IsOFiVf2Z2inRr6LWQWNwgQg3QsJQG6+h6U7jgAwSBcAUseTvAW+urW4yzO+NZz3KrQdWu1DV2cWwAHnsV61o1CqUF4WLH3bTudLcwpcSJjt2x92DzTtUebbjmbncc3kRaxLOkpEmPDS5f0Y0bWJ7+JWQaWOF1sAVu9JJdupL6ADAFW5PAWw8DX4+V/OXT8w4DoY9S+IrlsjH1GZBOAsETkCLDHGHCyxXasJqurLzYAlk+G3FyDDyS3DIqD3pTD0HmhY/opXWbkFXP/2YlbuTiVM4PlL+zC6R4KPAlfBKDbaTgX0RgJQ2P/ftmFtmtarWt+tCnKHtsLUW2HXAvs8rhWc8yJ0GFGjH1OZBOAy4FIAEdkFrMFOuu4uIsuNMUdqNDIVGnLSYNEkmP8SZNp+UcKjoO9VMPQu29x1DLn5Hm55fymLE+2f3zMX9WbcCc29HLQKdsVdADU/CLCo/1+b/1VpHg8smgg/PQL5WXZb/wkw6lGIqfnG9oomAHFAP+env/M4BhDgf8D/RGQvsMr5WWmM+ajGo1XBI/uo/UOf/zJkObljRIz9Yz/5Tqh3/It4gcdw9ycrmLXRNkg9em4Pzu/X0otBq1DhrS6AAo9hSWJh/782/6sSDm2Fr26HnfPs83ot4ZwXoOPpXvvICiUAxpg0YLbzA4CIxAJ9KE4I+gNnAGOxLQOaAKg/Krzwz3sJslPstohaMPB6OOkOqFuxpntjDP+YuprvVtlxAvee0ZmrhrT1Tswq5BTNAsit2QRg3d6jRQMLdQaAAuxd/+JJ8NPDkJdpt/W7Gs54DGLivPrRVR4E6Ez9+835AUBEYoC+zo9Sxcq68EfWhoE32Mp9dSpe09oYw1PTNvDhol0A3DS8PbeN6OiFoFWoquOlWQCFzf8t4mvRsn7tGj22CkCHt9u7/h1z7fN6LeDsF6DTSJ98fI2uBWCMyQbmOz9K2T7+hRNh3ou/v/APutFe+GMrP03vlVlbmfirLUB56cBW/G1s1yqthKVUeWKjvDMIcKEzAFBH/4c4jwcWv+Hc9TtldPpeCaOf8Ppdf0kVLQX8AtDcGHOh8zwe+BdwMpAHLALeL1EoyGfEfvNfB9wCdANynXgeN8b8WonjnAycC4wA2mKnN+4FfgaeMsZsqdnIg1xuhh3c99v/IMt+6VX3wg/w7oIdPD1jIwBn9WrG4+N76cVf1bjiQkA1NwjQ4zEsTrTngvb/h7CDm+DrO4pH+NdtZu/6O5/h81Aq2gJwFvB2iecfA6OALCAKGATcJiLfAVcaY47WZJDH8TzwZ2AH8BpQFztb4RcRudgY83kFj/M50BjbevE+kA8MAa7HrntwhjFmXg3HHnzysmDJWzD3WchwZotG1IJBN8DJd1X5wg/w3aok/u+rNQCc0rkxz13Sh/AwvfirmueNLoBNB9JIycwDYLBWpww9BXn2hmj2v4uXK+9zJYx+HGrFuxJSRROABOxaAIjIAOAU4BLgc2OMR0R6A9cCtwFzReREY0ymNwIuSUSGYi/+m4BBxphUZ/srwAJgooj8VLj9OJ4H3jXG7Cn1GX8HHgdeB3rWYPjBJT8Hlk2xi/QUFvAJj7aD+4beDXWqV5Rn3tZk7v54BcZAv9bxvHZlf6IitASF8o6SpYCNMTXSylS4/G/jutG0baj9/yFl73L46o7idUziW9u7/hqe119ZFU0A8kr891nAG8aYTws3GGNWAneJyNfADOAvwKM1FmX5bnYeHy95kTfGrBCRD4EJwIXAm8c7kDHmqXJe+jfwD6CHiDQyxiRXL+QgU5APKz+0WW2qHZRHeBT0u8Yu0lOvWbU/Yu3eVG6aspTcAg8dm9Rh8oSBv1uzXamaVpgA5HsMOfkeYiKr//e2qET/v3ZbhYi8LJj1pB0DZTyAwIm32Br+UbFuR1fhBGAPUDjM+nTs3P8/MMbMFJG3sE3wvkgACtOnH8p4bQY2ATiVCiQAx2Cw3QHw+0QotHk8sO5L+OUJOOQMjwiLsANZht0L8a1q5GN2Hc5kwluLSc/Jp1lcDFOuG0R87agaObZS5SnsAgDbDVDdBMAYU6IAkPb/h4TEubav/7AdsEzjrnDOS9BqoLtxlVDRBOA74FZn8N/J2ItreZYBV1UzruNy6hA0B9KNMfvK2KVw6bjO1fyoi7DjChYcqytBRFKOc5w4oCJdEf7NGNg0w65KVbQsr8AJl8Cpf4UG7Wvso5LTc7jqzYUcTMuhXkwE71w3iObxVV/5SgUmN86twlLAYLsBGlVzyd5tyRkkp9t+X+3/D3LZqfDjP2HpW/Z5WKRtDR12D0T419LPFU0AHsXeSd8KbMc2h99sjHmtjH37A9k1E94xFc6VKO/ELxyIGF/VDxCRdsCLQAFwb1WPEzQSf7PTVnYvKt7W7WwY8SA06VajH5WRk891by8m8VAm0RFhTJ4wkM5Na2YBDKWOp3QLQHUV9v/Xrx1Jx8Z1qn085ac2ToNv74E0Z/HcFv3tXX/T7u7GVY7KVAIcJCI9sQlAFLBIRG4BvgDWYi/6o7FT8r6uaAAishtoUYmYHzHGPFyJ/U0l9i0iIk2AadiZAX82xvx2rP2NMfHHOV5KVeLwC/tW29rUW34s3tbhdNuP1aJfjX9cbr6Hm99byipncZ+XLu/HgLbabBqq3Di3YkskADUxFXCR0/w/qF0DwnTmSvBJPwjTH4A1n9nnEbXg9Idg8M0Q5r/jlSpVCMgYs8b5zwxn3vwk4J8UX2QFSALur8RhvwAq8+2+ynksvPMvr2pCvVL7VZhz8Z8JdAHuMsa8WNljBIXD22wf/+pPi7e1HAQj/1kja1GXxRjDA1+sYs5mO9byifG9GNW9qVc+S6nyRIaHERURRm6+p9rFgGz/f+H8f23+DyoeDyx/F378v+JCZ+2G2xH+Ddq5GlpFVKcU8AHgXBHpDAwFGgG7ga+NMemVOM6fq/j5Gc4CRM1FJKGMcQCdnMdNlTmuiDTDFv/pCtxmjHmlKvEFtPQDdlT/0rfB43z5Ne4Gp/8fdBkLXhzB/NxPm/limZ2Jec+ozlw66NirASrlLXWiIzicn1vtLoBdh7NISrW9ojoAMIgc2ADf3gU7ncK3MXG2fn/fq7z6HVmTql0K2BiziUpeZGvQL8AV2EWIppR6bbTzOKuiBxORltg7/47AzcaY12sgxsCRk25X55v3AuQ6OVxcKxjxdzvIz8tNWZ8s3sULP9uxm5cMaMUdp2l9f+We2OhwDmdUvxzwgm22+T+uViTdm9X8kq7Kx/KyYc4zMPd58DgTw3pdbAv6VLPeia/V6FoALngNmwA8KCJflSgE1Ae4DDgEfFbyDc4dfhyQVHJUv4i0xiYUbYHrjTFv+eIX8AsF+bB8CvzyJGQcsNtqNYDh99lCPj4YuTp700H+9qWdVTC8c2MeG99T50orV9WJjgSyqt0CUJgAaP9/ENg2C769u3hqX/12MO5Z6HCaq2FVVUAnAMaYuc46BX8GVorI5xSXAo4E/lTG1L0ngWuwlQvfLrF9NvbivxRoIyIPl/GRzxtjUmrwV3CXMbDxeztl5ZAzazIiBk68FYbe5bNFKdbuTeXW95ZS4DF0b1aPV67oR2S4VvlT7oqvFQnAkczcKh/DGFOUAJyo0/8CV0YyzHgQVjmr3IdFwMl32pukyMCdmhzQCYDjLmA1doriLdhiPQuAxyqzGBD24g92GmP/cvZ5G0ipQoz+Z89SmPEP2Fm4vIFAnytsc39cZSZlVDOMlCyufWsxGbkFNI+L4a1rB/5uCpZSbmlQxxacOpxR9fpfuw5nsdfp/z+xvfb/BxxjYMX78MM/IOuI3dZqMJz9vxqf+uyGgP+mNcYY4A3npyL7T8BWCCy9PTTa5lJ2ws//+v3I/o6jYNQj0LSHT0NJzcrj2rcWcSAth7oxEbx93SCa1ovxaQxKladB7cIEIKfKxyjZ/98tQfv/A8rBTba5f8dc+zwmDkY+YsuchwVHC2XAJwCqgrJTYe5zMP8VKHC+0Jr2gtGPQftTfR5OXoGHW99fyqb96USGCxOv7K+FfpRfaRBbmABUvQtA+/8DUF6W/a6c+1zxqn09L4TRT0Dd4JqSrAlAsCvIh2Xv2Pn8mc46RnUSbJGK3pe5UqTCGMM/vlzDb1vsl+O/LziBkzpWfZlgpbyhYZ3qJQDa/x+ANs2AaffDkUT7PL6NHeTXcaSrYXmLJgDBbOsvMOPvcGCdfR5Z2w5cOekOV1eiem32Nj5eYlcOvPP0Tpzfr6VrsShVnvq1q5cAaP9/AEnZCdMegI3f2edhkfZ7cvh9EBW8SzdrAhCMDm21g1Y2fu9sEOhzOZz2UI0sz1sd369O4t/TNwBwXp/m3DWy03HeoZQ7GjpdAClZeRR4DOGVbMLX/v8AkJ8L81+E2U9Dfpbd1u4UOPMZaFzddeT8nyYAwSQ7FX59Gha8VlygovUQGPMkNO/rbmzAil0p3P3xCgAGtq3Pvy88Qef6K79V30kAjIGUzFwaVnJFQO3/93PbZsF39xZPga6TAGOegB7nB0wlv+rSBCAYeDx2qsrPj0DGQbstrrUd2d9jvF/8Me8+kskN7ywhJ99Dm4a1mXjVAKIj/HeRDKUKWwDAdgNUJgHQ/n8/djTJdo2u/cI+l3C7aM+pD0BMaLXUaAIQ6HYvhe/vhb3L7PPIWBh2Nwy53W8KVBzNzuP6t5eQnJ5DXK1IJk8YWDTCWil/Vb9UAlAZ2v/vhwryYOFEmPVkcanz1kNsc39CT3djc4kmAIEq/YBdonfFe8Xbel1s7/rrNXcvrlLyCzzc/sFyNu5PIyJMeO3K/nTQ9dBVAIgMD6NuTARp2fmVTgC0/9/P7JgP3/0FDqy1z2s3gjMetTOh/KCF1C2aAASagjxYNMlmsTlH7baEXjD2aWgzxN3YyvDYd+v5dZPtlnjy/F4M6aDNoSpwNIyNIi07n0NVTAC0/99l6QftUr0rP3A2CAy4zk6DrlXf1dD8gSYAgaQgHyadBvtW2ee16sNp/4D+17oyn/943luwg7fnJQJw8ykduGhAK3cDUqqSGsRGkXgokyOVSACMMczZYmtuDNH+f3cU3Sg9BTnOcjDN+8FZ/4UW/dyNzY9oAhBIwiNs1b79a+xF/7R/QG3/7F+cuzmZf35tm9vO6N6U+0d3cTkipSqvcKxKZVoANuxL42CarbY5vHNjr8SljmHbLJj2VzhopxsTEw+n/x/0n+CXN0pu0gQg0JxyP/S6EJr1djuScm07mM6t79vV/bo1q8dzl/TRZlAVkAoTgMqsCFjY5dUivhYdGrtXcCvkHNkBPzwI679xNggMuNbWP/HTGyW3aQIQaKLr+vXFPzUzj+vfWcLR7Hwa1YnmjWsGEKur+6kA1ciZ+rf/aHaF3/PrZpsADOvUSOtc+EJeFvz2P1u7P9/5d2o9BMb+26+/K/2BfjOrGpNX4OHWD5ayPTmDqIgwXr+6Py3i/WMqolJV0bK+LQO763BWhfbPzM1n8Xa7bKw2/3uZMbD+a7useepOu61uMxj1qG0l1eTruDQBUDXCGMPDX68tWuDn6QtPoF9rHWWrAlurBjaBTUrNIq/AQ2T4sZeBXbjtMLkFHsIETu6gC1x5zYH1tp9/+2z7PDwKhtwGw+6FaJ1mXFGaAKga8e6CHby/0Gbhd5zWkXP7tHA5IqWqr5XTAuAxkJSSTeuGx14YZrbT/9+nVTxxtSO9Hl/IyUqxI/sXvQ6mwG7rPMYu1duwg6uhBSJNAFS1/bYlmUe+sSsOju2ZwN0jg38RDRUamsfXQsS2Nu86knncBGCO0/+vzf81zOOxRc9+eqR4WfMGHWDMU9D5DHdjC2CaAKhqSUzO4Nb3l1HgMXRvVo//XtxbR/yroBEVEUazejHsTc1m1+HMY+67JyWLrQczABjWSROAGrNrMUy7D/Yut8+j6thlek+8BSIqt0CT+j1NAFSVpWXnccOUJaRm5dGoThSTrhlA7Sj9k1LBpWWD2jYBOHLsBODn9fsBW/63d8s4X4QW3FJ2wU8Pw5rPiredcAmMfMT1Zc2DhX5bqyop8Bju/GgFWw6kExlua/zriH8VjFrVr82i7YePOxPgh7U2ATi9WxMijjNYUB1DTrqd1jfvheJpfc16w9j/QOsT3Y0tyGgCoKrkPzM2MHPDAQAeP68XA9pqoQ0VnApnAhyrBSA1M6+o/v8Z3RN8ElfQ8Xhg1Ufw878gLcluq5Ngq/j1vgzCNKmqaZoAqEr7cvluJs7eBsB1J7fj4oFa418Fr1ZFtQDKTwBmbtxPvscQExnGKToAsPJ2zIcZfyvu54+IgZPugJPv0ml9XqQJgKqU5TuP8NfPVwN2pPPfz+zqckRKeVfHJvYClJyey/6j2TStF/OHfb5Zae9Yh3dqTK0orTdfYUcS4cd/wrqpxdt6XgAjH4b41i4FFTo0AVAVtv9oNn96dym5+R7aN4rlxcv6al+nCnrdmtUjOiKMnHwPy3ceYUzP3w9AO5SeUzT/X+tfVFBOGsx5Fua/DAV24SRa9IfRT0Lrwe7GFkL021tVSHZeATdNWcKBtBzqxkQw6ZoBxNXSQicq+EVFhNGrhR3Vv3xnyh9e/251EgUeQ93oCE7v1sTH0QUYTwEsmwIv9IO5z9qLf93mMP51uP4nvfj7mLYAqOMyxvC3L1azcncqIvDCZX3p0Fj75VTo6Ns6niU7jvwhATDG8MmSXQCM7ZVATKQ2/5dr+xzbz7/PdiESUQuG3mX7+qN01UQ3aAKgjmvSnG18uXwPAA+M6cqILnqXo0KLXddiO6v2pPxuTYBF2w+zZs9RAC4ZqH3WZTq8DX54CDZ8W7zthEvt6P447TJxkyYA6phmbTzAU9M2ADC+bwtuGt7e5YiU8r2+zsJW2XkeFice5iRnoZ83524HbO3//m108avfyU6FX5+Bha9BQa7d1nKQLd/bsr+7sSlAEwB1DFsPpnPHh8vxGOjdMo4nz++l65urkJQQF0OfVvGs2JXC278lclKHRizafpgf1tniP9cPbedyhH6kIB+WT4GZjxfX7Y9rBaMegR7n6zK9fkQTAFWm1Kw8bpyyhLTsfJrUjWbiVQO0f1OFtBuGteP2D5bz4/r9zNl8kH9+vRawyfGZvbQ0LQBbf4EZD8IB+/+GyFgYdjcMuR0itVKov9EEQP2BLfO7nG0HM4gKD+O1q/qTEPfHuc9KhZIxPRJoEV+LPSlZXPXmIgAiwoSnLjiB8FBfACt5C/zwD9g0zdkg0OcKOP0hqKuVEf2VTgNUf/Cf6RuYtdHOa37i/F7OACilQltEeBgvXNanaM2LuFqRPH9pH7o1q+dyZC7KOgLT/w6vDC6++Lc+CW76Bc57WS/+fk6MMW7HEBJEJAXAGBNfgd1d+0f5cvlu7v54JWD7NR8a192tUFRoqfIttK/PrazcAuZsPkj/NvVpWCdEl6MtyIelb8EvT0DWYbstvg2c8Sh0O0f7+f1Luf8YmgD4SCAkACt3pXDRxPnk5nsY1qkRb00YqJX+lK8ETAIQ8jb/BD88CAft7CCi6sLwv8DgWyBSuwr9ULnnlo4BUAAcOJrNTe8uITffQ7tGsbx0WT+9+Culih3caAf4bfnR2SDQ72o47R9QR2uDBCJNAJQt8/vuUvYfzaFOdASTru5PXG0t86uUAjIPw6ynYPEbYArstrbDYMyTkNDL3dhUtWgCEOKMMTz45RpW7Epxyvz2oWOTum6HpZRyW0GevejPegqyU+y2Bu1h1KPQ9Szt5w8CmgCEuDfnbufzZbsBuH90V07r2tTliJRSrjIGNs2w0/oObbbbouPglPth0E0QEeVufKrGaAIQwn7ddJAnvl8PwLl9mnPzKVrmV6mQtn8dzPg7bPvFPpcw6H8tjPg7xDZyNzZV4zQBCFFbDqRz2wfL8Bg4oWUc/77gBC3zq1Soyki2U/qWvgXGY7e1HwGjn4CmOhU4WGkCEIJSMnO54Z3FJcr89tcyv0qFovxcWDQRZj8NOal2W8OO9sLf6Qzt5w9ymgCEmLwCD7d9sIzEQ5lER4Tx+tUDaBanNbqVCinGwIbv4MeH7HK9ADFxcOrfYOANEK6zgEKBJgAh5tFv1/HblkMA/OfCE+jTKt7dgJRSvrVvNUz/GyTOsc8l3F70T30AajdwNzblU5oAhJB3F+xgyvwdANw+oiPn9mnhckRKKZ/JSIaf/wXLplBUELHjKBj9ODTu4mpoyh2aAISIeVuSedhZvnR0j6bcM6qzyxEppXyiIB+WvAm/PA7ZTj9/oy5OP/9Id2NTrtIEIARsT87glveXUeAxdGtWj2cv7kNYqC9fqlQo2P4rTPsrHFhnn8fEwYgHYcB12s+vNAEIdqmZeVz/zmJSs/JoVCeKSVf3JzZa/9mVCmopu2whn3VTnQ0C/a+B0/4PYhu6GZnyI3olCGK5+R5ufm8p2w5mEBUexmtX9qdl/dpuh6WU8pa8bJj3Asx5FvKz7LZWg2Hsf6B5H1dDU/5HE4AgZYzhH1NXM39b8Yj/AW11hK9SQalwWt+Mv0OKHehLnQQY9S844WKdz6/KpAlAkHp19lY+WWJr/N81shPn9dUR/0oFpYObYPpfYetM+zwsEobcCsPvg2hd2EuVTxOAIPTdqiT+M30jAOP7tuDO0zu5HJFSqsblZsKv/4F5L4In327rOBLGPAWN9JxXx6cJQJBZvvMI93yyAoBBbRvw1AW9tMa/UsFm43T4/j5I3Wmf129rL/ydx2hzv6owTQCCyK7Dmdw4ZQk5+R7aNqzNxKv6Ex2hNf6VChopu2D6A7DhW/s8PAqG/QVOvgsiY1wNTQUeTQCCRGpWHte9vZjk9FziakUyecJA6sfqut1KBYWCPFjwKsx6CvIy7Lb2p8KZ/4VGHV0NTQUuTQCCQHZeATdOWcLmA+lEhgsTr+pP+8Z13A5LKVUTdi6Eb++GA7aSJ3Wa2ip+PS/Q5n5VLZoABLgCj+Guj1awaPthAJ6+sDcnttdCH0oFvKwUu1rfsinOBoFBN8Jp/7AV/ZSqpjC3A6gusa4XkSUikiEiR0RkhogMr+Zxo0VkjYgYEUmsoXBrlDGGf369hulr9wHw4JnddLqfUsFg/bfw8uDii3+zPnDjTDjzab34qxoTDC0AzwN/BnYArwF1gUuBX0TkYmPM51U87mNAmxqJ0EtenLmF9xbYUcA3DmvHjcPbuxyRUqpa0g/Y0f2FJXwjY+H0/7N3/mE6oFfVLDHGuB1DlYnIUGAOsAkYZIxJdbb3ARYA6UCHwu2VPO5s4A7gZWCHMaZtNWNNATDGxFdg9+P+o3y4aCd/+2I1YOf6//ei3rrAjwpkVf7jrelzyxXGwKqP7Qj/rCN2W/sRcPb/oL5f34co/1fuuRXoXQA3O4+Pl7zIG2NWAB8CDYELK3NAEYkF3sEmAK/WTJg164e1+3jwS3vxH965Mf+58AS9+CsVqFJ2wfsXwZd/shf/mHg471W46ku9+CuvCvQEYITz+EMZr81wHk+t5DH/CzQBrjd+2DyyOPEwd3y4HI+B3i3jePWKfkSGB/o/o1IhyBhYMhleORG2/Gi3dTsHblsEfS7XEf7K6wJ2DIBzp94cSDfG7Ctjl83OY+dKHHM08CfgVmPM9krGk3KcXeKASnVFlJaTX8AdHywnJ99Du0axTJ4wUJf2VUHPF+eWzx1Ngq9vhy0/2eexTeCsZ6D7ue7GpUJKIN86Fg6FLe/EP+o8xlfkYCJSH3gTmIkdTOh3oiPCefXKfnRpWpcp1w2iYZ1ot0NSSlXWms+du37n4t/rYrhtoV78lc+5fvsoIruBysxde8QY83Al9q9oM/5L2KSiSk3/xxuAVIG7mArp27o+0+4cpn3+KmT46tzyuszD8P29NgEAqNUAxj0LPca7G5cKWa4nAMAXQGUWql/lPBbe+Zc3KbZeqf3KJSLnAJdjm/4TKxGLK/Tir1SA2fwTfHUbpDu9lZ3HwNkvQN2m7salQprrCYAx5s9VfF+GiOwFmotIQhnjAArXw9xUgcP1cx5fEZFXyni9jYgY53P16quUqpj8HPjpEVjwsn0eVQfGPAl9r9JBfsp1ricA1fQLcAVwBjCl1GujncdZFTjOMmz/f1mux9YT+LgK8SmlQlXyFvj8OkhaaZ+3OhHOn2iX7lXKD4RcISARaYbtNkiqSIEg587f7woBKRVkgqsQ0IoP4bu/2JX7JAyG3wfD74fwQL/nUgGo3HMroP8ajTFzReQFbCnglSLyOcWlgCOBP5VxkX8SuAa4Fnjbh+EqpYJdTpq98K9yGgzrNocLJkHboe7GpVQZAjoBcNwFrAZuBW4B8rB3/48ZY351MS6lVCjZsww+uw6OOCVEupwF574EtSszxlkp3wnoLoBA4pfNlEr5j8DtAvB4YP5L8PMj4MmH8GgY/TgMvEEH+il/EJxdAEop5ar0A/DlzbD1Z/u8UWe4cDIk9HI3LqUqQBMApZSqis0/wtRbIOOgfd7vahjzFETFuhuXUhWkCYBSShXyeCDsOBXS87Lhx/+DRRPt8+h6cPbz0PMCr4enVE0K5LUAlFKq5uxaDK8Pt1X7yrNvNUwaUXzxbz0Ebp6rF38VkHQQoI+4PlBJKf/m/iDAd86B7bNtoZ5bF0BkreLXctJh9lMw/xUwBSDhcOrfYNg9EBZe1dCV8oVyzy1NAHxEEwCljsn9BODgJnj1JPDkweCb4YzHIecorP4Mfnseju6x+zXsBOe9Cq0GVjVkpXxJEwC3aQKg1DG5nwAA/PwozHnG/ndEDBTkgvEUPx92L5z8Z4jQpbhVwNBpgEopdVzD74OMA7D8PcjPttsiasEJF9vmfq3jr4KItgD4iLYAKHVM/tECUOjwNjiwwU7pazXo9+MBlAos2gXgNk0AlDom/0oAlAoe5Z5bOg1QKaWUCkGaACillFIhSBMApZRSKgRpAqCUUkqFIE0AlFJKqRCkCYBSSikVgjQBUEoppUKQJgBKKaVUCNIEQCmllApBuhaA76RWYt8qV0VTKgTpuaVUFWgpYKWUUioEaReAUkopFYI0AVBKKaVCkCYASimlVAjSBEAppZQKQZoAKKWUUiFIEwCllFIqBGkCoJRSSoUgLQTkZ0RkBxDndhxK+ViqMaaNNz9Azy0Voso9t7QFQPlaHKH9Jay/v3/9/v4Wj/IN/XdHKwEqHxORFABjTLy7kbhDf3//+v39LR7lG/rvbmkLgFJKKRWCNAFQSimlQpAmAEoppVQI0gRAKaWUCkGaACillFIhSBMApZRSKgRpAqCUUkqFIK0DoJRSSoUgbQFQSimlQpAmAEoppVQI0gRAKaWUCkGaACillFIhSBMA5XMicqWIGOdngtvx+IqIjBWRb0XkoIjkiMguEflaRE50OzZvEmu8iMwUkb0ikiUiW0TkXRHp5VJMvUTkCxFJduJZKyL3iYgukR7ARKShiNwgIl86f2NZIpIqInNF5HoR+cM1z/n7vF5ElohIhogcEZEZIjLcjd/Bl3QWgPIpEWkBrAEigDrAtcaYt10NygdE5DngLmAP8D2QDDQBTgReNca87F503iUizwJ3AweBqcBhoDtwFlAAnGmM+cmH8ZwE/ASEA58Ae51YejjxnW/0izEgicjNwKvAPmAmsBNoCpyPXf73C+DCkv++IvI/4M/ADuBzoC5wKRALXGyM+dyXv4MvaQKgfEpEZgCdsCfavYRAAiAitwCvYH/nK40x2aVejzTG5LkSnJeJSAL2ApsEnGCMOVTitcuB94FZxpgRPoonHFgLdMEmHtOc7VHYpGAY9t/ofV/Eo2qWiJyGvbH4zhhTUGJ7ArAIaAVcZIz5zNk+FJgDbAIGGWNSne19gAVAOtChcHuw0S4A5TPOhXAUcD2Q4XI4PiEiMcC/sHf8E0pf/AGC9eLvaAMIsKDkxd/xrfPYyIfxjMBe/H8pvPgDGGNygYecpzf7MB5Vg4wxM40xX5e8+Dvb9wGvOU9PLfFS4b/14yUv8saYFcCHQEPgQq8F7DJNAJRPiEgH4GngFWPML27H40OjsBe4z4A8py/8ARG53bnLCHabgVzgRBFpWOq1s51HX/49FLY0/FDGa3OxiekQEYn2XUjKR3Kdx5IJ97H+HmY4j6d6KyC36YAX5XXOwJt3gAPAX10Ox9cGOI9pwGps90cREfkSuNoYk+7rwHzBGHNYRP4KPAusE5GpwBGgG3AmtlvkQR+G1Nl53Fz6BWNMgYhsB3oC7YH1PoxLeZEzuPMa5+l0Z1ss0BxId1oISiv8G+lcxmtBQVsAlC/cC5wEXGeMCYmm/xKaOI/3YO8uT8IOMhqI7ZMcjx20FLSMMc8DFwHRwE3YJPAcbF/8W8aYNB+GE+c8ltene9R5jPd+KMqHnsImdtONMYV39iH/t6AJgPIqEemJ7QN/2Rgzy+Vw3BDuPOYD5xpj5htj0o0xS7AXwXTgChFp6VqEXua0AHwCTALaYgdpnQTkAN+KyJ/di65cOjo6SDh/X38BNgJXVeEQQfu3oAmA8rYp2FHgD7gdiEuOOI/LjTE7S75gjNkPLMQOkuvv68B8QUROxd59TTXG3GeM2WGMyTDGzMcmQFnAEyJSx0chFd7txZXzer1S+6kAJiK3Af/DduecaoxJLvFyyP8taAKgvK0v0A5IL1H8xwD/dF5/y9n2sGsRetdG5zGlnNcLE4Ra3g/FFeOcx1mlX3ASoPXY+dZdfRTPJuexU+kXnCmC7bC1Cbb5KB7lJSJyF/AStu7IqaX7+Z3uyL1AHWeaYGmFfyObyngtKOggQOVtb5azvR82OfgVO9hmmc8i8q2ZzmM3EZEyCsz0cB4TfReSTxWOpi9vql9j5zHHB7GAnXHwd+AMbMtESUOxychcY4yv4lFe4HQ7PQWsAEaVuvMv6RfgCuzfw5RSr412Hmd5IUT/YIzRH/3x+Q/wMLZvbYLbsfjgd/3W+V1vL7X9Wmf7FiDc7Ti99Ltf4vyOSUCzUq/d4Ly2z1e/P3ZMxkbnc8eW2B6FTUYNcIXb/9/0p1r/xg85/45LgAbH2Xeos+9GIK7E9j5ANrZ+R5w34vSHH20BUMr7bgXmAS+KyNnAKmyT9zggE5sEFRzj/YHsM2wryGnAemfa40HsF+wowINNjHzy+xs71e9a4Gdgqoh8jE1OSpYC/sAXsaiaJyLXYAcdF2Ar/P1ZRErvtsIYMxXAGDNXRF7AlgJeKSIlSwFHAn8yQVoFELQLQCmvM8bsFJEB2HEP47DFR44AHwGPGmPWuRmfNzkX3LHAHdgv1QuBGOyd1RfAM8YOCPRlTPNEZDDwCPbCH4vt878feM44t4AqILVzHsOxa2+U5R1solfoLmyNjluBW7CFghYAjxljfvVGkP5C1wJQSimlQpDOAlBKKaVCkCYASimlVAjSBEAppZQKQZoAKKWUUiFIEwCllFIqBGkCoJRSSoUgTQCUUkqpEKQJgFJKKRWCNAFQyo+JyG0iskhEskVkltvxKBUs9NzSUsBK+bsk7KpmA4EhLseiVDAJ+XNLWwCUKoeITBARIyKnuhWDMeYLY8wXwH63YlCqpum55R80AVBBQ0ReFpE9UsbyX0qpqtNzKzhpF4AKCs4X07nAV/6+mpuIRGOXGi1PVhAvD6wCjJ5bwUtbAFSwGAi04PfLfPqrN4G0Y/wMcy80pf5Az60gpQmA8ikR6S0iX4lIqogcFZGpItJMRNJF5MNqHHo8kAr8UoEY2orI587npzrxtBORxIqMBhaRB53+yxdFJKzE9god1xhzpTFGjvFz3BiUKk3PLT23Kku7AJTPiMjpwLfADuAxIAuYAEwDYoEV1Tj8eOA7Y0zecWJoCMwBmgKvAeuxdwW/ODEc671hwEvALcDfjDFP1cRxj/OZEdjzNAIIE5EYwGOMya3qMVXw0XOr8vTcAowx+qM/Xv8BGgPJwDygVontccBhwACjq3jsbs77L6zAvv9x9r2inO2zSmyb4Gw7FagFfAnkAldX57iV/N0edt5f8qdKx9Kf4PzRc0vPrar+aBeA8pW/Ag2BPxtjsgo3GmNSgWXO0xVVPPZ5QA4wvQL7no2d/1u6SfSZY7ynAfAjMBI42xgzpYaOe1zGmIfNH5sxT63OMVXQ0XOrCvTc0jEAyncuBX41xiwp5/V9xpj9AE6/3nmVOPZ44EdjTHoF9m0HbDHGeEpuNMYcAFLKec/bwEnAWGPMjBo8rlI1Qc8tVSWaACivE5EE7CjiP3xBOX1/vajiHYqItAQG4N0Ryh8DHuD/RKSWFz9HqUrRc0tVhyYAyhcKB+qUNYf4XKAJzpeUiHwKtAY+dEYvv32cY5/nHPfrCsaSCHQsOcLY+dwmQHw573kfuBIYAXwrIrVr6LhKVZeeW6rKNAFQvrALKABOKblRRNoALzpPVwAYYy4CdgKXGWPqGGMmHOfY44G5xpiDFYzlG6AZcFmp7fce603GmI+c9wwDpolInZo4rlLVpOeWqjKdBqi8zhiTKyJTgGtF5CvgO6AVcCO2DncLqtBMKSL1geHA/ZV427+By4G3RGQQsAEYCpyMHUldbqUzY8xnIpIHfALMEJGxxpij1T2uUlWl55aeW9WhLQDKV/4MvA4MBv7rPI4H9gKZwOYqHPNsbBI7taJvMMYkY788vgWuw3651ME2QQp2/vSx3v8VcD7QH/hBROJr4rhKVYOeW6pKxJkPqZQrRGQXsNsYM6TEtm3APcaYqcd575dAO2NMnxqIoyH2bmKiMebm6h7P28dV6nj03FLHoy0AyjVOht+SPzZR7gc6VeAQ84G/V+Fzyxpt/Ffn8cfKHs/bx1WqsvTcUhWhLQDKNSIyDPgVuNkYM7HE9nHYAUz1gc+NMdfX8OfOwpZMXQKEA6cD47CV1IabKq4W5q3jKlVZem6pitBBgMpNvZzHFSU3GmO+xfb3ecs3wNXYaU61gN3YvtNHqvlF4q3jKlVZem6p49IWAKWUUioE6RgApZRSKgRpAqCUUkqFIE0AlFJKqRCkCYBSSikVgjQBUEoppUKQJgBKKaVUCNIEQCmllApBmgAopZRSIUgTAKWUUioE/T8lLddjhmhcEQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 504x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "PPa = 101325.\n",
+ "qt = np.arange(2.5e-3,8.e-3,0.2e-3)\n",
+ "TK = 285.\n",
+ "Plcl_X = np.zeros(len(qt))\n",
+ "Plcl_B = np.zeros(len(qt))\n",
+ "Plcl_R = np.zeros(len(qt))\n",
+ "\n",
+ "for i,x in enumerate(qt):\n",
+ " RH = mt.mixing_ratio_to_partial_pressure(x/(1.-x),PPa)/mt.es_liq(TK)\n",
+ " Plcl_R[i] = lcl(PPa,TK,RH)\n",
+ " Plcl_X[i] = mt.plcl(TK,PPa,x)\n",
+ " Plcl_B[i] = mt.plcl_bolton(TK,PPa,x)\n",
+ "\n",
+ "del1 = (Plcl_B-Plcl_X)/100.\n",
+ "del2 = (Plcl_R-Plcl_X)/100.\n",
+ "\n",
+ "fig, axs = plt.subplots(1,2, figsize = (7,5), constrained_layout = True, sharey=True)\n",
+ "\n",
+ "axs[0].plot(qt*1.e3,del1,label='$\\\\delta_\\mathrm{B}$')\n",
+ "axs[0].plot(qt*1.e3,del2,label='$\\\\delta_\\mathrm{R}$')\n",
+ "axs[0].legend(loc=\"best\")\n",
+ "axs[0].set_ylabel('$\\delta P$ / hPa')\n",
+ "axs[0].set_xlabel('$q_\\mathrm{t}$ / gkg$^{-1}$')\n",
+ "axs[0].set_title(f'T={TK:.0f} K')\n",
+ "\n",
+ "qt = np.arange(0.5e-3,28.e-3,0.2e-3)\n",
+ "TK = 310.\n",
+ "\n",
+ "Plcl_X = np.zeros(len(qt))\n",
+ "Plcl_B = np.zeros(len(qt))\n",
+ "Plcl_R = np.zeros(len(qt))\n",
+ "for i,x in enumerate(qt):\n",
+ " RH = mt.mixing_ratio_to_partial_pressure(x/(1.-x),PPa)/mt.es_liq(TK)\n",
+ " Plcl_R[i] = lcl(PPa,TK,RH)\n",
+ " Plcl_X[i] = mt.plcl(TK,PPa,x)\n",
+ " Plcl_B[i] = mt.plcl_bolton(TK,PPa,x)\n",
+ "\n",
+ "del1 = (Plcl_B-Plcl_X)/100.\n",
+ "del2 = (Plcl_R-Plcl_X)/100.\n",
+ "\n",
+ "axs[1].plot(qt*1.e3,del1)\n",
+ "axs[1].plot(qt*1.e3,del2)\n",
+ "axs[1].set_xlabel('$q_\\mathrm{t}$ / gkg$^{-1}$')\n",
+ "axs[1].set_title(f'T={TK:.0f} K')\n",
+ "\n",
+ "sns.set_context(\"talk\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "#fig.savefig(plot_dir+'Plcl.pdf')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/.ipynb_checkpoints/saturation-water-vapor-checkpoint.ipynb b/examples/.ipynb_checkpoints/saturation-water-vapor-checkpoint.ipynb
new file mode 100644
index 0000000..480defd
--- /dev/null
+++ b/examples/.ipynb_checkpoints/saturation-water-vapor-checkpoint.ipynb
@@ -0,0 +1,1213 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Notes on calculations of saturation and sublimation vapor pressures and the specific heats #\n",
+ "\n",
+ "There are a large number of expressions for the saturation and sublimation vapor pressure in the literature, and many of these, even recent ones, seem to reference previous studies in a haphazard way. So how much do these differ, is there a standard, and by what criteria should one judge them by. Here I try to develop an intuition for the answers.\n",
+ "\n",
+ "The first thing to note is that there is a community that concerns itself with this question. They call themselves the international association for the physical properties of water and steam, and mostly concern themselves with the behavior of water at high temperature. The approach of the IAPWS is to develop an empirical equation of state for water, in the form of a specification of its Helmholtz free energy, or potential, from which all other properties can be derived. The standard reference for the IAPWS equation of state is the publication by Wagner and Pru{\\ss} (Thermodynamic Properties of Ordinary Water) published in 2002 and which describes the IAPWS-95 approved formulation. Minor corrections have since been made to this, which as best I can tell are relevant at high temperatures. The most substantial change has been the TEOS-10 work by Rainer Feistel of IOW, which extends these approaches to composite systems, thereby allowing for representations of sea-water and moist air. By working with an equation of state, all properties of water, from the specific heats to the gas constants to the phase-change enthalpies can be derived consistently. The disadvantage of this approach is that the equation is derived by positing an analytic form that is then fit to a very wide and diverse abundance of existing data. The resultant equation is described in an ideal part, which involves a summation of nine terms and thirteen coefficients, and a residual part, with more than 50 terms and over 200 constants.\n",
+ "\n",
+ "For the case of the saturation vapor pressure over water Wagner and Pru{\\ss} suggest a much simpler equation that is described in terms of only six coefficients. First, below I compare the relative error to the IAPWS standard as has been formlated and distributed in the iapws python package, version (1.4). There has been some discussion on the web of its implementation, but the similarity with the Wagner and Pru{\\ss} formulation gives me confidence. Next we look at the TEOS-10 Sea-Ice-Air formulations of Feistel et al (Ocean Sci., 6, 91–141, 2010) which are distributed as FORTRAN90 code which I downloaded, ran, and tabulated to assess some empirical fits later used as a reference.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 132,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/bin/sh: line 0: fg: no job control\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from scipy import interpolate, optimize\n",
+ "\n",
+ "plot_dir = '/Users/m219063/Research/Projects/Thermodynamics/plots/'\n",
+ "\n",
+ "!%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 133,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gravity = 9.8076\n",
+ "\n",
+ "cpd = 1006.\n",
+ "Rd = 287.05\n",
+ "\n",
+ "Rv = 461.53 # IAPWS97 at 273.15\n",
+ "cpv = 1865.01 # ''\n",
+ "lv0 = 2500.93e3 # IAPWS97 at 273.15\n",
+ "lf0 = 333.42e3 #''\n",
+ "\n",
+ "cl = 4179.57 # IAPWS97 at 305 and P=0.1 MPa (chosen to give a good fit for es over ice)\n",
+ "ci = 1905.43 # IAPWS97 at 247.065 and P=0.1 MPa (chosen to give a good fit for es over ice)\n",
+ "\n",
+ "eps1 = Rd/Rv\n",
+ "eps2 = Rv/Rd -1.\n",
+ "\n",
+ "P0 = 100000. # Standard Pressure\n",
+ "T0 = 273.15 # Standard Temperature\n",
+ "PvC = 22.064e6 # Critical pressure of water vapor\n",
+ "TvC = 647.096 # Critical temperature of water vapor\n",
+ "TvT = 273.16 # Triple point temperature of water\n",
+ "PvT = 611.655\n",
+ "lvT = lv0 + (cpv-cl)*(TvT-T0)\n",
+ "lfT = lf0 + (cpv-ci)*(TvT-T0)\n",
+ "lsT = lvT + lfT\n",
+ "\n",
+ "es_default = 'sonntag'\n",
+ "\n",
+ "def thermo_input(x, xtype='none'):\n",
+ " \n",
+ " import numpy as np\n",
+ "\n",
+ " x = np.asarray(x).flatten()\n",
+ " scalar_input = False\n",
+ " if x.ndim == 0:\n",
+ " x = x[None] # Makes x 1D\n",
+ " scalar_input = True\n",
+ "\n",
+ " if (xtype == 'Kelvin' and x.max() < 100 ): x = x+273.15\n",
+ " if (xtype == 'Celcius'and x.max() > 100 ): x = x-273.15\n",
+ " if (xtype == 'Pascal' and x.max() < 1200): x = x*100.\n",
+ " if (xtype == 'kg/kg' and x.max() > 1.0) : x = x/1000.\n",
+ " if (xtype == 'meter' and x.max() < 10.0): print('Warning: input should be in meters, max value less than 10, not corrected')\n",
+ "\n",
+ " return x, scalar_input\n",
+ "\n",
+ "def eslf(T, formula=es_default):\n",
+ " \"\"\" Returns the saturation vapour pressure [Pa] over liquid given \n",
+ "\tthe temperature. Temperatures can be in Celcius or Kelvin.\n",
+ "\tFormulas supported are\n",
+ "\t - Goff-Gratch (1994 Smithsonian Tables)\n",
+ "\t - Sonntag (1994) \n",
+ "\t - Flatau\n",
+ "\t - Magnus Tetens (MT)\n",
+ " - Romps (2017)\n",
+ " - Murphy-Koop\n",
+ " - Bolton\n",
+ " - Wagner and Pruss (WP, 2002) is the default\n",
+ "\t>>> eslf(273.16)\n",
+ "\t611.657\n",
+ " \"\"\"\n",
+ " import numpy as np\n",
+ "\n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ "\n",
+ " if formula == \"flatau\":\n",
+ " if (np.min(x) > 100): x = x-273.16\n",
+ " np.maximum(x,-80.)\n",
+ " c_es= np.asarray([0.6105851e+03, 0.4440316e+02, 0.1430341e+01, 0.2641412e-01,\n",
+ " 0.2995057e-03,0.2031998e-05,0.6936113e-08,0.2564861e-11,-0.3704404e-13])\n",
+ " es = np.polyval(c_es[::-1],x)\n",
+ " elif formula == \"bolton\":\n",
+ " if (np.min(x) > 100): x = x-273.15\n",
+ " es = 611.2*np.exp((17.67*x)/(243.5+x))\n",
+ " elif formula == \"sonntag\":\n",
+ " xx = -6096.9385/x + 16.635794 - 2.711193e-2*x + 1.673952e-5*x*x + 2.433502 * np.log(x)\n",
+ " es = 100.*np.exp(xx)\n",
+ " elif formula =='goff-gratch':\n",
+ " x1 = 273.16/x\n",
+ " x2 = 373.16/x\n",
+ " xl = np.log10(1013.246 ) - 7.90298*(x2 - 1) + 5.02808*np.log10(x2) - 1.3816e-7*(10**(11.344*(1.-1./x2)) - 1.0) + 8.1328e-3 * (10**(-3.49149*(x2-1)) - 1.0)\n",
+ " es =10**(xl+2) # plus 2 converts from hPa to Pa\n",
+ " elif formula == 'wagner-pruss':\n",
+ " vt = 1.-x/TvC\n",
+ " es = PvC * np.exp(TvC/x * (-7.85951783*vt + 1.84408259*vt**1.5 - 11.7866497*vt**3 + 22.6807411*vt**3.5 - 15.9618719*vt**4 + 1.80122502*vt**7.5))\n",
+ " elif formula == 'hardy98':\n",
+ " y = -2.8365744e+3/(x*x) - 6.028076559e+3/x + 19.54263612 - 2.737830188e-2*x + 1.6261698e-5*x**2 + 7.0229056e-10*x**3 - 1.8680009e-13*x**4 + 2.7150305 * np.log(x)\n",
+ " es = np.exp(y)\n",
+ " elif formula == 'romps':\n",
+ " Rr = 461.\n",
+ " cvl_r = 4119\n",
+ " cvv_r = 1418\n",
+ " cpv_r = cvv_r + Rr\n",
+ " es = 611.65 * (x/TvT) **((cpv_r-cvl_r)/Rr) * np.exp((2.37403e6 - (cvv_r-cvl_r)*TvT)*(1/TvT - 1/x)/Rr)\n",
+ " elif formula == \"murphy-koop\":\n",
+ " es = np.exp(54.842763 - 6763.22/x - 4.210*np.log(x) + 0.000367*x + np.tanh(0.0415*(x - 218.8)) * (53.878 - 1331.22/x - 9.44523 * np.log(x) + 0.014025*x))\n",
+ " elif formula == \"standard-analytic\":\n",
+ " c1 = (cpv-cl)/Rv\n",
+ " c2 = lvT/(Rv*TvT) - c1\n",
+ " es = PvT * np.exp(c2*(1.-TvT/x)) * (x/TvT)**c1\n",
+ " else:\n",
+ " exit(\"formula not supported\")\n",
+ "\n",
+ " es = np.maximum(es,0)\n",
+ " if scalar_input:\n",
+ " return np.squeeze(es)\n",
+ " return es\n",
+ "\n",
+ "def esif(T, formula=es_default):\n",
+ " \"\"\" Returns the saturation vapour pressure [Pa] over ice given \n",
+ "\tthe temperature. Temperatures can be in Celcius or Kelvin.\n",
+ "\tuses the Goff-Gratch (1994 Smithsonian Tables) formula\n",
+ "\t>>> esli(273.15)\n",
+ "\t6.112\n",
+ "m \"\"\"\n",
+ " import numpy as np\n",
+ "\n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ "\n",
+ " if formula == \"sonntag\":\n",
+ " es = 100 * np.exp(24.7219 - 6024.5282/x + 0.010613868*x - 0.000013198825*x**2 - 0.49382577*np.log(x))\n",
+ " elif formula == \"goff-gratch\":\n",
+ " x1 = 273.16/x\n",
+ " xi = np.log10( 6.1071) - 9.09718*(x1 - 1) - 3.56654*np.log10(x1) + 0.876793*(1 - 1./x1)\n",
+ " es = 10**(xi+2)\n",
+ " elif formula == \"wagner-pruss\": #(actually wagner et al, 2011)\n",
+ " a1 = -0.212144006e+2\n",
+ " a2 = 0.273203819e+2\n",
+ " a3 = -0.610598130e+1\n",
+ " b1 = 0.333333333e-2\n",
+ " b2 = 0.120666667e+1\n",
+ " b3 = 0.170333333e+1\n",
+ " theta = T/TvT\n",
+ " es = PvT * np.exp((a1*theta**b1 + a2 * theta**b2 + a3 * theta**b3)/theta)\n",
+ " elif formula == \"murphy-koop\":\n",
+ " es = np.exp(9.550426 - 5723.265/x + 3.53068 * np.log(x) - 0.00728332*x)\n",
+ " elif formula == \"romps\":\n",
+ " Rr = 461.\n",
+ " cvv_r = 1418.\n",
+ " cvs_r = 1861.\n",
+ " cpv_r = cvv_r + Rr\n",
+ " es = 611.65 * (x/TvT) **((cpv_r-cvs_r)/Rr) * np.exp((2.37403e6 + 0.33373e6 - (cvv_r-cvs_r)*TvT)*(1/TvT - 1/x)/Rr)\n",
+ " elif formula == \"standard-analytic\":\n",
+ " c1 = (cpv-ci)/Rv\n",
+ " c2 = lsT/(Rv*TvT) - c1\n",
+ " es = PvT * np.exp(c2*(1.-TvT/x)) * (x/TvT)**c1\n",
+ " else:\n",
+ " exit(\"formula not supported\")\n",
+ "\n",
+ " es = np.maximum(es,0)\n",
+ " if scalar_input:\n",
+ " return np.squeeze(es)\n",
+ " return es\n",
+ " \n",
+ "def esilf(T,formula=es_default):\n",
+ " import numpy as np\n",
+ " return np.minimum(esif(T,formula),eslf(T,formula))\n",
+ "\n",
+ "def es(T,formula=es_default,state='liq'):\n",
+ "\n",
+ " import numpy as np\n",
+ " \n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ "\n",
+ " if (state == 'liq'):\n",
+ " return eslf(x,formula)\n",
+ " if (state == 'ice'):\n",
+ " return esif(x,formula)\n",
+ " if (state == 'mxd'):\n",
+ " return esilf(x,formula)\n",
+ "\n",
+ "def des(T,formula=es_default,state='liq'):\n",
+ "\n",
+ " import numpy as np\n",
+ " \n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ " dx = 0.01; xp = x+dx/2; xm = x-dx/2\n",
+ " return (es(xp,formula,state)-es(xm,formula,state))/dx\n",
+ "\n",
+ "def dlnesdlnT(T,formula=es_default,state='liq'):\n",
+ "\n",
+ " import numpy as np\n",
+ " \n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ " dx = 0.01; xp = x+dx/2; xm = x-dx/2\n",
+ " return ((es(xp,formula,state)-es(xm,formula,state))/es(x,formula,state) * (x/dx))\n",
+ " \n",
+ "def phase_change_enthalpy(Tx,fusion=False):\n",
+ " \"\"\" Returns the enthlapy [J/g] of vaporization (default) of water vapor or \n",
+ " (if fusion=True) the fusion anthalpy. Input temperature can be in degC or Kelvin\n",
+ " >>> phase_change_enthalpy(273.15)\n",
+ " 2500.8e3\n",
+ " \"\"\"\n",
+ " import numpy as np\n",
+ "\n",
+ " TC, scalar_input = thermo_input(Tx, 'Celcius')\n",
+ " TK, scalar_input = thermo_input(Tx, 'Kelvin')\n",
+ "\n",
+ " if (fusion):\n",
+ " el = lfT + (cl-ci)*(TK-TvT)\n",
+ " else:\n",
+ " el = lvT + (cpv-cl)*(TK-TvT)\n",
+ "\n",
+ " if scalar_input:\n",
+ " return np.squeeze(el)\n",
+ " return el\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Some basic properties of water from the IAWPS routines. ##\n",
+ "\n",
+ "These routines only provide information on $c_{p,\\mathrm{liq}}$ to a temperature of 253 K or -20$^\\circ$C, but already demonstrate its divergent behavior (exponential increase) with increased super-cooling. They also demonstrate the near linearity of $c_{p,\\mathrm{ice}}.$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 134,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using IAPWS Version 1.4\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/m219063/opt/anaconda3/lib/python3.7/site-packages/iapws/_iapws.py:124: UserWarning: Metastable ice\n",
+ " warnings.warn(\"Metastable ice\")\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 288x216 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import iapws\n",
+ "\n",
+ "print ('Using IAPWS Version %s\\n'%(iapws.__version__,))\n",
+ "T = np.arange(183.15,313.15)\n",
+ "ci_iapws = np.full(len(T),np.nan)\n",
+ "cl_iapws = np.full(len(T),np.nan)\n",
+ "for i,Tx in enumerate(T):\n",
+ " if (Tx < 283): ci_iapws[i] = iapws._iapws._Ice(Tx, 0.1)['cp']*1000 / ci\n",
+ " if (Tx > 253.15): cl_iapws[i] = iapws._iapws._Liquid(Tx, 0.1)['cp']*1000 / cl\n",
+ "\n",
+ "fig = plt.figure(figsize=(4,3))\n",
+ "\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_xlabel('$T$ / K')\n",
+ "ax1.set_ylabel('$c_\\mathrm{i}$ / %5.2f, $c_\\mathrm{l}$ / %5.2f'%(ci,cl))\n",
+ "ax1.set_xticks([185,247.07,273.15,305.00])\n",
+ "plt.scatter([247.065],[1.])\n",
+ "plt.scatter([305.000],[1.])\n",
+ "plt.plot(T,ci_iapws)\n",
+ "plt.plot(T,cl_iapws)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "fig.savefig(plot_dir+'cp-Tdependance.pdf')\n",
+ "\n",
+ "TK = np.arange(273.15,315.15,0.01)\n",
+ "es_iapws = np.zeros(len(TK))\n",
+ "for i, x in enumerate(TK):\n",
+ " es_iapws[i] = iapws.iapws97._PSat_T(x) *1.e6 #Temperature, [K]; Returns:Pressure, [MPa]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Comparison with Sea-Air-Ice library of Feistel et al (2010) v4.0.1\n",
+ "\n",
+ "Here we compare different thermodynamic constants or empirical formula to the IAPWS-10 and TEOS-10 standards taken from the Sea-Air-Ice library. These are calculated based on fits to potential functions as described above. The libraries are run off-line and the output is tabulated for comparison. The Feistel e tal formulation extends to the IAPWS formulation shown above to allow for representations of liquid to the homogeneous freezing point of ice."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 135,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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iIX4cje/FK2W3/R/A8svgzKvg3H/gxcWd71gjkT1/OZzTwu4ClYo6px7iIuIK/OwramiIK2Wzwq9g6UgqkntwS+nrfHnAzQtXWL2xKaXq7bRCfA+wBngLeMsYsym0tYWehrhS9ist3seUl3exp6oZc35aQatWZ9tdklLR6tQ7e8Hq3OV/gJbASyKyUUT+ISKXiYiOL6SUOkphBUxYnM2h+N7M7f4YrVaeC1tfsLsspRqdep8TD/SadhFwMTAcqyvWJcBLxphtoS7wVOieuFL2ySuFa16DRDfMugxSYw1sfgY+mwqtroC+T4JbBwhXqh7Cd2GbiPTCCvTNxpi5p7WyENEQV8oeO4pg4mvQOgWeHmUFeVDRN7DyZ1BVaI1NnjXUrjKVijZ6dfpxNL4Xr5RNvj9oBXivbHhsJMS6jrGQrxK+vBe+fQTa/Rx6/c3qh10pdSIa4sfR+F68Ujb4Ms8aSvTCtvCXC8F1sqttCj6D1b+E8j3Q+xE4c7x2nq7U8WmIH0fje/FKRdjHu2Dyf+DKLnDfkHqMROb3wnePwlf3Q1oP6P0wNOsf1lqVilKndXW6Ukod03tbrT3wX/aCafUJcACHCzrfAZduhNQu8M4g+Gg8HG7wv2JVqsGoc4iLSLyIPCQi20SkONB2sYjcGr7ylFIN1RvfwQ0L4a7z4db+p3E0PD4H+s2AH38BniJYeDasnATF34W0XqUao/rsiT8J5ACjAF+g7UvgxlAXpZRq2P7vS7jjbfjTCJjcK0QrTTsHhi+GH62AqgJ4swusuBoKPg/RBpRqfOp8TlxE9gOtjDFVIlJgjMkItBcZYxrUjz71nLhS4WEMPPEpPPYJPD4SRnYI48YKPoP1D8LuN6DZADjrv6H1aHDEhHGjSjVIp39hm4h8D4wwxuyqDnERaQe8aYzpHKJCQ0JDXKnQMwb++CH8ez3MGAWDzojQhku2wsYnYdMz4EqA9r+EdpMgqV2EClDKdiEJ8V8BU4AHscb9ngjcB8w0xvwrBEWGjIa4UqHl9cPd78E7W2D2T6Fnth1FlMK2ObB5Jhz8BJoPhnbXwhnjICbFhoKUipjQ/MRMRMYBv8DqT30XMMsY8+JplxdiGuJKhU6FF25ZAmv3wQtXwFmZdleE1fvb1tmw9f+g8iDkXAytx0CrS7XzGNUY6e/Ej6PxvXilQqikCn75H9hzGF4YbXWn2qD4fZD/Aex81Zoq9kPWCGh1mRXsyeE8aa9UxITkcPqfgDeMMatrtPUDLjXG3HvaJYaQhrhSp+9QOVz7BlT54PnLoUWi3RWdhPFbh9l3zoc9i6BoAyS1t8I852LIGqaH3VW0CkmI7wdaG2Mqa7TFAduNMVmnXWIIaYgrdXr2lVj9oKfGwrM/tW6jTulO2Pc27H0L9r4D3mJI6wnNB0GLwdY59fgG9dGl1PGEJMTzgDbGmPIabYnANmNM89MuMYQ0xJU6ddsKYcKr0D4D/nUJxDeGX3T5fVC4DvZ/CPkfQf6HUJEHSR0gsy9k9LGm9F46TKpqiEIS4nOAEuAWY0yliMQC/wukG2PGh6TMENEQVw3V4cOHSUpKQhroYB8b8q1uVPu3gr9fBG6n3RWFiTFW964HVsDBNdZv0gvXgq8CkjtagZ52DqR2hZQu1s/ZHI31H0NFgZCEeDPg38D5QB6QBXwITDDGHAxBkSGjIa4aqpkzZ+L1eunevTvt27enWbNmOBwNYwiDT/fAL96AyzrBA8PA2TDKihy/F4q/sUL90OdQ9LU1VewHRyyknG2Fempn61x7UntIbg/uDB2BTYVb6K5OF5FcIBfYaYzZe5qFhYWGuGqoysrKWL16Nd999x15eXm4XC6ysrJo3rw56enppKenk5GRQXp6OvHx8RHbY1+6DW580+pC9c4Bmkm1VByA4g3WhXJFG6ygL9kCpdvB+KyL5apDPakdJJ4BCa0gPte6jWsB0tS+ETVNxhiqfFWUecoQEdLi0kK16pCGeAKQVLPNGLO/Ds+LBZYCXYApxph5R8zvgNWJTAwwu7oDGRG5AbgW8ACTjTEnHeJIQ1xFg4qKCvbt28fevXs5ePAghw4doqCggKKiIowxOJ1OkpOTSUpKOuo2Pj6e+Ph4EhISgvedzlM73LvgO7j9bfjN+XB97xC/yMbM74HSHVCyOTBtgcOboWwXlO+C8n2AAXFBfEsr0BNyIS4bYptb4R7X/If7sc3BnaaBH0Y+v48yTxmlnlLrtsq6rVObt6zW/Op5Rz7HZ6yhRSb1mMTsy2eHqvSQHE4fATwNtD1ihcYYc9JPDxFxYB2CvwFYf4wQnwf8GVgHrAB+HJi1GBgI9ALuMsaMq8O2NMRV1PL5fBQXF3P48OFaU0lJCYcPH6a0tJTy8nLKysrw+XzB57ndbuLj44mLiyM2Nha32x2cjvf4g50uZq93cf25Li7u6MLlchETE4PL5QpOTqezwZ7Db9D8HivIy3ZB+W7rtmyXdUFdZT5U5EPlfuvWH/jRjzitMI9tZl1gF5MGMalWuLtr3A+2p4IrCVyJ1uRMsLqmjaIvAj6/j3JvOeWecso8ZcH79WrzBto8P9w/MmBLPaVU+aqO2r5DHCTEJJAYk2jduq3bo9pcCbXmV887XltGfAbp8SHreCgkIb4ZK2Tn1LxCvd6ViEzn2CG+zhjTI3D/r1h77QIMNcbcfeQyJ9mGhniUMsbg9/vx+/34fD58Ph9ut5uYmBiKi4upqKjA5/Ph9/uJiYmhRYsWlJSUsG/fvlrP7dChAzExMXz11VfB5f1+P7m5ueTk5LBp0yby8vKC7QkJCfTt25eCggLWrFkTbDfGMHjwYFJSUli8eDHl5eXBeWeddRY9e/Zk7dq1fPPNNxhjMMYQFxfHmDFjOHToEAsWLKD6b8wYw8UXX0zLli157bXXOHDgQPA5Z511FsOHD2ft2rWsXLky2C4i/OpXv6K0tJQZM2bU+rcaOXIk7du359VXX2XXrl3B52RmZtK5c2d27drF5s2bg+0ALVu2xOPxsG/fPvzG4DfgwNrOiT4LHA5HcH51oKekpOB2uzl06FBwOREhJSWFtLQ0ioqKKC0tRUQQEWJiYmjbti1VVVXs2rUr2C4itG3blsTERDZv3ozH4wm2Z2Zmkpuby4EDBzhw4AAOhwOHw4HL5eLss8/G4/Gwbdu2YLvD4SA3N5fk5GT27t1LVVUVDocDp9NJUlISLVq0oLS0lOLi4lrPycrKqlVP9Ws5lcf1fq4xiK8UqcxHqg5ART5SddAalrWq0Lr1FP5wv6qwdrvxH/0/zBlvhbkz0bqtGfLOOHC4rfP8TvcP9x1u/OLCiwOvOPEgeIxQhVBlDB6/n0q/l0qfjyq/h0qflwqfhwq/hwqvh0q/h3Kvh3JfJRU+D+W+Ksq9lcHbCm8llb4KqryVVHorqfBWUOmrwOu3voge+YEd43AS64ojzhlLnCuWWFcssc444lxu4p2xJLjcxLvcxDvdxDliiHPFEO90E+uIId4ZQ5zTaot1uIhzWrexTmtyO5y4HU5ixYVTQPBbp0b8XuvLl/FYt9XTSR97j56Xexn0+utx/6bq6bh55qrHStKAZ4051jsmJGoWeQjICNwvPM4yVoP1peD+MNXUaPn9fioqKvB6vXi9Xnw+H6mpqbjdbnbt2hWc5/P5SEtLIzc3l927d7N9+/ZgO8Dw4cMpKyvj3XffDYalz+dj4MCBtGrVisWLF7Nv375gILdp04aLL76YL7/8knfffbdWWP/mN7/B5/Pxl7/8pVatl156Kb1792bRokV8990PY0y3a9eOa665hl27drFgwQIcDgcigsPhYMqUKcTExLBixYpgGFZ/+Ofk5JCXl8emTZuCH+Jpada5K6/XW+sDvma4xcXFBdfjcDhITLR6P0lOTiYnJwewPpzdbjdg7Rl36NCh1gd29XM6duxIq1ataoUVWCE7cODAoz70Y2Njueiii4LbAMjJySEmJob+/ftTWloa/HdJSkrijDPOoLCwkK5du9b6t+zUqRMiDh58bQMrdsANveHsZpCdnU16ejrbtm2jsLCw1hel7OxsiouL2b17N16vF2MMPp+P7OxsHA4H27dvr/VFKTExkeTkZOLj4zl8+HCwvfqLQPUXs+ovXUDwNEL10YXqLx4ejyfYXlZWVuuLRkFBAV6vl5KSklpfVL777jucTmfwCxdYX6Cq/79Vrz8aiDhA0oHAHp2AIBhMjU9Dc4LJDxgrpAK3ckS79bgKoSLwhc7gwPpy5xATvBWsySEElxP4YRkgSVwk47TmY3CIH4Hgc6XGekQMxFQ/5qj51msNtPkM4qu5jFjXbYh1K3gQ8SJSUetvRwQ8CF4Ryhw/tDuqb6vbHIJDHIgE/u6dTkRcOBxOxOHE4YxHJBmH04E4XNY8pwtxu3A4XFabI8Zqc8bgcLhwp3cgIRLvkXrsiU8Diowxj57WBo+/J77WGNMzcP8vwDKst+kQY8w9Ry5zkm1ExZ641+vF4/Hg8Xjwer3ExcWRkJBAfn4+RUVFwXlut5uzzz6bQ4cOsW7dulrPGz58OMnJycyfP5/S0tJgwHbv3p1+/fqxbNky1qxZE2xPSkpi6tSp7Nmz56g9u2uvvZY2bdrw9NNPU1hYGDyUes455zBixAjWrVvHF198EWyPjY1l9OjRlJWV8f777+N0OoN7Pd27d6dFixZ89dVXlJSU4HQ6cTqdpKen065dOw4dOkReXl5weafTyRlnnIGIkJeXF2xzOBwkJCTgdruDe2jVH8aq/rx+uOc9eGszzL4cetkxkIlNjDH4jI+SyhKKKos4XHU4eMg1Mz4Tj89Dpc/aS6z0VlLlraLSV0mVr4pKT+DWW4nH56HKV0WVt8q6rZ68VXj8Hus2sIzH5wm2e3yBye/B4/UE27w+Lx6fJ3guVWr8V/3YgYM4ZxyxzljcTndwqn4c67BuYxwx1jyHO/g4xhGD2xG474wJtsU4YnA5XNZ9se67HC5c4gq2O8WJS1w4xVnr3/FE01HL+P3WV4oTLROCeXVZpvro2pG3p9J2suzs0aMHl19+eajeviE5nP4N0AFrzzi/5jxjTJc6V3L8EJ/LD+fEPwIuCcx6ExgE9ADuaajnxNesWcOePXuCe7aZmZlccMEFbN68mbfffjsYvF6vl5tvvpmkpCQeeOCBWm+ECy+8kPPPP5+FCxeyYcMGYmJigoeMr7zySvbt28f7779f67zl0KFDSUlJYdWqVfh8PpxOJy6Xi5YtW5Kbm8v+/ftrBbLb7SYnJye4B1PdXn3+U899Nl6VgYFMvjiFgUy8fi8lVSXWOUdPOWXeMlLcKSCw8eBGSqtKqfBVUOGtICU2hcz4TLYUbmFn4c5gEAKck3UOBWUFfLLnk2DIefweujTvQmJMIit3rqSosigYcM0SmtEmrQ1bC7ey5dAWfH4fPr8Pg6Fbi26Uekr5ev/X+IwPv/HjN34y4zNxO93kleZR6a209lrryClOYl2xtQLS7XQf1XbMx44f2k+67DHWH+eyQjrOFVdrinXF4nLU56CpioSTfUGo3tEJkZCE+NDjzTPGLK/jOuZjXaBWArwTmFKNMS+f4Or0G4FJNPCr01evXs3+/fuD4ZqZmUmvXr04dOgQW7duDbbHxMTQpk0bXC4X+fn5wfCsDuxTvcJY1Y/f+PH5rQ/+6j2ghJgEvH4vhRWFwUDw+X00S2hGrCuWHUU7qPRWBuelxaWRk5zD3sN72XN4Dz7jw+v3IggDWg+gzFPGB9s/wOv34vNb8wzWH/yaPWsoKC8IhlVGfAadmnViW+E2vsn/Bq/fG5wGnTGISm8ly7Yvw2d8wSDrkNGBzIRMvsz7kgNlB4JBluROokNGB/aX7mdzwWb8xo8hgUT3HGKd7Uly38imgqVWuzEYDEnuJOJccRRWFB4VfMHDt3UkCLGuWPx+Px5/4Pw2gsvhok1aG/zGz67iXTjEEZzObnY2mQmZfJv/LZW+SpwOJy6Hi9zkXLq16Mbew3vZUbwjuPfodrq5qP1F+P1+1uxdUysguzXvRsuUlmwu2EyVr4qEmATiY+JpntCcjpkdqfRWUlJVQqI7kcSYRJLcSWQlZhHrisWpHbqohun0z4lXB3XgKvNsY8ye+lZhjBlzgnmbgKtYMZYAACAASURBVKO+KBhj/gn8s77birS4M+OIaxaH3/jx4qUqztrzKHeVsyvRuujIb/wYjyHX5OLCxfL85Xj93uCHaY/sHnRr0Y2VO1fy/cHva4XF2C5j2V28mwXfLcBggvOu7nY1zROb8/RnT1NcWRz8UD635bmMaDuC97e+z8qdK4PbSHQn8uuBvyavJI/HVj8WDBW/8TOl9xQ6Znbk4ZUPs6t4V3A75+Wex8TuE3l3y7vM3zA/EArWh/rTlz5NUUURtyy5Jbhtv/Hzq76/YmDrgfzxgz/yZd6Xwef0yu7FvUPu5d0t7/Lwqodrhekb498gISaBwc8N/iFEjY87B97J+G7jufPtO1m4cWHwOd2zuvPqVa/y3pb3mPT6pOB6/MbP6imraZ/RnlZ/b0VBeUEw4H476Lf8fvjvmbp4Kk98+kTw/1/btLZsmbqFz/Z8Rv+Z/Wv9v13xixUMbD2QC56/gE0FP3yHvOncm3jykid5+rOnmb58ejCo0uLS2H/nfvaX7uea166xDkk6XDgdTlqntGbP4T0cKDtAla/KOj2Ag5S4FNqktaGoooi9JXtrBVxqXCpOcbK/dD9OhxOHOHCKE4Mh3hVPamyq9c0/EHyZ8Zn0ze1LQXkBzeKbYUhje/F0jIll0jlvkZU0hlU7W1rLO124HW7aZbSjbVpbdhbtpLCiMLiHmBKbQpfmXaj0VZJfmh/cY4x3xZOTlEOCO4FKb2UwKGOdGoRKRVKdQzzQY9s/gcuAKiBJRC4DBprA1eNN2V9X/JUlm5YE9zoGnTGI18e/zud7P+fGhTcGLpqw5g2YMoD4mHjuW3ofHp8nOO+OAXfQrUU3lmxawmvfvma1I3TI6MDYLmPZV7KPZ9c+G/xwF4SRHUbSPLE5b21+iwNlB4LzUmJTGNF2BNsKt7Fi54rAhSBCepx1gUylr5INBzZY59sC2y/3Wj86OFB2gP1l+4PzyjxlgPVTEI/fE2x3BH7GIiIku5ODNTnEQZwrDoCsxCzaprUNvvbWKa0BaJ7QnIGtBtYKJZfDhUMcjO82Prh+pzjp1qIbAJd2upTuWd2Dz2mW0AyAri268uRPnqy1ruwk62Tvy2Nfxm/8VrvDSauUVgDcNegupvSeEmyPdVqHvXpk92Dr1K3B9TjEQWaCddx5zS/XAASfU32Ic9rQaUwbOu2oUxFt0tqQf2etM08RVz2QSedm8NxlkBr3SwAm955sa11KqdCoz+H014FvgT9i9daWLiIZwBpjTLsw1lhv0XJhm1LhtK0QJrwG7dPhn5dAQmMYyESppikk58QPAFnGGJ+IFBhjMgLthcaYkPUtFwoa4qqp+yYfrmkKA5ko1TQcN8/q8zudfUCnWmsV6Q5sO7WalFLhsGYPXDkfLmoHj16sAa5UY1afEJ8OLBKRXwMxIvLfwPxAu1KqAVi2zTqEfs058OCIJjgSmVJNTL0GQBGRvsAvgDOA3cBzxphVYartlOnhdNUU/ed7uO0t+PVAuLGP3dUopULo9M6Ji4gL+AdwizGmMoSFhYWGuGpq5nwF05bBH4fD1d3srkYpFWIhubBtN9DWGHP0MDANjIa4aiqMgafWwCOrrfPfP+lod0VKqTAIyYVtDwKPiEg9OmtUSoWLMfDnFfD4p/DspRrgSjVF9dkTLwfcgYdVWHuxgjWeeCQGa6kz3RNXjZ3PD/e8D0s2wayfQu8cuytSSoVRSIYiPTsEhSilTlOlF6a+BZ/thbljoVMzuytSStmlXlenA4jImUDLhnhVejXdE1eNVWkVXL8QdhTDnCvgjFS7K1JKRcDpnxMXkXYi8inwKfB2oG2MiDx/+vUppU6msML6DfiBMpg3TgNcKVW/C9uewfpdeAvAG2h7Gxge8qqUUrXklcC4eSACr4yFrES7K1JKNQT1OSfeC7ggcL/6MHQJoB8nSoXR9sBAJm3T4F+jdCATpdQP6rMnvhnof0TbIOC70JWjlKppQz6MmQvds2DmZRrgSqna6rMnfgfwuojMBeJE5GHgKuDqsFSmVBP3yW6YvAAu6wQPDNN+0JVSR6tv3+mtgAn80Hf6i8aYrWGq7ZTp1ekq2r27BW5eBDf0gdv7W+fClVJN1ql3uyoie4wxLUVktjHm2pCXFgYa4iqazf8G7noXfjsIftHL7mqUUg3AaYX4IaAdsN0YkxLiwsJCQ1xFq2c+h7+sgL/9CEZr90pKKctp9dg2F9gPOESk7BgrbnDdrioVbYyBh1bBzC+sK9AvaGt3RUqpaFDXoUizsK5O73qs+caY7SGu67TonriKJj4/3LsUFn4Pz14GfXPtrkgp1cCc+p64iHxsjOkvIq80tLBWKtpVeuHWt+DTPVYnLp2b212RUiqa1PWceHOgQM+JKxU6JYF+0HcWwwuXw5lpdleklGqgTqvv9A+AnUCCiGw41lTnKkRuEJGVIrJcRDocMW+0iKwWkY9E5MeBtjYiclBElgWmAXXdllINWUE5/OxV63beOA1wpdSpqcueeAzQG1gK/PhYyxhjlp90QyIZwGJgIFYXrncZY8YF5jmBdUBfrG8cy7F6h2sNPGGMGVXH11O9Ld0TVw3WnsMw8TXIiLd6YUuNtbsipVQDd+rnxI0xHmC1iIwwxnx8GkX0A5YaY3zAGhE5q8a8ZsBeY0w5gIgUAR2ASqCviHwIfA3cYYwpPY0alLLVpgK45jU4uxk8+ROI125UlVKnoS4Xtt1qjPlfYIiIDDnWMsaYv9VhW+lAYc1V17ifD7QUkeaB9l6B5b8A2htjSkTkN8BdwLQj6psO3F+H7Stlq3X74No3YFgbeOhCiHHaXZFSKtrV5XfinQK3nU9zW4eAc2o89lffMcb4ReQW4BWgACu89xpjKrH2xgFeAp46cqXGmOnA9JptIqKHyVWD8tEO6yK2q7rCfUPAod2oKqVCoC6H028K3P78NLe1GpgWOP/dA9h4xHbeA94TkUxgtjFmu4ikGGOKA4sMAzadZg1KRdyijTD1LbjlPPivvtoPulIqdOpyOP2Yh9BrMsZ8UIdlCkRkNvAh4AEmi8hIINUY83JgVLTeQAVwa+BpQ0Tk91jjlpcAp/tFQqmIenG91ZHL74fCxO52V6OUamzqcnX6NzUetgeqsLphbQG4gU3GmC5hq/AU6NXpym7GwFNr4JHV8MhFMOqskz9HKaWO47SuTu8MICJ/wdpL/pMxpkpE3MA9QFyoqlSqMfAbePBD+Pd6ePZSGHym3RUppRqrOo8nXt1zmzHGW6PNBeQbY9LDVN8p0T1xZRev3xpG9N2tMOun0Cvb7oqUUo3AafXYVm038LMj2q4G9pxKRUo1NhVeuPFN+GgnzB2rAa6UCr/67In3B+YBpVjdsLYGEoGxp9kJTMjpnriKtOJKmPIfyC+F/7sCWkXFKANKqShx3Dyrc4hDsAvWgUA2sA9YZYypOu3yQkxDXEVSfilMegOcArN/CpkJdleklGpkQhPi0UJDXEXKjiKrG9WcZJgxCpK1H3SlVOiF5Jy4UqqGbw/AmLnQqZl1EZsGuFIq0jTElToFa/bAuHkwvI01kElcXTowVkqpENMQV6qe3t8KE16DCd3grxeAS/+KlFI20f0Hperh9W/h1+/AnQPhhj52V6OUauo0xJWqo+fWwh8/hD+PgCu72l2NUkppiCt1UsbAIx/DPz+zzn9f3N7uipRSyqIhrtQJ+A1MWwavfQuzL4cBreyuSCmlfqAhrtRxVPngjrdhxU54aQyc08LuipRSqjYNcaWOobTK6gd9yyGYNw7aNaghfpRSyqIhrtQRCsrh529AuRdevRKykuyuSCmljk1/4apUDbuLYexc67ffc8dqgCulGjYNcaUCvj8Io+dCmzR44QpIjbO7IqWUOjENcaWwulEdOxcGt4anR0F8jN0VKaXUyWmIqybvvUA3qld3g4d+pN2oKqWih17Yppq0+d/AXe/CXefDL3vbXY1SStWPhrhqsp7+HP62Av52IYzubHc1SilVfxriqskxBv68Amavs85/j2hrd0VKKXVqInr2T0RuEJGVIrJcRDocMW+0iKwWkY9E5Mc12h8ItC0WkeaRrFc1Pl4/3PkOvLQe/j1aA1wpFd3EGBOZDYlkAIuBgUAv4C5jzLjAPCewDugLCLAc6A90Bv5sjLlURMYC/Ywxd9ZhW8YYI3UoKzIvXjUI5R741WL4Oh/+73I4K9PuipRSqk6Om2eR3BPvByw1xviMMWuAs2rMawbsNcaUG2PKgCKgAzAYeDOwzJvAoAjWqxqRogqY+BpsK4RXx2mAK6Uah0iGeDpQWONxzW8W+UBLEWkuIi2w9tTTaz7HGFMOJB65UhGZLiKm5hS2V6Ci0r4SGDfPOpQ+bxzkpthdkVJKhUYkQ/wQkFrjsb/6jjHGD9wCvAI8BXwB7K35HBGJA0qPXKkxZroxRmpO4XsJKtpsPgRjXoHsJOsceEa83RUppVToRDLEVwPDRMQpIr2BjTVnGmPeM8YMB64HKowx24EPgJ8EFvkJ8FEE61VRbt0+qxe23jnwzKWQ6La7IqWUCq2I/cTMGFMgIrOBDwEPMFlERgKpxpiXReRhoDdQAdwaeM7XIrJORD4CSoBrIlWvim4fbofr34TxXeG+IeDQ4zNKqUYoYlenR5Jend60/ed7uP1tuLUf3HwuiAa4Uiq6HfdTTDt7UY3KrHXwhw/gj8OtvtCVUqox0xBXjYIx8PeP4V+fwZM/gYvb212RUkqFn4a4ino+P9y3FBZ8D89fDv1b2V2RUkpFhoa4imoVXrj1LWs88FfGQhftmFcp1YRoiKuodbgSpiyEfYfh1SvhjNSTP0cppRoTDXEVlfJL4do3rPvzxkHzo/ryU0qpxi+io5gpFQo7imDMXEiJhZfHaIArpZouDXEVVb7Oh9GvWOe+Z/0UkmPtrkgppeyjIa6ixse74Kp51s/H/vFjiNOTQUqpJk4/BlVUWLIJblkCN51r9cSmvbAppZSGuIoCL66He5fC/UNgUg+7qwk/v9/Pnj178Hg8dpeilIqgmJgYWrZsicNR94Pk2ne6arCMgX98Co9+Ao9cBKPOsruiyNi1axcpKSmkpOjA50o1JcXFxRQXF9Oq1VE9Vmnf6Sq6+Pzw+w9g3gZ47jIYdIbdFUWOx+PRAFeqCUpJSeHgwYP1eo6GuGpwKr1w29uwepf1E7JzsuyuSCmlGiYNcdWgFFfCLxfC3kAvbGem2V2RUko1XBriqsHIK4XrXgeHA+ZrL2xKKXVS+jtx1SBsOWR14pIRr72wKaVUXWmIK9ut22d1o9o7G577KSS57a5INRRer5cBAwZw+PDhsG9LanQ+0LNnz5Mu7/P5GDhwIIWFheEsS0WxiooKRo8ezW9+85uwbUNDXNlq2Ta4aj5c0QkeHQlup90VqYbkhRdeYMSIESQnJ0d0u2vXrj3pMk6nk0mTJvHYY49FoCIVjV588UW6du3Kxo0bw7YNDXFlm1e/gSn/gVv7w31DwKG9sDVIBQUFTJ48md69e9OpUyf+/ve/R2zbzz77LFdeeSUA06ZN4w9/+ENw3vvvv88FF1wAwFVXXcW5555Lly5duOWWW6jZ/4WIcP/999OvXz969epVp4CuuVf+wgsv0LFjR4YMGcJtt93GsGHDgvPGjh3L7NmzT/dlRgU73wfRasGCBeTm5jJgwICwbUMvbFO2+Ndn8NBK+NuFMLqz3dU0XD6/j7zSvLCsOysxC6fjxIc+/H4/o0aNYuLEicycORNjDHl54annSB6Ph7Vr19KtWzcArrnmGkaNGsV9990HwJw5c5g4cSIATz75JJmZmfj9fq644goWLVrEJZdcElxXdnY2q1evZuHChVx33XV1CnKAffv2ceedd/L555+TnZ0d/EJRrVmzZogIO3fupHXr1qF42cfm90FFmP7d47KgAb8PTpXf76e0tDRs609MTDxpz2qbN2/m66+/DuvhdA1xFVF+Aw9+CHO+gmcuhWFt7K6oYcsrzSP377lhWffu23fTMrnlCZdZtGgRcXFx3HzzzYC1h5qdnR2Weo504MABkpOTcTqtgOnYsSNpaWl88skndO/enUWLFvHII48AMGPGDF5++WW8Xi/5+fkMGDCgVohPmDABgFGjRjFp0iSKiopITU09aQ2rV69mwIAB5OTkADBx4sTgNqtlZWWxe/fu8IZ4RR68Hp73AZfvhoTwvA+WLVvGmjVrGDZsGBs2bGDSpEkhKbkuSktLw3q04Pbbbz/haZ68vDzi4uLwer1hfW9oiKuIqfLBr9+BD3fAS2OgZ2SyIKplJWax+/bdYVv3yaxdu/aoQ4HFxcX069eP0aNH89lnnzFz5kxyc2sHzM0338w999xD69atGTt2LE888QSPP/44RUVFdOzYkalTpzJr1iyWLFlCv3792LZtG48++mitdcTHx1NZWVmr7ZprruGFF15gyJAhDBkyhJSUFD744ANeeuklli9fTmpqKnfccQcVFRXHfU3GGESEZ555hieeeAKAxx9//ITLnkh5eTnx8fEnXOa0xWVZYRuudZ/Eqb4PqpWUlLB//342bdrEb3/7Wzp16sS8efP45ptvQvISjiUxMZHbb789rOs/kf379/Ptt9/yyiuvAPDcc8+RlpbGFVdcwbRp0xg9enSdLqA8GQ1xFRElVXDjm7C10PoNeLt0uyuKDk6H86R7y+GUk5PDG2+8gd/vx+FwkJeXx/r16xk1ahQPPvggM2bM4NNPPz3qw/vaa69l9uzZTJo0iZycHGJiYqiqqiIjI4M5c+YwdepUAEaMGMH111/Pb3/7W9avXx88dA6QlpZGXFwcBQUFZGRkADB+/Hh69uzJli1buOGGGwAoLCwkLS0t2GXl/Pnzue6662rVM2fOHG666SYWLlzImWeeSUpKClOmTGHKlCknfP39+/fnpptuYu/evWRnZzNnzpxa8/1+Pzt37uSss8Lcsb/DedK95XA61ffBkf75z38yffp0unTpwltvvRXWmh0OR8QviKxp9+7dDB48mKysLLZt20aPHj1YuHAhvXv3pqioKCQBDhG+sE1EbhCRlSKyXEQ6HDFvpIisCcx/okZ7qYgsC0xXRLJeFRoHymD8fDhYZvXCpgEePSZOnEhiYiKdO3emZ8+ePPXUU6xevZpzzz0XgPXr19OrV6+jntevXz8+/fRTZs2axbXXXsusWbO48MILeeCBB/D7/cHlqkdqO96IbZdddhnvvfde8HGzZs3o3bs3H3/8MSNHjgRg5MiRpKWl0bVrVyZMmMCQIUOOWk/1XuO9997LrFmz6vz6s7Ozeeihh4J7/h07dqw1f/Xq1fTv3z/8e+I2O9X3wbGc7MhGY+D3+3nzzTdp2bIll19+OT6fj65du/Ltt9/ypz/9id/97nch21bE9sRFJAP4BTAQ6AX8GRhXY5H7gNHGmB0islBEuhtjvgS2GmOGRapOFVrbC+Ga1yEnGWaMgpRYuytS9REbG8uLL75Yq23cuHHk5eWxatUqunXrxplnnkleXh6vvvoqN910U3C5QYMGMX/+fKZNm4bP5+Oxxx5j7dq1tQ6RL1u2jB07dnD48OFae+HVbrnlFm677TbGjfvho2LBggW1lnG73bz++usnfB133XUXd9111wmXqXlFe837EydODF5At2zZMlasWBGcN3PmTG699dYTrrcxOJ33QU033ngjv/vd7+jYsWNEfvtvF4fDccxTNDt27ODyyy+nRYsWoduYMSYiE/Bj4C81Hq87Yv4/ge6AE3gXaBVoLwKWA/8GmtdxW6aOdakw+jLPmN7/MubGhcaUe+yuJnps2bLF7hJOaMyYMUe1vfnmm+att96q13qee+45M3fu3JMu9/zzz5vi4uJ6rbumwOdBSCxdutQMHTrUGGOM1+s1Tz31VMjWHW1O931wySWXhLqkRuE4f//HzbGIjScuIj8DzjDG/CXw+EtjTPca838EzAbKgCXGmP8KtGcaYw6KyJXAKGPMpCPWOx24/8jtGR1P3FYf7YDrF8KYzjB9KDi1R4I627p1K23btrW7DKWUDY7z93/cPIvkR+shoOZvOvxHzH8c6AOcBTQTkfMBjDHVg6vOBXocuVJjzHRjjNScQl+6qo8F38HPF8DN58IDwzTAlVIqXCL58boaGCYiThHpDRzZD50XKDLG+LECP11EEkWkuheCwcC2iFWrTsmzX1hjgf9xOPzXedAErmFRSinbROzCNmNMgYjMBj4EPMBkERkJpBpjXgb+CCwTkSpgB7AEa897hoiUYIX8DZGqV9WPMfDXFfDcOvjXJXBhO7srUkqpxi9i58QjSUSMnhOPHI8P7noP3t0Cz14G59r3c9ZGQc+JK9V01fecuHb2ok5LmQduehO+Pwjzr4SOGXZXpJRSTYeGuDplBeXw8zegzGt14pJjX+dISinVJOl1w+qU7CyGsXMhxgnzxmqAK6WUHTTEVb19kw+jX4H26fDCFZAaZ3dFSinVNGmIq3pZtQvGzYML28JTl0CcnpBRYeT1ehkwYEBEuug8nQEpfD4fAwcOpLCwMIQVqWhXUVHB6NGjwzqeuIa4qrM3N8K1r8PkXvCnEeDSd48KsxdeeIERI0ZEZDSqtWvXnvJznU4nkyZN4rHHHgthRQ1LQUEBkydPpnfv3nTq1CmsY3U3Fi+++CJdu3Zl48Yju0UJHf0YVnXy/Dq4ZQlMGwK39ddOXJoSOz+8n332Wa688koApk2bxh/+8IfgvPfff58LLrgAgKuuuopzzz2XLl26cMstt9QawEREuP/+++nXrx+9evU6bljXHF1r6dKl9OvXjx49etCnTx+2bdsGwIcffsjgwYPp06cPQ4YMYcOGDcHnjB07ltmzZ4fstdfk80NeSXgm35F9Zx6D3+9n1KhR9OnTh88//5xvv/2Wn/3sZ2F5rY3JggULyM3NPWos9lDSg6HqhIyBh1bCM1/AP34MIzuc/Dmq8aj+8J44cSIzZ87EGENeXl5Etu3xeFi7dm1wdLNrrrmGUaNGcd999wHWGOHVo4s9+eSTZGZm4vf7ueKKK1i0aBGXXHJJcF3Z2dmsXr2ahQsXct11151wr/vAgQNMmDCBd955h65du1JeXo4xhoKCAn7961+zZMkS0tPTWbVqFZMnT2bVqlWANUyqiLBz505at24d0n+LA2Vw3syQrjLok8mQlXTiZRYtWkRcXBw333wzYH3hyc7OPum616xZw4YNG5g0adJJlw2HyspKqqqqarUlJyfj9/spLS2t1R4fH4/L5aK0tLTWcLkul4v4+HiqqqqCI/C53W5iY08+JOPmzZv5+uuvw3o4XUNcHVfNTlzmjIa+2omLLQ5XHuZw1Q/nhAUhJzkHr9/L/tL9tZbNiM8gzhVHfmk+Hv8PY3THueLIiM+gtKqUosoikt3JJMee/BD1qX54h8KBAwdITk7G6bR6Xu7YsSNpaWl88skndO/enUWLFvHII48AMGPGDF5++WW8Xi/5+fkMGDCgVohPmDABgFGjRjFp0iSKiopITU09eqPAxx9/TN++fenatStAcKzwpUuXsnHjRoYPHx5ctqCgoNZzs7Ky2L17d8hDvFmCFbbh0Czh5MusXbv2qL3J6jHaR48ezWeffcbMmTPJzc2ttUxJSQn79+9n165d3H333WRlZdG/f38GDhzI3/72N4wxxMbG8tBDD4XyJQWtWrWK5cuXBx/HxsZy9913U1hYeNRQoePHj6dTp068+OKL7N69O9jetWtXxo4dy9q1a1m8eDEAQ4cOZdiwYSfcdl5eHnFxcXi93pC/H2rSEFfHVFIFNy+CjQdh3jg4K9Puipquh1c9zO+X/z74OCU2haK7i9hWuI2Oj3esteyC8Qu4tNOlXPripazevTrYfmXXK3l57Ms8t/Y5/nvxf3P/0PuZPmz6Sbd9qh/eN998M/fccw+tW7dm7NixPPHEEzz++OMUFRXRsWNHpk6dyqxZs1iyZAn9+vVj27ZtPProo7XWER8fX2vscbD2xl944QWGDBnCkCFDSElJ4YMPPuCll15i+fLlpKamcscdd1BRUXHc12SMQUR45plneOKJJwBqfaAfrxdLYwwDBw5k4cKFx113eXl5MPRDyek4+d5yOOXk5PDGG2/g9/txOBzk5eWxfv16Ro0axYMPPsiMGTP49NNPj3ofVHv88ce588476dHDGsPq7rvvxul0kpKSwsaNGzl06BDp6ekhr3vAgAH06dPnqPa0tDRuv/32Wm3V/9+uvvrqo/bEwbrwsXPnzoC1J34y+/fv59tvv+WVV14BrFC/9957SUtLo2vXrlx33XWn9JqOpCGujpJfao1C5vXDa1dBto0fHgruGHAH1/e5PvhYAj0wtklrw+7bd9daNiPe6jLvP1f/56g9cYCf9/w5ozuPJtldtwvFTvXD+9prr2X27NlMmjSJnJwcYmJiqKqqIiMjgzlz5jB16lTg/9m77zCpyvP/4+9nO213WcrSe5EuvYgFRcSGRCVRf0aNGgG/GmOMsSXRJMYYTaKJKRpNLLEjVsRKE6miUgXp0us2Ftg28/z+eGZhWLayM3Nmls/ruuaamXPOztyHXc59ng5nn302N910E/feey8rV648UnUO7kKbkpJCVlYWGRnuvK644gpOPfVUNm7cyMSJbimFnJwc0tPTSU1NZf/+/UydOvW4C+RLL73E5MmTmTZtGu3btyc1NZUbb7yRG2+88bhzHjFiBJMmTWLVqlXHVKcPHz6ciRMn8tVXXzFgwAD8fj/Lli2jf//+gGt62Lp1K926davWv20sufrqq/n000/p0aMH9erVY/z48SQlJTFo0CAAVq5ceVxSDGatJS7uaBcsv9/PhAkTwtpWDK7kXV61d1xcXIWdJRs0aFDu9qSkpGol71Lbt2/n9NNPJzMzk82bN7Nt2zYKCwu5/vrrj9wMhIKSuBxjUzb88G1okwr/vghSq272kTBrlFx+1XdCHOSgJwAAIABJREFUXAKtGpXfxtGsQbNytzdIakCDpPIvUuU50Yv30KFDeeihh/D7/Vx77bU899xzjB49mvPOO4/p06cfOa64uPiY57LGjRvHjBkzmDBhAuDanQcMGMD8+fN56623ABg7diz//e9/6dWrF+3ateOMM8447nNKaw8KCwt57rnnKj3nJk2a8NJLL3HddddRVFREYmIiU6dOpX379kyZMoVbb72V/Px8ioqKuOyyy44k8UWLFjFs2LCwlMS9lpyczCuvvHLMtgkTJrB7924WLFhA7969ad++Pbt37+bNN99k8uTJxxx76623cu+999K6dWuGDBnCT37yE+68807efPNN8vLyeOqppyJ5OmHn9/t5//33adWqFePHj+ef//wnI0eOpHXr1vz2t7/lqquu4txzzw3Jd2kBFDli6S5XAh/ZFv50LiTrFs8T0b4AyoQJE2jVqhXGGHr16sWPf/zjci/ejz76KFOnTmXhwoUsWrSIv/3tb/Tt25cXX3yRFStW8Nxzz/H+++/TqVMncnNzefLJJ4/7rjVr1nD77bcfaYs8EcaYCqvIQ+nGG2/kyiuvPNJjvq67/PLLeeONN47ZNn36dBISEhgzZoxHUUWvu+66C5/PR3Z2Ng899BCZmZnlHlfTBVCUxAWAGZvg/6bD/+sD950OcRpC5ploT+Khung/99xzNGzYkMsvv7zS4/73v/8xfvz4Ex4rHokk7vP5ePrpp5k0aVJYv0fqPiVxlMRr6pWV8MtZcPdp8OMBXkcj0Z7ERSR8tBSpVJu18LfF8Pcv4LExMK671xGJiEhNKImfpEr88KtZ8N5aeP4SGBG+YYwiIhImSuInocPFcMsHsGIPTLkcepTfkVlERKKckvhJJuswXP8uHCiCt74PrVO9jkhERE6UFkA5iWzJhcteh3gDUycogYuIxDol8ZPEyj1w6etu+tSXLoX0FK8jkookJiaSl5fndRgiEmF5eXkkJibW6Gc0xOwkMPc7mPg+XNYDHjjTzcMs0cvv97Njx44KZzETkbopMTGRVq1aHTNFbYDGiVeg7p18GW+ugV98Aj8bBpMHaR1wEZEYVOGVO6JlMmPMRGPMfGPMHGNMlzL7xhpjlgT2/706PyMVsxaeXOIS+B9Hw82DlcBFROqaiJXEjTEZwAfACKA/cJe1dkLQ/nnAldbaLcaYacC9wLbKfqaS7zqpS+I+P/z2M3j9G3jyQjizvdcRiYhILUTFjG1DgVnWWh+wxBhTdr2+FUC6MWY7kAJkVeNnpIyCErj9I1i8HV6/DPqUP8e+iIjUAZGsTm8M5AS9L3tnMRX4EPgWWGOt3VaNn5EguQVwzdvwzV63DrgSuIhI3RbJkng20Cfovb/M/ieAgcBu4GVjzGnV+BmMMQ8A959gTHXqpiAtBV6vfEEoERGpQyJZEl8EnGWMiTfGDADWldlfAuRaa/245N24Gj+DtfYBa60p+wjzuYiIiHguYiVxa22WMeZ5YC5QDNxgjBkLpFlrXwMeBGYbY4qALcCH1tqSsj8TqXhFRESiXZ0cJy4iInIy0NxdIiIiMUpJXEREJEYpiYuIiMQoJXEREZEYpSQuIiISo5TERUREYlQkZ2yLOsYYja8TEZGoV9EkZhonHiI1WDkt6tWlcwGdT7TT+UQ3nU90U3W6iIhIjFISFxERiVFK4iIiIjFKSTx0fuN1ACFUl84FdD7RTucT3XQ+UUwd20RERGKUSuIiIiIxSkm8mowxycaY+caYHGPM5YFtHYwxc40xc4wxHxlj0gPbhwaOXWCMGedt5OUzxvQwxnxujPnMGDPLGNPJGFPfGPN64JyeMsbEBY6N1fO51Riz2BgzzxjzRNCxUX0+5Z1L0L5njDHTgt6PC5zHfGPMUG8irlxF52OM+aUx5tPA9j6BbTF5PjF+LWhljFkUiH2+MaZvjF8LyjufmLwWVIu1Vo9qPHA3PC2BB4DLA9t+D1wTeP1z4CeB1/OAVkBD4Csgwev4yzmfZkB64PVY4BngFuCOwLa/AxfG+Pl04WiT0avAyFg4n/LOJfC6O/AOMC3wPgH4OnAerYB5Xsdeg9/NRcB9ZY6L5fOJ5WtBPBAXeH028GKMXwvKO5+YvBZU56GSeDVZa/3W2p1lNq8A0gOv04E9xpgUIN5au8Namw+sw/0BRRVr7V5rbU7gbQngA04H3g9sex84PZbPx1q73gb+p5Zui4XzqeB3A/Ar4I9Bh3YFvrXW5ltrdwDxgfOLKhWcz+VAhjFmpjHmn8aYJGL7fGL5WuCz1voDb1NxN1KxfC047nxi9VpQHUritTMfmGSMWQGcD7wJZAA5QcdkB7ZFJWNMfeC3wONAY47GXhp3LJ9P6bYzgObW2gXE0PkEn4sxZjCwE9gRdEjw74vA66g8Fzjud9MSKLDWng1kAdcT2+cT09cCY0xPY8x84AlgLjF+LSjnfEq3x+S1oDJK4rXzMPBza20f4D/AnbgLUlrQMemBbVHHGJOAq1p6xFq7GvdHXBp7adyxfD4YY3rhSq9XBQ6LifMp51zu5dhSOBz7+4IoPReo8G/t48DuD4G+xPb5xPS1wFr7jbV2BHAxLvHF9LWgnPOJ2WtBVZTEaycO2Bd4vR9obK0twFXVtDTGNAC6Aeu9CrAixhiDu9h8YK19O7D5M+DCwOsLgLmxfD7GmHbAc8BV1tp9ALFwPhX8bjrj2vaeB4YYY+7AVf91N8Y0MMa0BEoC5xdVKjifOcDAwOtBuHOJ5fOJ5WtBctDbbOAQsX0tOO58YvVaUC1eN8rH0gOYCmwElgN/BvoAnwOzA492geOG4qrXFgLjvI67gnMZi/vPWhr740B94HVc9dNTHO0cEqvn82rg91W6bWwsnE955xK0rwOBjm2B9+OABYHzGep17DX43aQALwGzgLeABjF+PrF8LRiJu6maFYh9QIxfC8o7n5i8FlTnocleREREYpSq00VERGKUkriIiEiMUhIXERGJUUriIiIiMUpJXEREJEYpiYuIiMQoJXERqZAxJj/oYY0xB4Pet6vgZ+42xtxXzvbnjDF3B70/3xiTZYw5M5znIFKXJXgdgIhEL2ttw9LXxhgLdLbW7qrix87DTTtaIWPMWNxkL5dZa+fUOlCRk5RK4iISMsaYhrjpK7+q5JgxwMu4JX1nRSo2kbpISVxEQmkUMMceXQqyrLNwU2BOsNbOjFhUInWUkriIhNJY4KNK9p8GbMbNjy4itaQkLiKhNIbKk/ifcMs9TjXGJEYmJJG6S0lcRELCGNMZOFhFx7dCYDzQGPifMUbXIJFa0H8gEQmV86i8FA6AtTYft0Z1L+Bf4Q5KpC5TEheRUKmqPfwIa20Wrup9tDHm4bBGJVKHaT1xEam1QPv2FqC9tbbI63hEThYqiYtIKGQA9ymBi0SWSuIiIiIxSiVxERGRGKUkLiIiEqOUxEVERGKUkriIiEiMUhIXERGJUUriIiIiMUpJXEREJEYpiYuIiMQoJXEREZEYleB1AOFgjLHWWlONQzVdnYiIRLsK85lK4iIiIjFKSVxERCRGKYmLiIjEKCVxERGRGKUkLiIiEqOUxEVERGKUkriIiEiMUhIXERGJUUriIiIiMUpJXEREJEYpiYtE0uyLYN1TXkchInWEkrhIJCU0hLw1XkchInWEkrhIJKV2h7xvvY5CROoIJXGRSGrUDQ4oiYtIaCiJi0RSanc4uBl8hV5HIiJ1gOdJ3Bgz0Rgz3xgzxxjTpcy+scaYJYH9f/cqRpGQSe0G1g/5G7yORETqAE+TuDEmA7geOB24A/hDmUN+BVxqrR0BdDDG9I1wiCKhlZgKKS0gb63XkYhIHeB1SXwoMMta67PWLgG6ldm/Akg3xsQDKUBWpAMUCbnU7moXF5GQ8DqJNwZygt6bMvunAh8C3wJrrLXbyn6AMeYBY4wNfoQvXJEQaNRNJXERCQmvk3g2kBb03l9m/xPAQFwJvakx5rSyH2CtfcBaa4If4QtXJARUEheREPE6iS8CzjLGxBtjBgDryuwvAXKttX5cwm8c6QBFQk5jxUUkRBK8/HJrbZYx5nlgLlAM3GCMGQukWWtfAx4EZhtjioAtuKp1kdiWegoU7oOCfZDS1OtoRCSGGWvrXhOyMcZWs1q97p28RD+/D6Y0hLM+hMwzvY5GRKJfhfnM6+p0kZNPXDyk9oDclV5HIiIxTklcxAtpvSF3lddRiEiMUxIX8UJ6LyVxEak1JXERL6QFkngd7JMiIpGjJC7ihbReULgfCnZ7HYmIxDAlcREvNGgPCQ1UpS4itaIkLuIFEwepPZXERaRWlMRFvJLeC3I0zExETpySuIhX0tRDXURqR0lcxCtpvd2EL+qhLiInSElcxCuN+0FxHhzc7HUkIhKjlMRFvFKvJaRkQvZSryMRkRilJC7ipcanKomLyAlTEhfxkpK4iNSCkriIl9KVxEXkxCmJi3gpoz8c2gKFWV5HIiIxSElcxEsNu0B8fZXGReSEKImLeCkuHtL7KomLyAlREhfxmjq3icgJUhIX8VpGf8hREheRmlMSF/Fa+qmQuxp8BV5HIiIxRklcxGvpvQG/FkMRkRpTEheJpC9/Bqv/fOy2hPqQ2gOyvvQmJhGJWUriIhFlYf+i4zc3GQL7F0c+HBGJaUriIpFU0RriTQbD/i8iH4+IxDQlcZFISusFeWvBV3Ts9ozBLrmXHPQmLhGJSUriIpGU3gu63gy+w2W29wUTD1lfexOXiMQkJXGRSEpMhUF/haS0Y7fHJ7lJX7JUpS4i1ackLhJpm1+FLW8cvz1jsDq3iUiNKImLRFrWEvju1eO3Nxmizm0iUiNK4iKRltYLclcev73JYMjfoGVJRaTaPE/ixpiJxpj5xpg5xpguZfY1NMY8a4yZYYyZ5VWMIiGV1gsOrIeSMp3bUrtDQiOVxkWk2jxN4saYDOB64HTgDuAPZQ65H/iPtfYca+2oSMcnEhbpfaBRFyjYeex2EwdNBqlzm4hUm9cl8aHALGutz1q7BOhWZv9I4ApjzGxjzC2RD08kDBLqwUVroGGn4/c1GQr7FkY+JhGJSV4n8cZATtB7U2b/AOAdYDRwqTGmZ9kPMMY8YIyxwY/whSsSIvkbYe/847c3HQH75oP1Rz4mEYk5XifxbCB4wGzZK9c+4FNrbQkwE+hd9gOstQ9Ya03wI3zhioTI1rfgqzuO395sBBRlQ96ayMckIjHH6yS+CDjLGBNvjBkArCuz/3Ogf+D1IGB9JIMTCZvG/SFnOfh9x25PbgKpp8Deed7EJSIxxdMkbq3NAp4H5gKPAfcYY8YaY34QOORu4BFjzDzgG2vtVx6FKhJajU8F3yE4sPb4fU1HKImLSLUkeB2AtfZJ4MmgTeuD9m3CtYeL1C3JGdCgPWQvhbQex+5rdhp887A3cYlITPE8iYuctMYsgpRmx29vdhocWAcFeyCleeTjEpGY4XWbuMjJK6U5FOcev71RN9c2Xl7vdRGRIEriIl7Z8QG81xVsmVGRxgSGmqldXEQqpyQu4pWM/lC4Hw5uOn5fs9PUuU1EqqQkLuKVei2hflvYt+j4fc1GutXOSg5FPi4RiRlK4iJeajIE9peTxDMGg0mEfQsiH5OIxAz1ThfxUq97XLIuKz7JVanvng0tzol4WCISG5TERbyUMbDifZlnwY7pEQtFRGKPqtNFvOT3wQcDIKucyQibj4L9i6HkYOTjEpGYoCQu4qW4eLdiWXnLjzYZBHFJGi8uIhVSEhfxWkWd2+ISXS/1PbMjHpKIxAYlcRGvNR1afhIHyBwFu2dFNh4RiRlK4iJeyxwF7X5w/MxtAM3Pgv1fQHF+xMMSkehnbHkXjhhnjLHWWlONQ+veyUvd4i+BNzJg5GvQ6nyvoxERb1SYz1QSF4kGG5+HDc8evz0uwZXUd34S+ZhEJOopiYtEg4NbYNPz5e9reR7s+iiy8YhITFASF4kGzU93ndt8hcfvazkGcr+Bg1sjH5eIRDUlcZFo0GQoWB9kfXn8vkZdoGEn2PVx5OMSkaimJC4SDRLqQYerwVfBqmUtz4OdSuIiciz1TheJBVvfhkXXw6V73SxvInIyUe90kahXlA2r/+LmUy+rxdlQnOfWGBcRCVASF4kmX/8cclcevz0xFZoOh53qpS4iRymJi0SLpMaQ3gf2zC1/f6vzYcf7kY1JRKKakrhINGl+BuyZU/6+1uPc0qSHd0U2JhGJWkriItGkxWg4uLn8fWm9oEEHlcZF5IgaJXFjzEBjzAJjzAZjzHJjzOhwBSZyUmo9DsZ+Uf4+Y6D1xbD9vcjGJCJRq9IkboxpUGbTn4ErrLWdgSuAciZ7FpETZgzkb4bspeXvbzPOjRcvORzRsEQkOlVVEv/cGHNt0PtioJUxJgFoCxSELTKRk9WGZ+Dru8rf1+wMiEuE3TMjG5OIRKWqkvjpQE9jzFxjzDDgZuBOYDlwG640LiKh1OIc2DsXfOXcI8cnudnbVKUuIkBCZTuttfnAXcaYTriq9APALdbaHZEITuSk1HS4e963wC1DWlbrcbD0LrD/BKO+qSInsyqvAMaYTKAJcAPwHPCmMeZeY0xSmGMTOTnFp0Cz0yseL976QijcC/sWRjYuEYk6VXVsuweYA9wOzAbaASOAXGCeMeaycAcoclI67RXo/cvy9yU1hszRsGVKZGMSkahTVUn8DmCgtfYqYCTwM2ut31r7D2AMcEZtAzDGTDTGzDfGzDHGdClnf7wxZo0x5ue1/S6RmJGc4SZ1Kc4vf3+7CbD1DbD+yMYlIlGlqiS+HLg7MB787sB7AKy12dba22rz5caYDOB6XAe6O4A/lHPY9cCG2nyPSMyxFj4aAtveKX9/2/FQsBv2LYpsXCISVapK4pfiOrNdCuwCbgrx9w8FZllrfdbaJUC34J3GmBRgHPBGiL9XJLoZU/lc6apSFxGqSOLW2hxr7SPW2puttX+z1h4K8fc3BnKC3pddM/VW4CkqWffbGPOAMcYGP0Ico4RQbgGs3Q9f7gSf3xU4P98CX2yHjdlwoNBtE6DVhbDzQ/CXlL+/3QTYOkVV6iInMa/Hp2QDaUHvj1yNjDFpwFnW2mmVfYC19gFrrQl+hClWqaEtubAkMBjxk43Q7yno+xSc+yJ8/w3ILQS/hcnvw1VvwagXoPeT8Nwy9zOvroQnv4TF26GggjxWp7UYDSUHK+6F3uYS126uKnWRk5axHhZ7Am3i7+M6zfUD7rHWTgjsGwr8DZfoWwOJwK3W2k+q8bm2mslcZb4QW5cF734LH25wJe7hbeDVy2DnAVizHzqkQWZDqJfgaoxLWQv5RbDnEKQnQ5P68NSX8NEGWLnHHXN1H/j1mVDsg8R4b84v4nJWQGoPiKtgSodZ57v9A/8S2bhEJJIqzGeVJnFjTIK1NqxlIGPMJOAa3JSuNwBdgDRr7WtBx1wHNLXW/qman6kkHkGFJfBdLnRrAk9/5Urd53WGMZ2hbWrtP7+gBJbtdol+WBv44zyX3Md2gct7QKfGtf+OqFaUC0lp5e/b+IKb+GX81ooTvYjEuhNO4juAJcBHwEfW2vWhjy30lMQjo9gHU76BJxZD0/rw3pUu0ZowN2jsyndJ/L218MUOmNAD/jQmvN/pmUPb4Z32MG4TNGh7/P7ifHgzE06fCq3GRj4+EYmEEy+J4yZ3OS/wSAM+xiX1mYFpWaOOknj4LdsFt3wIh4pg8iC4ui+keFAQ3JgNew/B0Nbw7FL3+tp+kFl2/b1YNq0HdLsFuv1f+fvnXwO2BE57ObJxiUiknFgSP+5gY5rhJnk5DxiFG7/9IfCqtXZz7WIMHSXx8Nl9EJrXh/2HXcez6/tD/USvo3I+2Qh/XQRr9sEl3eHmwdC5LlS1L7vPzaN+TgUrl+36FOZcDJfuhsQQtF+ISLSpMJ/VqHe6tXavtfYla+011tq2uOlYAQbXJjqJDVNXw1nPw7ytrvr8liHRk8ABzu0E710BL37P3WRMW+u2F/m8javW2l4Ke+ZAwd7y9zcfBUlNYIumUxA52XjaOz1cVBIPrQOF8KvZ8MF6eOBMuKJX+Nu9Q8FvXRv9eS/BgJbws2HQoqHXUZ0Aa11pu/mZbinS8iy9x5XWR8+OaGgiEhGhKYnLyekvC10V9bQr4cresZHAAeIMxMfBH86GzTmuFuGvi+BwsdeR1ZAx0PJcgqZROF7HH7rSev6miIUlIt5TSVwqtHY/dM2AwyXuNrBeFFWd15S1buz6nxfAf8dBu7TI9KQPmdxv4KNhMH4LJKWXf8yHQ1wP9b6/jWxsIhJuKolLzbz7LVz4Cizc7tq9YzmBg0vW53eBT652CfzzLXD5FHejEhNST4GEBrC9grnUAbrcBBueAX+sVTWIyImqVhI3xtQzxjxqjNlsjMkLbDvPGPPT8IYnXnh9Fdz+MTw4ys24VpeUlry7N4HWqXDBy/DIvBioYjdxroPbltcrPqbDlW6a1u2VzlQsInVIdUvi/wRaAhcBpX19lwOTwhGUeGfWZrh3Jjw2Bn7Qy+towqdZA/jbWFe1/t4613Ev6nW4CnZ+AIVZ5e9PaAAdr4F1T0Y2LhHxTLXaxI0xe4A21toiY0yWtTYjsD3XWlvBfJDeUZv4iSsMTHE6pLXXkUROQYnrgd+sgauFGNsFUpO9jqoc1g/b34OWYyG+ggBzVsL0vnDxOmjUObLxiUi41LpNPAdofswnGtMJ2FGLoCSKfLQBfvcZJCecXAkc3ExzzRrAoWI369uYF+Gz77yOqhwmzq1cVpn03tDsNFj/78jEJCKeqm4S/yvwnjHmciDeGHMx8CrweNgik4hZsA1u/cB1+DqZ1U+Ed66ACT3hR+/CvTOicKKYw7tgajM4uKXiY7pMgo3/BV9h5OISEU9UK4lba/8BPIRbZWwbcAvwmLX2qTDGJhGwMRtumgaTBro5x092SfFwx3B4+/uQlACJcW4oWtSo18JVk2+uZJ70dpe5Untlx4hInaBx4ie5X8+G7MOuk1fMjJmOsJ9+BK0bwU+HRsk65qv/BBufgwtWVPxLW/Fb2DIFLliuX6xI7Ktdm7gx5iFjzNAy24YaYx6sbWTijdIpSe8/Ax49V9f5ykzo6ZZcnfAGbMn1Ohqg/VWQtxqyvqz4mK6TIX897Pw4cnGJSMTVpHd6W2ttYdC2FOA7a21mGOM7ISqJV+2R+a6995enex1JbMg6DL/4FBZug+lXRUH/gZ0fuw5sCZWsubp4EuRvhLOVyEViXK17p9tyjo2vwc9LFPl8Czz1JZzT0etIYkdGPXj6InjifGibCjkFkF/kYUAtx4CJr7zz2im3u4VTspdHLi4RiajqJuFPgceNMckAgec/ATPCFZiEx96Dro33lsF1bza2cDMGRnVwz39dBBe9At9UsDpo2FkLHw6uvPNaandofRGs+XPk4hKRiKpuEr8N6AhkGWM2AlmB9/8XrsAkPKZ8Ax3T4dYhXkcS2+46DYa2hvGvwWurPAjAGGgzzs2VXpkev3CJPn9zRMISkciqUe90Y0xroDWw1Vq7M2xR1ZLaxCtmLRwshoYVLEstNTN1Ndw3E166FAa2jPCXH9gA73WBC1dBWs+Kj5txNjTsAkM1AYxIjKown9U0idcHGgZvs9buOfG4wkNJ/Hjf5bh1wf842s1QJqGz+yBkNoA9B+FAEXRuHMEvn3kepHaDQU9UfMzuOTDrXDcVa4P2kYtNREKl1kPMzjbGrAcOALuCHlFbGpej/Bbu/NStC54cDeOc65jMQAfxN76Bi1+B99ZG8MsH/6Pq9cMzz4SmI2DVw5GJSUQiprpt4k8DDwMNrbVxQQ+lhBjwwnJYs88tLarx4OEzeRDcMxJ+9jH8apZbTCbsGnWBhEauar0yfe6Hjf+Bg1sjEJSIREp1k3g68F9r7eFwBiOht+8Q/HEePHAWNK9kSLHUnjHww77w5gSY/R18EanlgTb8B2aeC/5KJnpvfhY0GQarfh+hoEQkEqo72cuvgVxr7V/DH1LtqU38KGth0XbXk1ql8MgpLHErwm3Lc49h4RzOV5QLb7eBES+5HusV2fM5zDjLdYRL7R7GgEQkxGrXsc0YsxrogluS9JiRsdbaSrrFekNJ3FmxB5rXh8yGVR8r4fHyCvjVbLjnNLihfxhvpJbcBjlLYfScyo/7bDwQB2e8GaZARCQMap3Ez6xon7W2iqtG5CmJu5LgeS/BhV3hzhFeR3Ny+3gD3P6xmyjmkdFuydOQO/gdTOvhFkVp1Lni43LXwPTeLtk3Oy0MgYhIGIRsiFkc0MJaG6nWvhOiJA5/XwwvrYAZ14QpaUiNbMiGm9+H342CIa3D9CVFOZCUXvVxiydBznI4d57aWERiQ62HmDU1xrwBFABrA9vGGWM0ZiUK7TkI/1gCvzxDCTxadG7sFk4Z0tol9BmbwvAlSemwexbkfVv5cX3ud0l8y5QwBCEikVTd3unPAOuBDKA4sO1z4PvhCEpqZ1ueW9zkgi5eRyLB4gP/277eCTdNg8cWujH8IfXt39xa4pWp1xJ6/RK++hkU54c4ABGJpOq2ie8DMq21PmNMlrU2I7A9x1pbjfq7yFJ1ukS7eVvhlg/g1Ex4/DxISwnRB+//Aj4eBheshLQeFR/nK4IP+kLrS6D/H0P05SISJrVeinQXcMyYFGNMX2Dzicd05HMmGmPmG2PmGGO6lNn3fGDfImPMtbX9rpPB7R/BJxu9jkKqclpbeP9KyCuETTkh/OAmg6H1xbD815UfF58EA5+Abx+D3NUhDEBEIqm6SfwBYLox5udAojHmVmBqYPsJM8ZkANcDpwN3AH8oc8iD1toRwBnAPcYYtfBWYuE2eHdthOfulhPWqhG8MQFObQEr98Dba0L0wX0fBN8hV9quTMtzoc14WHKLm1BARGJOtZK4tfYNYALQGfgM6ANcY619u5bfPxSYZa31WWuXAN3KfO+6wMvSdnh/Lb+vzrKefzv7AAAgAElEQVQW/rQArugFnZTEY0Zp5/CN2fCLT+H+2VBUycRr1ZLeG85635W2qzLgL64KfuNztfxSEfFClUncGJNgjHkKWG6tnWytvdBae5O1dkEIvr8xbgKZI19XwXF3Aa9aa4+7vBljHjDG2OBHCOKKOZ9vgWW74ZbBXkciJ2Jcd5hyuWsKuWIq7KptfzNrYcF1sOezyo+r3wb6Pwpf3Q6HttfyS0Uk0qpM4tbaEuAiwtMJLBtIC3p/XEnbGPP/gFOBcrvcWmsfsNaa4EcY4ox6/VrAvy+Clo28jkROVL8WMO1KaFIPsmq7SoExEF8Pvv4F2CoqsLrcBBkDYfFEVauLxJjqton/HnjMGNMkxN+/CDjLGBNvjBkArAveaYwZC1yHq7pXVXoFduW78eCjOngdidRWRj14+mLo2cwtoPLvr2qRV/v+BvLWwKYXKz/OGBj6H9gzGzZXcayIRJXqDjE7DJQ2sBXhSuUGsNba+rUKwJhJwDW4du8bcHO0p1lrXzPG7AJ2AHmBw6+w1u6qxmeeNEPMrIXxr8HZHeG2oV5HI6E0ezPc+gGMbAePngsNq9HEfZw1j8M3f4SL10FiFZPor/sXLL0Hzl8KDTucwJeJSJjUeu709hXts9Z+d4JBhc3JlMQ/+w5ufA/m/ggytdRonbM5ByZOg2K/K6HXeOSBvxh2fAitL6p6ilVr4bNLoHAfjP4M4hJOOG4RCanajRO31n4XlKxblb6PxgR+snnyS5jQUwk8ZuyeAwt/VO068g7p8PYPYFjr6rd9HSMuEdpcDAW74eCWyo81Bob+1y2msuI3J/JtIhJh1Z07vZMx5gvgC+DjwLbLjDEvhDM4qdz2PFiyA348wOtIpNoatIW0PjVaeKReIjx0DnRs7GpeHpwLJTXtIfL1nbDg2qo7uaU0hREvwjcPw66ZNfwSEYm0msyd/qy1tjlQEtj2MTAqLFFJtbROhS9udKU1iRENO0GPn7lkOncC7Jlbsx9Pgne/havedAvdVFv/RyB7Kax/uupjM0dBr/tg3g+qLr2LiKeqm8T7A/8KvC6tB8wHVInrkW15bv7tkM25LZFlLTQbCY26uvf7v6hWFfuAlm66VmPgwldgWZXdPAPqtXQTu3x9JxzcWvXxfX4NTYbB3EuhpLbj3UQkXKqbxDcAw8psGwlUseahhMuTX8ITi72OQk5YXDycchvUa+FKuzPOhkPVK/U2awAvfQ+u6u1eQzWb2DtdB20ugYObqz7WxLlq9eID8MUkjR8XiVLV7Z1+JvA6MAU31/m/gB8AV1pra1YfGAF1vXf6/kMw/L+ut/KZFY4bkJhSnAeJqZCz0iXNsz+F+OpVs3y0AV5YBo+dB82rWzfmK6je5+d+Ax8Ng173uIeIeKHWvdPnAAOBrcCzwH7g9GhM4CeDl1a6dvAz2nkdiYRMYqp7Tm4GnX7kEmz+Ztj2bpWl4FMzwWfhgpfd9LtVOrgF3unoEnRV0nrC6W/AivurnjRGRCKu0iRujNkReH7eWrvNWvtHa+3/WWsfstZuikyIUta2PPjRqTXq4Cyxol4mdL7Bvc76Ejb8x/2iSw5V2LM8s6GrXr+6D1z7DrxZ1cqiDdpBi9Ew7wpXIq9KyzEw5GlYdAPsmlGz8xGRsKq0Ot0Ykw10Ar6z1qZGLKpaquvV6eAKZ0riJ4HSX/TXv4DDu2BE5aM6F2yDLo1dW3luQSUdH4vz4IMB0PI8GPyP6sWy8vdu9rfRcyCjf83OQ0Rq44Sr06cAe4AGxphDZR6HjTGHQhqmVOnvi2FLrhL4SaP0F93zrqNt0uuegu3Tyz18eBuXwOd8B2c+Dx+sr+BzE1PhtFdh54dQuL96sfS6FzpdCzNHQ/aymp2HiIRFpUncWnsT0AY4DPQo8zgl8CwRsnqvWzPcH7P1B3LCkptAWuC/m+8Q+Avd69zV4C857vDT28H/DYaffAh3fgL5ReV8ZpNBcNEa99n+4qpjMAYG/g3afd8l8pyVJ34+IhISVbWJL7TW7gZeD55qVdOueuOF5XBWB03uctI75XZo+z1X1f75BNj88nGHxBk3k9+7P3DrzD8yv4LPikuE/UtgWk84vLPq7zbGVb+3Ge+GxSmRi3iqOm3izYAstYl7K7fArRb5rwu15KgEKcqFhPouoS+6Efo96DquBSkogSIfNEqCt76Fi7tCYnzQAf4SmDUWinPgnFmQWI1F6a3frT++dSqcOQ2ajQjteYlIsBNuE/8MN6ysvjHmm/IeIQ1TKrT7IJzbSePCpYykNFea9h2CpHRXNW79x0yXmpIAqclumtY/zoPLpsCG7KDPiEtww8j8xTD3MvCVV/dehomDIf+GLje5qvXt74f+3ESkSlWVxBOBAcAs4PzyjgmMIY8qXpTEn10KrRvBmM6QfRiSE6B+Yqg+XaQGdn0K866C8VuOm9AlpwDumwmfbISfDYeJA4I6SR7aAQuuhmHPu4Vaqmv1X2DZ3TD4Seh8fejOQ0RK1Xo98WHW2oUhDSmMvEjiLyxzC5Kc0xHunw1Zh+GJ82HhNvhqJ9w82FVrFvogLblmn71iD7y1Bn51unqlSzUd3OKq1XfNhNxV0P3WY3Z/sB6+2AG/PqOC4YpZX0Pjfq7EXR2bX4aF10PXydD/Ua1FLhJaFV75K/2fZoz5qbX2ceAMY8wZ5R1jrX2klsHVCdf0O/r6npFwMFAj6bNHe5Mv2Aa3fQjLJkJeEfx5Adw5HBolu9J7ekr5SfrlFa4qVAlcqq20Xdx3yD3ADSVLagwmjvO7wPldXAK/4T3olwk3Dwq0lZcchjkXQ6uxrsq8Oom8w1XQsAvM/Z67aTjtVUjOCNvpiYhTVXX6v6y1k40xz1Z0jLX2R2GJrBaitWNbsc+1bbdJhV358IfP4c9jXJI/5R/w1vehXwv4xxeu7bt3c9idD6NegL+OdW3iIidsziWudN33t8dsnrEJ7p0JGSnw8Dnub5Ccla73eesL3Wxt1S1ZH9rhEnnBHjjtFWhadt0kkSr4fVCS5zptFudAUhPXvJOzCg6sBV8h+Asgvh60/wEUZsGO6YBxN5xxidD8TEhpBrlrXOknoaHrM5IQswtv1q46PdZEaxKviLWwNQ8yG7i29F/PhktPgVNbwLn/g20HYMUk+HA9FPndvrzAMOHUGlbNy0msYK/r9FYvE7551A0TS3VLoeYVupvK11bBpz+ETo1xF81Z50Lby2DQE9X/Hl8hLL0b1v0D+j4IPX5e/Wp5qZuK89wSuL7Dbn6CAxtg3b+gYJebibBwH4yc4v4e32wBBbuP/mzPe+DUh2D5/W4a4vhkiEuG+q3h7E/cGgBzL3UXUusHWwynvQZNh8K0UyAvaLHNLhNhyJOw8QX47lWX6Ou3gQYdocuN7gbCFld78aEIOrEkXlEVejBr7WcnGFTYxFoSr8yUVbDvEEweDFNXu6FCV/aGp79y7Zpvfh/W7IOXVsDvRrnS/pY86JAG8bpuSnn8xTD/auj9K0jvDcX5kNgQgM05bh6CzTmwZAdc1u47jC2BRp3dULSatHVvew8WXgeN+8PQZ6Bhh7CcjkSBkkNwYL1b5vbgZkjrDS3OhhW/hW8fh6LAcIj0vnDBMjdJ0YoHIKWFW443uZmb+yC5CeSscKXsxPSjoy9OlN/nmpNK8qEoxyXnhh1h73zY9YmrMTq0DUoOwjmfuhvX6b2hXmu3+E9aT+h+m/uZkoNeluRPOIkHL6XQGSjCTcPaHEgC1ltre4YoyJCpS0m8IkU+19O4eQOXxN9dC78YAeuz4Jz/wcpJ0DAJbp7utndsDN/sdZ3vatqxTuqw/E3wwalw4SpXIgn4dCP89CPo0dTdHJ7in+sWQBn5OjQ+tfqff2ibG0++Zw70+wN0+z+VymOV9bvfZ963kLfGPfe4wyW4t9u6fSmZ0KADdPmxW8gn6yuXKBt2dH9f0V6d7SuEvNXuhiR3NeR9A31/B426wFut3Q1FxmBoMgRaXQDpvSIVWa17pz8MFAAPWWuLjDFJwD1AirU26hYZritJ/N9fQWoSXNG7Zj+XW+gSdWEJ/HUx/Kifm0971PPw8xFwYVf43WfQrQn8oJfr/V7ih/4twnMeEsWshX0LodlwOLQdNv3PVX/HJbD7IPx+LkxbC3cOLWSyuQ02PQ+Dn4JO19TsOza/BF/e5qaOHfwvSO8TvnOS2rHWVWfnrICc5S45D3zMvZ/eFxIaQWp39+j1S0g7BfI3ulJ1Qn2vow+fvG/dyoL7F8P+L6DjD6HrJFj1BzfbYeYo1xYfng6dtU7i2UAza21J0LYEYK+1tnFIQgyhupDE/RZGPgu3DnHV56FgrestnxDnSlpN67t2978vhv2H4f4zYco38N5aeGE87DgA09bBjwMLVh0u0dj3Om3vAteOPfx/rvoRA4kNWbLD/UcZ3Ao2LHmWTusnY059GE75ac0+//Bu+Op22PK6a5vs8xtIaRqOM5HqKjnsSps5K1znr3aXw5q/wlc/de/T+7gq8EH/AKwrVddrqaEywTa/ClvfgD2zXSe7Lj+GIU+F+ltqncRXAo9Ya18I2vZD4G5rbcTqE6qrLiTxRdvh6rdgyY8jW/29Pc91pBva2pXQ/7YInr7Y9aYf+h9Xo9qiIdwzw61p3q0JrNjttjWL8poyqYFVD8Ouj+GcmUc2lfhh7EvQuvgrrhyQzrl9OxF/aHPN27r3znOl8gMbXLt818mQUC+k4Us5Sg5DzjJX5d2wIyy8wdWsYKFRV7ewTd/fukRdchAatFfTR01Yvxte6SuAJoND/em1n+wFeAM4iJuGtS3QALg8GieBqQtJ/J4ZbsKYpy7yOhKn2Acbs6FrE/fX9OBcuLYftEuDy16HCT1dtf/vPoOMem4FrS93uuR/YVc30Y3fqiQfM/zFrhq1YUf49u9gfXDKbRSUwHPL4J9fQP/6a3gusQ+mx50uGdckEVs/bHoBlv8abAn0vNf1Do6+XsGxyVcAJtEl4cU3uSrg3FWAgYF/hW43u5qXuATXCU03UdGu9kPMAlOwjgBaALuABdbaakyyHHl1IYk/vww6N4aR7ao+Nhr4rVs568udkBQHfTLhzdWwZj/cOxLeX+faV+df76rp75sJ/7wA6iXCm2tgVHtoXM+14ydrsq/osn26S+JtLoadH0O9VuTV683LK+D6ljNIWHIjBSWGlCGPE9fm4ppVtfoK3bChVb93Caf7T6Hzja5XslRfYRZseS3QZrvEJexz50HTIbDsPteprPFAaNxXN0qxSePEK1D3Tj5KFZS4WefapbnnF5fD7cOg2A/nvwxPXghdM+DsF+AnQ2D8KfDkEmjVCMZ1dz3wD5e4znfFPnfDoCF0HlhyKzTq5qZxzV0NDdrz5U7Dwk/+SP/kr9nW720u6eYnOSm+6s8K5itwyXzN427scOcboPtPoKFmODqGr9C1X2ctcQn7wDq38tyhrW6WvYyBkDHIPTc+1Y2plrpASbwCUXnyD86FYa1h9El4/Vqf5Trcpae4cfFN67vZ655cAptz3Yxib62BJxbDzGtgSy78chY8czEkxrmq3nHdoEl9177fpL5bxUtCqHSy9Rlnu2E2PX5OTm42L6xO4/nlcfy+/nWM6VBIXN8HXA/mmvD7YMc0WPMY7PkMWoyGTtdD2/EnXwnSX+xmzstaAibeLS6z7klYcosbv1yarLvcVLux1BILlMQrEHUnn1cIg56G58fD8DZVH38yyi10pfmuGW5a2infwC1D4HAxXPUW/PU8V+If+SzcdRpc3M1V33fJcJ3xPtrgJtD5f31gWx6s3Q9nd3Ql/JwCd+OgzrfV4C92j4T68OEg6HQ9BZ1uZvO6eZyy+2HsjumsSv4eyf3uo2uX/jX//OzlsPFZ2Pw/l9zbXwHtvw/NTq97C6z4S9z45IQGrvbhq5/D2r+Dv8gNy2szHvr93k3MY+Lq9lAuKY+SeAWi7uRfXwV/WejajuOUSGrlQKFb0CMlwS0+k5YMPZvB9HUuiV/Tzy3J+fwyePF7bjKc81+GdbdAvIHR/3MdC7s1cetwn93RDbOaucktWjO4lSvtHypxNxTFPvcHVdOa5Drh0A5XdZvcBD4aBp2uY5VvMIeXPsR/c65ga9oE7ugyn7NO7Xtkdrhq8xW50vmmF2DHh5DYyCW1tpe5sbmxVmXsK3J3iXGJ8PVdsHcuZC91U5L2/R30/qUbu+8vdrPd1fTfS7xlreuTkB6iscGOkngFou7kf/gWnNIU7jvd60hOPiV+V8Jv1ci9nrYWRnV0yf+Jxa5av28mPDrfzXN/TT83xn7pbled/8F6+M0cN3Jnex788G145wcu4d88HX461N0QPLvUrRo2oCUs3u7a9ge2dD35cwuge1PXwe9AkasVKP0vGjO1A/mbXIkypTl8OorcjDH8p+Aurt/aiXT2kNXscjan/5DefUaRlFjDEnVxvlvsYutU2PG+6+Xe/Axoca57pPeJvn+oohxXo5C9zA3xyl0FZ013TQXLfgmJaW4+8cYD1KEvVvmL3YRGbS5x08W+1wXOW+xuakMjepO4MWYicC1QDNxgrV0ftK8L8B8gEXjeWlutEfSRTuLrsuCVlW4SlYRAh6uEwCM+aFti3NF9pduDj0uMcxOtDG/jLuRJ8ZAceCQFHskJ7niJHqVNxPlFLhF3yXDNIp9udJ3yEuLgLwvc7HitU+Hhz2FEWzijvVuONineTerz7FJX1f/qZfD5Fpj8PqyY7GoNBj8Dn1/nfv6iV9xyt6e1dTcNvZvDZT1cH4LcAri+PyzdBfO3unXs9xx0f5+3DnFXguB+AzM2umGD7dJg1V5Xa9G5sTuPg8Xu9aFiF0O7NPD53c1FarKrKSp3LfJgRbmuZ3tyBsz/IVgfG3OgTc4bTCmayNI2T3BF5kIGdu9W85mufAVH58De+TFkf+0WtGg6wq2e1nS4azeOVNXzwa2we6arFs9Z5aYmvWg1lByAWWNdR7P0fu658amqEo91xXmw9F7o9yAkpsLHw2HwP10/BV8RxCeF8tuiM4kbYzKAD3BD1/oDd1lrJwTtfwP4A7AMmAecb63NqsbnRjSJr9oLT33pLnAlQQ+fDTz7XS9snx9K7LHHBW8r9Lk50Yt8R9cgL0+cCUrq5ST50tcpCVAvwT2XPuolQkp80OvgfeW8Lj0mOT76Cjh1WWEJZBe4SXSKfO5vrFcz93udsQn6NHfz5n+0AVo2dDUEMza6BDv+FFfCn7kJ7h7pFjO5fzY8d4n7m7x8Cjw2xs2nf+3bcFUfOK8z3D0DmteHnw13y+Eu2u5m7puxEe6a4SYe2pYHpz0LX/3Y3QT0/pcbWTCynfus0Z3gh33dTUuBzw0v/GC9i/Px82DdvhJ+93kC/+n2N9buLeDG9b9gSr3utGEtfyh6koGNNtK/VRKfpf2OZvUtZ7S3fLEzjn2H3PrnW3Jh+W64qJsb8fD1LhjU0jWbbNubRbP82SRnz8fuXYDJ/tKNQU/r5WYdK519LL0P1Gt14n/Q+xa7zmZ5a1zCrt8Whv0XvnsdVvwaUnu4jmfp/aD1JZCQ7G44inLdCnK+QsjfAKmnuPbt7GXQsLOrNs9Z6RYDqZfp5u82ca6NvGAfFOe6hWhKDrtpURt2cDURRTlumU1NzFJz1u/6I9gSd7Np/YANerbu3z13DTQZ6KaX/fwHcM4MiEuCr++A7j+D+q2O/Rmsa/ap1zJUkUZtEj8fONNae3fg/TJrbb+g/UfeG2P+CMyy1n5Yjc+Nyer0385x7a4j27kEX+RzF/Mi39EEH5zoi0rc++BtwccXlrhhWQWB5+D3BcGvi49976vgX+W4BJ/gbhqOS/rxR49NDjomeHtVD900eK+0lF3sc38bqcnu73LHAWjdyNUerdoL7VJdk8Hy3W6inzapbrY/n99N67t2vxt1cEFX2HkA3v4WJg9yf49/XgiTum8mI/9zfr64B+cU/oX2dhWXHFjKz1IfZlLCvUxp+CSri/txX8NfMLfVMzz5bRf+0eMlNscN4P/N7MnU0StISErhwmldeWTkHrqn5XPbvE6c1zaP8RmzeXZtGxoVb+En9R9kYW57VhT24dZGf2Jzwgg+KLmSK5rN4UBSN4obdKN70lpXSm51vlu4JXeV6xl+YJ0bWnfuHFg0Eba8Cs1HuVLXjunQ73euenzhDZDaDXre5ZbOzF4KZ74DW6bA17+ASza5ObinnQIT8tyF/tVkNzNes9Pgo+HQ6Vo3J/fiiWASYPA/3FSoW16HMfNg9yyYMw6+f8CND5/aBMZtdBPzvNsVBj7u1oFfPNG1qXedBOufcYm/931uaNquT12MhVmw/V3ocLXrLLhvsWvLTajvViaLT6nZzYHfB/5Cd9PiLwys/R14H/y67L6aHOsrdB3+ShPvkSRc5r2/NDGXvg9+HdgXzhTQ8RoY/nyoPi1qk/hVQDtr7cOB98uttX2D9h95b4y5G9hirX25zGc8ANxf9rNjLYnnFsCAp+H1y137qJdKL9oFZR5HbgjKJP2yNwtlf66w7Hvfse8r+iUkxwfdAFRyI1DeMYmB2ojEOPc6Md5NQpMY2FZaWxH8vqJjE+Njv5OhtUdrh3xBz8V+9/suDtw0Hnnvc2vXH7MvaH9R0HHBP1t63DGfFfzsh5Kg18U+F1fp/hL/0ZvTAfGzuSBpCg3j8ugQv56G5gBX537M0MTPeLzR1eT4M9jlb0PTuN28W3gFvz/4J95KG06L+B08d/hWeiQsY1TSBwzM2s+E5Gd4uNGP+efBu9jlb82vGt7BLw/8k3nFZ/NZRhcO2obs82fSMSHQmpfQwF30TZybkjQhFYr2u2FeCY1g96fQ/kqXOHfNdJOqpPZw1egpTV1J/NB211mtUReXEIsPuBK29btkFF/P3SWVfk9ld62lJcb4JNf+WpLv2l59he4GI6U54HezsNVv5eLf+znEN4AG7WDfIijOcTcd2csg6wtoPQ4OboFNz0LnSeA/DKv/5OZPT2jk5gNv1M0tbJKzzN1QpDRzc+D7Drq51Uvy3RStJs7VEOCv5K/QBNYCTzm6Jnh8srtRKH1d0b64wOvSfXFJ7qbDJBx9NvFB74NeE+9+dynNIL4+7JrhZqlr1AnW/9tNhNNugpuj4NBWGPR3t/DL/iXQ9aZACTsO4uLdszHuXEyce8a4bce8j3MdF0M3LDJqk/j5wBmlK6EZY5Zaa08N2n/kfWAltdl1tST+xjfw6AJYcJL1SrfWXfALfOUn/ONuCHwVbC85+hkFJUHJppxEUzZJVVTzEMzgSp7x5uhEM3Hm6PvSbUf2lz0msO/IeQedf9ltlLMt+Dg/gURcTlIOfi7xu2aZEn/N/tANx97gHHNTU95NT0U3RaXHBI5LKPM6qcy2xLgy2wPb4g1kFcDWXPguF3Jy93F79xU0Mvt5dNZ+6tv9PH3oDkpI4P3Ok+iZUcDyXcXszCvho+Lv807B97k14yl+2nkhOw434p1N6eQndWRlg+vo3nAvkzsvpXHjTIoTW5CYmOjmDi/Y6Xrclz4X7XfV1kXZ7lEceF1ysPx/xLhkl6QT6gUSTBwuAcQHEnaZZ+sPlBp9Fb8m8NpffLQ0WtVvNi7xaNKLL01+ZRJn8GuTFEiWKe4GJCnddbwr2OVuCBp1hvzNUJLnah7yN7tSfa973L/HklvhjLdch65pPWHkFMg8C2aOho7XuuleV/zG/Vv0+RVsfcvNBzDwMTd50JrHYOi/3b/rsvvcdL7JTWD5A9DuMtcUsv7fbrnTlmPc4iOFe93kQ7s+dZ3Lhj3rmiVmjILv7XL/Rq8mwtgvIWMAfH4FdLoOWo2Fbe9AUgY0P939m8YlRWsVYIVBeT3YchHwa2NMPNAPWFdm/zpjzABcm/iZwCMRji9ipq93bX4nUwIH9/+ltMrdq3XOy5ZIy5Y0iwKJ3mfB7z/+tT/wKE2gR16XOcbnP/b6UPqyvGtG8KbS/cHHl94wJARuFIKf44P2l30uPa50W3m1D/EmWq9jpZoCowC4v5PrgHd1oetM2Lje09AA6mVBg3y4FLg6ERqnTITGE2lpYfKo4M9qDowBXO9ZwHWwSzuleqH4i13C8R12j5LDR1/7DrsS93EJuWxi9h9N6EcSfXxQyS8+6BEXSMRlk3Lw66SjiTsSv8iedx593fZ7R19PyDlaGh72rEuWAJlnH62ir9/GVfmDO7+kdPfa+qFwf+DfCHcT5Q/M8l2UA8mBm6eEBuAvcK9TWrpOjeAWbxn+P8C67790LyQFFtwc+erRGNtccvR1rA1VDIiG3umTgGsI9E4HugBp1trXYqV3eig8Mh/GdHJtiCIiIkGiszo9XGIxiYuIiFSgwnymMQlR4E8L3IxiIiIiNaEk7rGCEnj6K9deKiIiUhNK4h6bt8V1KBra2utIREQk1iiJe+yTTXBWB9crWEREpCa8HmJ20mtWH0Z39DoKERGJReqdLiIiEt3UOz0avbnGLS4hIiJyIpTEPfTUEtiS53UUIiISq5TEPbIlF9bsh3PUHi4iIidISdwjMzZB9ybQLs3rSEREJFYpiXukyAeX9vA6ChERiWXqnS4iIhLd1Ds9mizbBe+XXXRVRESkhpTEPfD6N/DBeq+jEBGRWKckHmHWwuzNcGZ7ryMREZFYpyQeYRuyYdsBOENJXEREaklJPMI257gVyzIbeB2JiIjEOvVO94C1YKoTnYiIiHqnR4fDxa5XerHf60hERKQuUBKPoAXb4K5PK7mlEhERqQEl8Qia8x2c1hYS472ORERE6gIl8Qia8x2c1cHrKEREpK5QEo+QfYdgxwE4vZ3XkYiISF2h3ukRVFACKQmR/EYREakD1Dvda6v3gr/u3S+JiIiHlMQjwFq4+i2YtdnrSEREpC5REo+Ab/fD/sMwvI3XkYiISF2iJB4B87ZCz2aQUc/rSEREpC5REo+AeVvd+HAREZFQUu/0CMgthKISaKZFT0REpOYqzHtXAssAABbESURBVGdK4mF2sAiS4jVLm4iInDANMfPKM1/D9e96HYWIiNRFnk49YozpAvwHSASet9Y+VWb/BODnQCGwA7jGWlsU8UBrQe3htWetZdeuXRw+fBijNVxFpA6y1lKvXj1atGhRo+ucp9Xpxpg3gD8Ay4B5wPnW2qyg/R2BrdbaEmPMw8BKa+2L1fjcqKhOP1QMfZ+EVy+DQa3C+U11W3Z2NoWFhWRmZiqJi0idZK1l9+7dJCcn07hx47K7o7Y6vau19ktrbQkwGxgSvNNauymwD6AE8EU4vlr5aqdrC++X6XUksS0nJ4fmzZsrgYtInWWMoXnz5uTk5NTo57xO4sFX5Wwgo9yDjDkFGAO8Wc6+B4wxNvgRnlBrrmczePqi/9/evUdHVd0LHP9ukkB4JhotDyNJiRERCBAhyjvBLkRMEAVEuSmiwqXW6pLHteADUq3KVVxQK16rFXBB6mOBIMJSqG0CFKwUJNVSqgKJGpL4ipAQEsjjd/84ZyZDyIQJzDBzwu+z1lk5Z8957N8+Z/bOeczZ+lCbP7RqFexDVSmlAuts6rmA3xM3xnQF1jXy0VNAncd0NJDXyPLdgNeAO0TkRMPPRSQLyGqwTEg05BdFwjDttUwppVSABPz0RkSKReS6RoYNwBfGmGRjTBgwEtjluawxJhpYA9wnIgcDnVd/qqqBIcvhQOmZ51VKKeVfqamp5ObmntM6nHALL9jXKOcDS4DtwErXQ23GmBX25/OAeGCxMSbXGDMtGJk8G3kl1vvSL+8U7Jyolq66ujooy15ItJxCj+4TS1AbcRE5ICIjRWSI58/LROQu++88EekmIqn2sDJomW2mjw7DgK7QRvsPbzGWLl3KY489BsCKFSto27YtVVVVAHTv3p29e/cydOhQkpOT6devH++//7572S1bttCrVy+Sk5OZP38+8fHxABQUFBAXF8fs2bPp168fAwYM4MCBA4D1tGpWVhYpKSn079+fmTNnUlNjPecZHx/PE088wciRI1m0aNFpeU1NTeXBBx9kxIgRJCYmsnp1/Y86jDE8++yzDBs2jFWrVp12xhIfH09BQQF1dXXMnDmT3r17k5SURGZmJgA7duzgmmuuoX///vTp04edO3f6r5BDSMNyKiwsZMyYMSQlJTFw4EB27NgB1O/DBx980P3Zp59+ys0330zPnj2588473ev0tl+OHz/OLbfcQlJSEn379uXXv/51UGIOJGMMTz/9NMnJyfTq1YuPP/6YzMxMevfuzdixYzlxwrpb6u14dI17HvdZWVlMmTKF0aNHc9VVVzFr1iw8f3H1/vvvM2TIEHr06MHLL78MwG9/+1sWLFjgnmfz5s2MGTOmybxXVVVxyy238PjjjwOwb98+hg8fTlJSEsOHD2f//v3ueRcsWECfPn3o06cPWVlZ7vSmvpPnRERa3GCF5dO8AXP7GpHndgZyCxeOQ4cOucdrakVKygM31NR6z0deXp4MHTpURESmTp0qKSkpkpOTI59//rkMGDBAysrK5OTJkyIi8tVXX0n37t2lrq5OKisrpWvXrvLJJ5+IiMicOXMkLi5ORETy8/MFkJycHBERWbRokcyYMUNERFasWCFz5syRuro6ERGZOXOmvPTSSyIiEhcXJ/PmzfOa15EjR8rkyZOlrq5OCgsLpXPnzlJcXCwiIoB7Pa55Xdt3rTs/P1/27Nkjo0aNcqeXlpaKiEhGRoZs27ZNRESqq6ulrKzMe6Gdo/ITDfbRMSu9upHjoLLa+uz7ilPTf6y00itOWtPlJ3zbdsNyGj9+vCxevFhERHbt2iWxsbFSVVXl3ofbt28XEZH77rtPEhIS5LvvvpOamhpJSkqSHTt2iIj3/bJ27Vq5++673dtylbW/lZ8QKT1ujVdWi3zboDxrrUNNSo6JVHmUZ4V1WMuRKmsQsdK+r/B924CsXr1aRESeffZZiYmJkc8//1xERG666SbJzs4WEe/Ho2vc87hfuHChXHHFFXL06FE5ceKEDB48WNatW+dez1133SV1dXXy9ddfS1RUlFRXV0txcbHExcVJdbUV4K233ipr1671muejR4/KqFGjZNmyZe70/v37y5o1a0REZM2aNZKcnCwiIuvXr5dBgwZJZWWlVFZWyqBBg2TDhg3u/Hj7TnryrO88s+Jt0PPEALm5p/60LBC+Pw4prwZu/bvugc4dGv8sKSmJgwcPUlFRQV5eHg8//DA5OTnExsaSlpZGeXk599xzD/v27SMiIoLi4mK++eYbSkpK6NKlC3379gVg6tSprFmzxr3ezp07k5qaCsDAgQP585//DMCmTZvYu3cvH3zwAQCVlZV07NjRvZzrzNibKVOmYIzhsssuY/DgwXz00UfcfPPNPi0L0KNHDwoLC/nlL3/J9ddfz9ixYwEYOXIks2fPZvLkyYwdO5arr776jOs6W698DEs/qp/u2Br+dS8UlsHI106d99UM+FkPuOdd2FtSn56eCMvGwlv/hoW58OC1MOs637bvWU65ubmsWGHd6Rs0aBAxMTF89tlndOrUiS5dujBs2DAABgwYwA8//MAll1wCWMfNoUOHGDJkCND4funXrx9z585l7ty5jBo1itGjRzevoHz0ysfw6bewfBzkFMAT22Dn3fXl+ekvoFMbGLESVo2HlMus8hx/FUzrB0//zVrPouut8lz/H1g/2fftT5w4EbDKKDExkcTERPf0oUOHfFpHw2M3IyODTp2s+5aTJ09m69atjB8/HoDbbrsNYwyxsbF07NiRkpISYmNjSUlJYePGjQwePJjdu3fz5ptvet1eamoqs2fPdm+3rKyMgoICJkyYAMCECROYPn06ZWVl5ObmMmXKFCIjIwFrX+fk5JCRkeGe9vadPFvaiAfI7X2CnYOW6ZJ2VkMbyPV7Y4xh8ODBZGdnExcXx/XXX8+LL75IbGwst99+O48++ih9+vThzTffxBhDTEyM+3K75wMy0uAFS64vPEBYWJj7krmIsGjRInfF11D79vU96gwcOJCamhp69+5Ndnb2adtsOO25bHh4OHV19T8UcV3WjI6OJi8vj7/85S9s3LiRhQsX8s9//pM5c+aQnp7Oli1bmDhxIo8++ihTpkzxXnDnYEYyTPH8LtkhxHY6/TiIsovx1Qyo8fjdi+uW1m1Xw40J0L6179v3LCdjjNcybdOmjTstLCzstGnXPvVcxnM6ISGBvXv3smXLFl577TV+97vfsXnzZt8z6qMZyVBtv20jLR4GdrXGXeXZwS6bbdMg2g7h1QxoG2GNzx9Wv67broaMxOZt31UuTZWRt+PRxXOfQNPHubfv1r333sszzzzD/v37yczMJDw8nLy8PKZNmwbAnXfeyaxZswDrn9ZNmzZxxx13EBYWhog0+sCbK62p/DT12dkK9oNtLdLr/4KX9gQ7Fy1TWCvrTDlQQ9gZvhFpaWk8+eSTpKWlcckll1BRUUFubi4jRozgyJEjXH755RhjWLNmDaWl1k8TrrrqKoqKiti3bx+Au5E9k/T0dJYtW0ZFRQUApaWl5OfnNzrv7t27ycvLO2Xd2dnZiAiHDx/mww8/JCUlpdFle/TowT/+8Q8Atm3bRkmJdRr73XffceLECdLT01myZAmHDx+mvLycL774gp49e3L//feTmZnpXjYQOrRusI/s+ju8keMg0m6sY9qdmh5t1+PtIqzpDs1oxD2lpqa6z8T37NlDaWkpV155ZbPX09h+KSwsJCIigkmTJrFkyRJ27dp15hWdhQ6t4aK21nhkeH3Piq7ybGW3KZ3b1//zE9POKjuAqDbWAFZaTBP/9J4tb8ejNxs2bKCsrIyTJ0/y1ltvMWLEiDNuIy0tjS+//JIXXniB6dOnA9C/f3/y8vLIy8tzN+AAS5YsoXPnzmRmZlJbW0tUVBTx8fGsX78egHXr1pGQkEDHjh1JS0vj9ddfp6qqiqqqKt544w3S0tLc6/L1O9kceiYeAO8dgD4/CXYuVCCkpaXxwAMPuL+YQ4cOZceOHURFRTF//nymTp3Kyy+/TEpKCt27Wy8JiIyMZPny5UyYMIGYmBhSU1OJioo647amTZtGUVER1113HcYYIiIiWLp0KT/96U99ymuPHj0YMWIEJSUlLF68mC5dujQ630MPPcSkSZN4++23GTJkiDvfX3/9NdOnT6e2tpba2loee+wxoqOjeeSRR8jNzaV169ZER0ezcuVKn/LjdM8//zzTp09n+fLltG7dmtdff/2Us0lfNbZf3nvvPebNm4cxBhHhhRdeCEAEzuDtePRm+PDhTJo0iYKCAsaOHevz5emf//znbN261afv09KlS5k1axaZmZmsXr2aVatWMXPmTBYsWEBUVBSrVq0CYNy4cezevZuBAwcC1u2D9PR093p8/U42S1M3zJ06EMQH26prRXotE9laEIi1X5i8POjhKOXl5e7xJ598UqZPnx7Q7TV8OEiFBt0v/rVw4UJZuHDhWS2bnp4u77zzjn8z1ARf931zH2zTy+l+tu9b60Uv13QNdk5UKMnOzmbAgAH07t2b7du3u3+qopQ6vw4ePEhiYiIRERHuB86cLKi9mAVKMHsxyyuBt/8Dj6f6e80Xrvz8fJ8vISullJN5qe+8tmd6T9zP+nexBqWUUirQ9HK6H4nAcx9CcXmwc6KUUupCoI24HxWWw/OB+WWIUkopdRptxP1oTxF06wBdO555XqWUUupcaSPuR7uLYWC3YOdCKaWUdkWqmq2yGq6LDXYu1IVGuyINPC2n0KP7xKKNuB89Nxr+q2+wc6ECRbsi1a5ItSvSs6ddkWpXpCH9xraCH0V2FfpzjcrllDcY1daIVBwO3FBb4zUf2hXp+e+KVE6Wnbp/jhdZ6bXVp++7GrvP0cpvT02v+sFKrz5mTZ/0Lb8Ny6kldEUqJ8tEqr63xquPixwvscZd5Vln98V7vEikxu5ztPJbq+xERE78aA0iVlrltz5vGu2KVLsiDWXr/gO7iuBPtwY7Jy1c1Tew/rLArX/8YWjX+IMN2hXp+e+KlP3Pwb9+Uz8d0QkmHYWKAni3QRdaIzZAbAZszYAfPPov7X4bDHsTDq6APfdDn4WQlOXT5ltaV6Tsfw5K90Dqu1D8HuyZBeO/rC/PiUegdRRsSIC0zfCT4VZ5xmdCz1/B3oes9Vz7slWeBavhhr/7vHntilS7Ig1Zu4vru/VTARTZ2WpoA7l+L7Qr0vPfFSm95sAV/+0ZhB1A/OnHQZuLrb8j34U6j/ulYXb5JtwFl98KEb7/fKSldUVKrzlQd9Ia73oj3DDUGneVp6tsxh2E1h7lGW53Vzbgmfp1JdwFcc3oTBztilS7Ig1RtXWwtwSu0SfTA69VmHWmHKihVViTm9euSM9vV6REdDx1/7S1/1NuFX76vnM11pGXnpruatzD21vTzWjEPbWErkiJ6AhtYqzx8LbQ1v6n1VWexm4S2naFMLuRjbzUKjuA1tHWAFZa5KV+z6J2Rdo8eibuBwd/hOPVMEBft9riaVek2hWpdkUaWNoVafNoByh+crQKoiLPPJ9qvpbQAcqxY8fo0KEDAE899RT5+fm88sorAdteamoqWVlZ7nvtKjTofvEv19Pfnk+B+yojI4MZM2Ywbtw4/2bKC1/3fXM7QNHL6X6iDbhqinZFqlRo0K5IHSCYXZEq/2sJZ+JKKeULPRNXSimlLhDaiCullFIOpY24CnkRERGUlZUFOxtKKRVQZWVlRERENGsZvSeuQl5dXR1FRUXa4YFSqkWLiIigW7dutGp12vm11/ZMG3GllFIqtOmDbUoppVRLo424Ukop5VDaiCullFIOpY24Ukop5VAXegco594PnFJKKRUkLfLpdKWUUupCoJfTlVJKKYfSRlwppZRyKG3ElVJKKYfSRlwppZRyKG3ElVJKKYfSRlwppZRyKG3ElVJKKYfSRlwppZRyqAv6jW3GGH3TjVJKqZDnrXttfWObnzSjD/OQ15JiAY0n1Gk8oU3jCW16OV0ppZRyKG3ElVJKKYfSRlwppZRyKG3E/ec3wc6AH7WkWEDjCXUaT2jTeEKYPtimlFJKOZSeiSullFIOpY24j4wxbYwxO40xR4wxE+20eGPMdmPMVmPMZmNMtJ1+rT3vh8aYccHNeeOMMb2MMX8zxmwzxuQYY3oYY9oZY96yY/qDMaaVPa9T47nfGLPLGLPDGPN7j3lDOp7GYvH47I/GmI0e0+PsOHYaY64NTo6b5i0eY8yjxpgP7PS+dpoj43F4XdDNGPORnfedxpgkh9cFjcXjyLrAJyKigw8D1j88XYEsYKKd9iQw1R6fCzxgj+8AugEdgI+B8GDnv5F4LgWi7fExwB+BXwFz7LQXgJscHs8V1N8yegMY5oR4GovFHu8JvANstKfDgb12HN2AHcHOezP2TTrwSIP5nByPk+uCMKCVPT4KWO3wuqCxeBxZF/gy6Jm4j0SkTkSKGyR/CkTb49HAt8aYSCBMRIpE5BjwBdYBFFJE5DsROWJP1gC1wHBgk522CRju5HhE5IDY31RXmhPi8bJvAB4D/tdj1kTgMxE5JiJFQJgdX0jxEs9E4GJjzF+NMS8aY1rj7HicXBfUikidPdkJ6x8pJ9cFp8Xj1LrAF9qIn5udwC+MMZ8CNwJvAxcDRzzm+dFOC0nGmHbA48BS4CLq8+7Kt5PjcaWNAH4iIh/ioHg8YzHGDAKKgSKPWTz3F/Z4SMYCp+2brkCViIwCSoG7cXY8jq4LjDFXG2N2Ar8HtuPwuqCReFzpjqwLmqKN+LlZBMwVkb7Aq8D/YFVIUR7zRNtpIccYE451aekZEdmPdRC78u7Kt5PjwRjTG+vsdYo9myPiaSSWhzn1LBxO3V8QorGA12Nti/3x+0ASzo7H0XWBiPxbRIYAGVgNn6PrgkbicWxdcCbaiJ+bVsD39vgPwEUiUoV1qaarMaY9cCVwIFgZ9MYYY7Aqm/dEZL2dvA24yR4fC2x3cjzGmO7ASmCKiHwP4IR4vOybBKx7e68BKcaYOViX/3oaY9obY7oCNXZ8IcVLPFuBa+zxgVixODkeJ9cFbTwmfwSO4+y64LR4nFoX+CTYN+WdNABrgUPAJ8BzQF/gb0CuPXS357sW6/La34Fxwc63l1jGYH1ZXXlfCrQD3sK6/PQH6h8OcWo8b9j7y5U2xgnxNBaLx2fx2A+22dPjgA/teK4Ndt6bsW8igWwgB1gHtHd4PE6uC4Zh/VOVY+c92eF1QWPxOLIu8GXQl70opZRSDqWX05VSSimH0kZcKaWUcihtxJVSSimH0kZcKaWUcihtxJVSSimH0kZcKaWUcihtxJVSXhljjnkMYoyp8Jju7mWZecaYRxpJX2mMmecxfaMxptQYMzKQMSjVkoUHOwNKqdAlIh1c48YYARJEpOQMi92A9dpRr4wxY7Be9jJBRLaec0aVukDpmbhSym+MMR2wXl/5cRPzjAb+hNWlb875yptSLZE24kopf0oDtkp9V5ANpWK9AnOSiPz1vOVKqRZKG3GllD+NATY38flQoADr/ehKqXOkjbhSyp9G03Qjvhiru8e1xpiI85MlpVoubcSVUn5hjEkAKs7w4NsJYDxwEbDKGKN1kFLnQL9ASil/uYGmz8IBEJFjWH1U9wb+L9CZUqol00ZcKeUvZ7of7iYipViX3n9mjFkU0Fwp1YJpf+JKqXNm39/+CogTkZPBzo9SFwo9E1dK+cPFwCPagCt1fumZuFJKKeVQeiaulFJKOZQ24koppZRDaSOulFJKOZQ24koppZRDaSOulFJKOZQ24koppZRD/T/klL9w4FrZLwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 504x625.763 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "sia_dir = '/Users/m219063/Library/SIA_v4_0_1/calculated-values/'\n",
+ "d0 = pd.read_csv(sia_dir + 'psat.dat',sep=' ',names=['T','Psat_liq','Psat_ice'])\n",
+ "x0 = d0.to_xarray()\n",
+ "d1 = pd.read_csv(sia_dir + 'cp-liq-vap.dat',sep=' ',names=['T','liq_density','vap_density','cp_liq','cp_vap','lv'])\n",
+ "x1 = d1.to_xarray()\n",
+ "d2 = pd.read_csv(sia_dir + 'cp-ice-vap.dat',sep=' ',names=['T','liq_density','vap_density','cp_ice','cp_vap','ls'])\n",
+ "x2 = d2.to_xarray()\n",
+ "\n",
+ "fig = plt.figure(figsize=(7,14/1.610834))\n",
+ "\n",
+ "ax1 = plt.subplot(2,1,2)\n",
+ "ax1.set_ylabel('difference / %')\n",
+ "ax1.set_xlabel('T / K')\n",
+ "\n",
+ "formula = 'wagner-pruss'\n",
+ "es_r = es(x0.T,formula=formula,state='ice')\n",
+ "diff = es_r/x0.Psat_ice\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',label=formula)\n",
+ "es_r = es(x0.T,formula=formula,state='liq')\n",
+ "diff = es_r/x0.Psat_liq\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',label=formula)\n",
+ "\n",
+ "formula = 'romps'\n",
+ "es_r = es(x0.T,formula=formula,state='ice')\n",
+ "diff = es_r/x0.Psat_ice\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',ls='dashed',label=formula)\n",
+ "es_r = es(x0.T,formula=formula,state='liq')\n",
+ "diff = es_r/x0.Psat_liq\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',ls='dashed',label=formula)\n",
+ "\n",
+ "formula = 'murphy-koop'\n",
+ "es_r = es(x0.T,formula=formula,state='ice')\n",
+ "diff = es_r/x0.Psat_ice\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',ls='dotted',label=formula)\n",
+ "es_r = es(x0.T,formula=formula,state='liq')\n",
+ "diff = es_r/x0.Psat_liq\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',ls='dotted',label=formula)\n",
+ "plt.legend(loc=\"best\",ncol=3)\n",
+ "\n",
+ "###\n",
+ "ax2 = plt.subplot(2,1,1)\n",
+ "ax2.set_ylabel('difference / %')\n",
+ "ax2.set_xlabel('T / K')\n",
+ "ax2.set_ylim(0.8,1.2)\n",
+ "\n",
+ "plt.plot(x1.T,x1.cp_vap/cpv,c='green',ls='solid',label='$c_{p,\\mathrm{vap}}$ (vap-liq)')\n",
+ "plt.plot(x2.T,x2.cp_vap/cpv,c='green',ls='dashed',label='$c_{p,\\mathrm{vap}}$ (vap-ice)')\n",
+ "\n",
+ "plt.plot(x1.T,x1.cp_liq/cl,c='orange',ls='solid',label='$c_{p,\\mathrm{liq}}$')\n",
+ "plt.plot(x2.T,x2.cp_ice/ci,c='dodgerblue',ls='solid',label='$c_{p,\\mathrm{ice}}$')\n",
+ "\n",
+ "lvx = phase_change_enthalpy(x1.T,fusion=False)\n",
+ "lsx = phase_change_enthalpy(x2.T,fusion=True) + phase_change_enthalpy(x2.T,fusion=False)\n",
+ "\n",
+ "plt.plot(x1.T,x1.lv/lvx,c='gray',ls='solid',label='$\\\\ell_v$')\n",
+ "plt.plot(x2.T,x2.ls/lsx,c='gray',ls='dashed',label='$\\\\ell_s$')\n",
+ "\n",
+ "plt.legend(loc=\"lower right\",ncol=3)\n",
+ "sns.set_context(\"paper\")\n",
+ "sns.despine(offset=10)\n",
+ "plt.tight_layout()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Comparing formulations of saturation vapor pressure ##\n",
+ "\n",
+ "This comparison of relative error suggests that the Wagner-Pru{\\ss}, Murphy and Koop, Hardy, and Sonntag formulations lie closest to the IAPWS-97 reference. Romps (2017) and Bolton (1980) are similarly accurate and may have advantages. Hardy is interesting as it appears in a technical document and is rarely mentioned in the subsequent literature, but used by Vaisala in the calibration of their sondes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Temperatures above the triple point ###"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 136,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "state = 'liq'\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_xlabel('$T$ / K')\n",
+ "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "es_ref = es_iapws\n",
+ "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "es_r = es(TK,formula='romps',state=state)\n",
+ "es_g = es(TK,formula='goff-gratch',state=state)\n",
+ "es_m = es(TK,formula='murphy-koop',state=state)\n",
+ "es_s = es(TK,formula='sonntag',state=state)\n",
+ "es_b = es(TK,formula='bolton',state=state)\n",
+ "es_f = es(TK,formula='flatau',state=state)\n",
+ "es_h = es(TK,formula='hardy98',state=state)\n",
+ "es_a = es(TK,formula='standard-analytic',state=state)\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_h/es_ref-1),c='tab:blue',ls='solid',label='Hardy (1998)')\n",
+ "plt.plot(TK,np.abs(es_f/es_ref-1),c='tab:orange',label='Flatau (1992)')\n",
+ "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(es_b/es_ref-1),c='tab:red',ls='dotted',label='Bolton (1980)')\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:brown',label='Wagner-Pruss (2002)')\n",
+ "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "plt.legend(loc=\"lower right\",ncol=3)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'es_l-error.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Extension to the freezing regime ###\n",
+ "\n",
+ "To extend over the entire temperature range a different reference is required, for this any of the Hardy, Sonntag, Murphy-Koop and Wagner-Pru{\\ss} formulations could suffice. We choose Wagner-Pru{\\ss} because Wagner's group is responsible for the standard, and has also developed the IAPWS standard for saturation vapor pressure over ice. Below the results are plooted with respect to this standard over a much larger temperature range.\n",
+ "\n",
+ "It is not clear how accurate Wagner and Pru{\\ss} wis hen extended well beyond the IAPWS range, based on which it might be that the grouping of errors of similar magnitude from the Bolton, Flatau and Goff-Gratch formulations are indicative of a low temperature bias in the Wagner-Pru{\\ss} formualtion. I doubt that this is the case, as the poor performance of all these formulations in the higher temperature range, and the simplicity of their formulation make it unlikely. The agreement of the Murphy-Koop formulation with these simpler formulations at low temperature may be indicative of Murphy and Koops focus on saturation over ice rather than liquid."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "state = 'liq'\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_xlabel('$T$ / K')\n",
+ "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "TK = np.arange(180,320,0.5)\n",
+ "\n",
+ "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "es_r = es(TK,formula='romps',state=state)\n",
+ "es_g = es(TK,formula='goff-gratch',state=state)\n",
+ "es_m = es(TK,formula='murphy-koop',state=state)\n",
+ "es_s = es(TK,formula='sonntag',state=state)\n",
+ "es_b = es(TK,formula='bolton',state=state)\n",
+ "es_f = es(TK,formula='flatau',state=state)\n",
+ "es_h = es(TK,formula='hardy98',state=state)\n",
+ "es_a = es(TK,formula='standard-analytic',state=state)\n",
+ "\n",
+ "es_ref = es_w\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_h/es_ref-1),c='tab:blue',ls='solid',label='Hardy (1998)')\n",
+ "plt.plot(TK,np.abs(es_f/es_ref-1),c='tab:orange',label='Flatau (1992)')\n",
+ "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(es_b/es_ref-1),c='tab:red',ls='dotted',label='Bolton (1980)')\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "#plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:olive',label='Wagner-Pruss (2002)')\n",
+ "\n",
+ "plt.legend(loc=\"lower left\",ncol=2)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'es_lsc-error.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sublimation vapor pressure ##\n",
+ "\n",
+ "A subset of the formulations also postulate forms for the saturation vapor pressure over ice. For the reference in this quantity we use Wagner et al., (2011) as this has been adopted as the IAPWS standard. Here is seems that Murphy and Koop's (2005) formulation behaves very well in comparision to Wagner et al., but Sonntag is also quite adequate, particularly at lower ($T<273.15$ K) temperatures where it is likely to be applied."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 138,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "state = 'ice'\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_xlabel('$T$ / K')\n",
+ "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "TK = np.arange(180,320,0.5)\n",
+ "\n",
+ "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "es_r = es(TK,formula='romps',state=state)\n",
+ "es_g = es(TK,formula='goff-gratch',state=state)\n",
+ "es_m = es(TK,formula='murphy-koop',state=state)\n",
+ "es_s = es(TK,formula='sonntag',state=state)\n",
+ "es_a = es(TK,formula='standard-analytic',state=state)\n",
+ "es_ref = es_w\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "#plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:olive',label='Wagner-Pruss (2002)')\n",
+ "\n",
+ "plt.legend(loc=\"lower left\",ncol=2)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'es_i-error.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Clausius Clapeyron ##\n",
+ "\n",
+ "Often over looked is that many conceptual models are built on the application of the Clausius-Clapeyron equation,\n",
+ "\\begin{equation} \n",
+ "\\frac{\\mathrm{d} \\ln e_\\mathrm{s}}{\\mathrm{d \\ln T}} \\left(\\frac{\\ell_\\mathrm{v}}{R_\\mathrm{v} T}\\right)^{-1} = 1 \n",
+ "\\end{equation}\n",
+ "with the assumption that the vaporization enthalpy, $\\ell_\\mathrm{v}$ that appears in this equation, is linear in temperature following Kirchoff's relation. This is similar to assuming that the specific heats are independent of temeprature, an idealization which is, unfortunately, just that, and idealization.\n",
+ "\n",
+ "But because of this it is interesting to compare this expression as given by the above formulation of the saturation vapor pressure (through their numerical derivative) and independent expressions of $\\ell_\\mathrm{v}$ based on the assumption of constant specific heats. \n",
+ "\n",
+ "This is shown below for ice and liquid saturation. The analytic expression, which has larger errors for es is constructued to satisfy this relationship and is exact to the precision of the numerical calculations. The various formulations using more accurate expressions for $e_s$ which implicityl don't assume constancy in specific heats are similarly accurate, with the exception of Goff-Gratch, and Romps for Ice. Hardy is only shown for water. For ice Sonntag does not behave well for $T> 290$ K, but it is not likely to be used at these temperatures. Note that Romps would be perfect had we adopted his modified specific heats.\n",
+ "\n",
+ "Based on the above my recommendation is to use the formulations by Wagner's group, unless one is interested in very low temperatures ($T<180$K) in which case the formulation of Koop and Murphy may be desirable. For just liquid processes Hardy might be a good choice, it is less well known but used by Vaisala for its sondes. There may be advantages to using Sonntag if there is interest in liquid and ice as it might allow more efficient implementations, but for my tests all formulations were within 30% of one another.\n",
+ "\n",
+ "Another alternative, would be to use the analytic approach, either using Romps' formulae if getthing the staturation vapor pressure as close to measurements as possible is preferred, or using the analytic formula with the correct (at the standard temperature and pressure) specific heats and gast constants."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 139,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x720 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "state = 'liq'\n",
+ "\n",
+ "fig = plt.figure(figsize=(10,10))\n",
+ "\n",
+ "ax1 = plt.subplot(2,1,1)\n",
+ "\n",
+ "ax1.set_ylabel('$|\\mathrm{CC}_\\mathrm{liq} - 1|$')\n",
+ "ax1.set_yscale('log')\n",
+ "ax1.set_xticklabels([])\n",
+ "\n",
+ "TK = np.arange(180,320,0.5)\n",
+ "\n",
+ "lv = phase_change_enthalpy(TK)\n",
+ "if (state == 'ice'): lv += phase_change_enthalpy(TK,fusion=True)\n",
+ "\n",
+ "y = lv/(Rv * TK)\n",
+ "cc_w = dlnesdlnT(TK,formula=\"wagner-pruss\",state=state) / y\n",
+ "cc_r = dlnesdlnT(TK,formula='romps',state=state) /y\n",
+ "cc_g = dlnesdlnT(TK,formula='goff-gratch',state=state) /y\n",
+ "cc_m = dlnesdlnT(TK,formula='murphy-koop',state=state) /y\n",
+ "cc_s = dlnesdlnT(TK,formula='sonntag',state=state) /y\n",
+ "cc_h = dlnesdlnT(TK,formula='hardy98',state=state) /y\n",
+ "cc_a = dlnesdlnT(TK,formula='standard-analytic',state=state) /y\n",
+ "\n",
+ "plt.plot(TK,np.abs(cc_h/1 -1.),c='tab:blue',label='Hardy (1998)')\n",
+ "plt.plot(TK,np.abs(cc_g/1 -1.),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(cc_r/1 -1.),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(cc_s/1 -1.),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(cc_m/1 -1.),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(cc_w/1 -1.),c='tab:olive',label='Wagner-Pruss (2002)')\n",
+ "plt.plot(TK,np.abs(cc_a/1 -1.),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "plt.legend(loc=\"lower left\",ncol=1)\n",
+ "\n",
+ "state = 'ice'\n",
+ "TK = np.arange(180,320,0.5)\n",
+ "\n",
+ "lv = phase_change_enthalpy(TK)\n",
+ "if (state == 'ice'): lv = phase_change_enthalpy(TK,fusion=True) + phase_change_enthalpy(TK)\n",
+ "\n",
+ "y = lv/(Rv * TK)\n",
+ "cc_w = dlnesdlnT(TK,formula=\"wagner-pruss\",state=state) / y\n",
+ "cc_r = dlnesdlnT(TK,formula='romps',state=state) /y\n",
+ "cc_g = dlnesdlnT(TK,formula='goff-gratch',state=state) /y\n",
+ "cc_m = dlnesdlnT(TK,formula='murphy-koop',state=state) /y\n",
+ "cc_s = dlnesdlnT(TK,formula='sonntag',state=state) /y\n",
+ "cc_a = dlnesdlnT(TK,formula='standard-analytic',state=state) /y\n",
+ "\n",
+ "ax2 = plt.subplot(2,1,2)\n",
+ "ax2.set_xlabel('$T$ / K')\n",
+ "ax2.set_ylabel('$|\\mathrm{CC}_\\mathrm{ice} - 1|$')\n",
+ "ax2.set_yscale('log')\n",
+ "\n",
+ "plt.plot(TK,np.abs(cc_g/1 -1.),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(cc_r/1 -1.),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(cc_s/1 -1.),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(cc_m/1 -1.),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(cc_w/1 -1.),c='tab:olive',label='Wagner-Pruss (2002)')\n",
+ "plt.plot(TK,np.abs(cc_a/1. -1.),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'cc-error.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Optimizing analytic fits for saturation and sublimation vapor pressure ##\n",
+ "\n",
+ "Romps suggests modifying the specific heats of liquid, ice and the gas constant of vapor to arrive at an optimal fit for the saturation vapor pressure using the analytic form. One can do almost as good by just modifying the specific heat of the condensate phases. Here we show how the maximum error in the fit depends on the specific heat of the condensate phases as compared to the reference, and how we arrive at our optimal fit by only manipulating the condensate phase specific heats to values that they anyway adopt within the range of temperatures spanned by the atmosphere. This justifys the default choice for saturation vapor pressure and the specific heats used in aes_thermo.py\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 140,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Taking fit for $c_\\mathrm{liq}=$ 4179.57 J/(kg K) at $T=$ 305.00 K\n",
+ "Taking fit for $c_\\mathrm{ice}=$ 1905.43 J/(kg K) at $T=$ 247.06 K\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(10,5))\n",
+ "\n",
+ "cl_1 = (iapws._iapws._Liquid(265, 0.1)['cp'])*1000.\n",
+ "cl_2 = (iapws._iapws._Liquid(305, 0.1)['cp'])*1000\n",
+ "ci_1 = (iapws._iapws._Ice(193, 0.01)['cp'])*1000.\n",
+ "ci_2 = (iapws._iapws._Ice(273, 0.10)['cp'])*1000\n",
+ "\n",
+ "cls = np.arange(cl_2,cl_1)\n",
+ "err = np.zeros(len(cls))\n",
+ "\n",
+ "ax1 = plt.subplot(1,2,1)\n",
+ "ax1.set_xlabel('$c_\\mathrm{liq}$ / Jkg$^{-1}$K$^{-1}$')\n",
+ "ax1.set_ylabel('$(e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1)_\\mathrm{max}$ / %')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "state = 'liq'\n",
+ "TK = np.arange(260,300,0.5)\n",
+ "es_ref = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "for i,cx in enumerate(cls):\n",
+ " c1 = (cpv-cx)/Rv\n",
+ " c2 = lvT/(Rv*TvT) - c1\n",
+ " es_a = PvT * np.exp(c2*(1.-TvT/TK)) * (TK/TvT)**c1\n",
+ " err[i] = np.max(np.abs(es_a/es_ref -1.))*100.\n",
+ "ax1.plot(cls,err,c='tab:purple',ls='dotted',label='Analytic $c_\\mathrm{liq}$ for $T\\in$ (260K,305K)')\n",
+ "ax1.legend(loc=\"upper left\",ncol=2)\n",
+ "\n",
+ "cis = np.arange(ci_1,ci_2)\n",
+ "err = np.zeros(len(cis))\n",
+ "\n",
+ "ax2 = plt.subplot(1,2,2)\n",
+ "ax2.set_xlabel('$c_\\mathrm{ice}$ / Jkg$^{-1}$K$^{-1}$')\n",
+ "ax2.set_ylabel('$(e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1)_\\mathrm{max}$ / %')\n",
+ "ax2.set_yscale('log')\n",
+ "\n",
+ "state = 'ice'\n",
+ "TK = np.arange(180,273,0.5)\n",
+ "es_ref = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "for i,cx in enumerate(cis):\n",
+ " c1 = (cpv-cx)/Rv\n",
+ " c2 = lsT/(Rv*TvT) - c1\n",
+ " es_a = PvT * np.exp(c2*(1.-TvT/TK)) * (TK/TvT)**c1\n",
+ " err[i] = np.max(np.abs(es_a/es_ref -1.))*100.\n",
+ "ax2.plot(cis,err,c='tab:purple',ls='dotted',label='Analytic $c_\\mathrm{ice}$ for $T\\in$ (193K,273K)')\n",
+ "ax2.legend(loc=\"upper right\",ncol=2)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'es-analytic-fits.pdf')\n",
+ "Tfit = 305\n",
+ "print ('Taking fit for $c_\\mathrm{liq}=$ %3.2f J/(kg K) at $T=$ %3.2f K'%(iapws._iapws._Liquid(Tfit, 0.1)['cp']*1000.,Tfit))\n",
+ "Tfit = 247.065\n",
+ "print ('Taking fit for $c_\\mathrm{ice}=$ %3.2f J/(kg K) at $T=$ %3.2f K'%(iapws._iapws._Ice(Tfit, 0.1)['cp']*1000.,Tfit))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RCEMIP comparision ##\n",
+ "\n",
+ "During RCEMIP (Wing et al.) different models output different RH, differing in ways of calculating it and also whether or not it was calculated relative to liquid or ice. In this analysis we create a python implementation of the intial RCEMIP sounding and then for the given state estimate the RH using different formulat and different assumptions regarding the reference condensate (liquid/ice). We also show the difference associated with 1 K of temperature. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 141,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def rcemip_on_z(z,SST):\n",
+ " # function [T,q,p] = rcemip_on_z(z,SST)\n",
+ " # \n",
+ " # Inputs:\n",
+ " # z: array of heights (low to high, m)\n",
+ " # SST: sea surface temperature (K)\n",
+ " # \n",
+ " # Outputs:\n",
+ " T = np.zeros(len(z)) # temperature (K)\n",
+ " q = np.zeros(len(z)) # specific humidity (g/g)\n",
+ " p = np.zeros(len(z)) # pressure (Pa)\n",
+ "\n",
+ " ## Constants\n",
+ " g = 9.79764 #m/s^2\n",
+ " Rd = 287.04 #J/kgK\n",
+ " \n",
+ " ## Parameters\n",
+ " p0 = 101480 #Pa surface pressure\n",
+ " qt = 10**(-11) #g/g specific humidity at tropopause\n",
+ " zq1 = 4000 #m\n",
+ " zq2 = 7500 #m\n",
+ " zt = 15000 #m tropopause height\n",
+ " gamma = 0.0067 #K/m lapse rate\n",
+ " \n",
+ " ## Scratch\n",
+ " Tv = np.zeros(len(z)) # temperature (K)\n",
+ "\n",
+ " if SST == 295:\n",
+ " q0 = 0.01200; #g/g specific humidity at surface (adjusted from 300K value so RH near surface approx 80%)\n",
+ " elif SST == 300:\n",
+ " q0 = 0.01865; #g/g specific humidity at surface\n",
+ " elif SST == 305:\n",
+ " q0 = 0.02400 #g/g specific humidity at surface (adjusted from 300K value so RH near surface approx 80%)\n",
+ " \n",
+ " T0 = SST - 0 #surface air temperature adjusted to be 0K less than SST\n",
+ " \n",
+ " ## Virtual Temperature at surface and tropopause\n",
+ " Tv0 = T0*(1 + 0.608*q0) #virtual temperature at surface\n",
+ " Tvt = Tv0 - gamma*zt #virtual temperature at tropopause z=zt\n",
+ " \n",
+ " ## Pressure\n",
+ " pt = p0*(Tvt/Tv0)**(g/(Rd*gamma)); #pressure at tropopause z=zt\n",
+ " p = p0*((Tv0-gamma*z)/Tv0)**(g/(Rd*gamma)) #0 <= z <= zt\n",
+ " p[z>zt] = pt*np.exp(-g*(z[z>zt]-zt)/(Rd*Tvt)) #z > zt\n",
+ " \n",
+ " ## Specific humidity\n",
+ " q = q0*np.exp(-z/zq1)*np.exp(-(z/zq2)**2)\n",
+ " q[z>zt] = qt #z > zt\n",
+ " \n",
+ " ## Temperature\n",
+ " #Virtual Temperature\n",
+ " Tv = Tv0 - gamma*z #0 <= z <= zt\n",
+ " Tv[z>zt] = Tvt #z > zt\n",
+ " \n",
+ " #Absolute Temperature at all heights\n",
+ " T = Tv/(1 + 0.608*q)\n",
+ " \n",
+ " return T, q, p\n",
+ "\n",
+ "z = np.arange(0,17000,100)\n",
+ "T, q , p = rcemip_on_z(z,300)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 142,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 288x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def get_rh (T,q,p,formula='wagner-pruss',state='liq'):\n",
+ " es_w = es(T,formula=formula,state=state)\n",
+ " x = es_w * eps1/(p-es_w)\n",
+ " return 100.*q*(1+x)/x\n",
+ " \n",
+ "fig = plt.figure(figsize=(4,5))\n",
+ "\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_ylabel('$z$ / km')\n",
+ "ax1.set_xlabel('RH / %')\n",
+ "ax1.set_ylim(0,14.5)\n",
+ "ax1.set_yticks([0,4,8,12])\n",
+ "\n",
+ "plt.plot(get_rh(T,q,p,state='mxd'),z/1000.,label = 'Wagner Pruss (ice/liq)')\n",
+ "plt.plot(get_rh(T+1,q,p,state='mxd'),z/1000.,label = 'Wagner Pruss (ice/liq) + 1 K')\n",
+ "plt.plot(get_rh(T,q,p,state='ice'),z/1000.,label = 'Wagner Pruss (ice)')\n",
+ "plt.plot(get_rh(T,q,p,formula='romps',state='mxd'),z/1000.,label = 'Romps (ice/liq)')\n",
+ "plt.plot(get_rh(T,q,p),z/1000.,label = 'Wagner Pruss (liq)')\n",
+ "plt.plot(get_rh(T,q,p,formula='flatau'),z/1000.,label = 'Flatau (liq)')\n",
+ "\n",
+ "plt.legend(loc=\"lower left\",ncol=1)\n",
+ "\n",
+ "sns.set_context(\"paper\")\n",
+ "sns.despine(offset=10)\n",
+ "plt.tight_layout()\n",
+ "\n",
+ "fig.savefig(plot_dir+'RCEMIP-RHerror.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## lifting-condensation level approximations\n",
+ "\n",
+ "Here we compare the LCL base predictions to those proposed by Romps and Bolton as well as the differences between density potential temperatures.\n",
+ "\n",
+ "For the estimation of the LCL we modify the Romps expressions (using his code) to output pressure at the LCL, as this eliminates an assumption as to how pressure is distributed in the atmosphere, and thus only depends on the parcel state. What we find is that the much simpler Bolton expression is as good as the more complex expression by Romps, and differences between the two are commensurate with those arising from slight differences in how the saturation vapor pressure is calculated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 148,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Version 1.0 released by David Romps on September 12, 2017.\n",
+ "# \n",
+ "# When using this code, please cite:\n",
+ "# \n",
+ "# @article{16lcl,\n",
+ "# Title = {Exact expression for the lifting condensation level},\n",
+ "# Author = {David M. Romps},\n",
+ "# Journal = {Journal of the Atmospheric Sciences},\n",
+ "# Year = {2017},\n",
+ "# Volume = {in press},\n",
+ "# }\n",
+ "#\n",
+ "# This lcl function returns the height of the lifting condensation level\n",
+ "# (LCL) in meters. The inputs are:\n",
+ "# - p in Pascals\n",
+ "# - T in Kelvins\n",
+ "# - Exactly one of rh, rhl, and rhs (dimensionless, from 0 to 1):\n",
+ "# * The value of rh is interpreted to be the relative humidity with\n",
+ "# respect to liquid water if T >= 273.15 K and with respect to ice if\n",
+ "# T < 273.15 K. \n",
+ "# * The value of rhl is interpreted to be the relative humidity with\n",
+ "# respect to liquid water\n",
+ "# * The value of rhs is interpreted to be the relative humidity with\n",
+ "# respect to ice\n",
+ "# - ldl is an optional logical flag. If true, the lifting deposition\n",
+ "# level (LDL) is returned instead of the LCL. \n",
+ "# - min_lcl_ldl is an optional logical flag. If true, the minimum of the\n",
+ "# LCL and LDL is returned.\n",
+ "\n",
+ "def lcl(p,T,rh=None,rhl=None,rhs=None,return_ldl=False,return_min_lcl_ldl=False):\n",
+ "\n",
+ " import math\n",
+ " import scipy.special\n",
+ "\n",
+ " # Parameters\n",
+ " Ttrip = 273.16 # K\n",
+ " ptrip = 611.65 # Pa\n",
+ " E0v = 2.3740e6 # J/kg\n",
+ " E0s = 0.3337e6 # J/kg\n",
+ " ggr = 9.81 # m/s^2\n",
+ " rgasa = 287.04 # J/kg/K \n",
+ " rgasv = 461 # J/kg/K \n",
+ " cva = 719 # J/kg/K\n",
+ " cvv = 1418 # J/kg/K \n",
+ " cvl = 4119 # J/kg/K \n",
+ " cvs = 1861 # J/kg/K \n",
+ " cpa = cva + rgasa\n",
+ " cpv = cvv + rgasv\n",
+ "\n",
+ " # The saturation vapor pressure over liquid water\n",
+ " def pvstarl(T):\n",
+ " return ptrip * (T/Ttrip)**((cpv-cvl)/rgasv) * math.exp( (E0v - (cvv-cvl)*Ttrip) / rgasv * (1/Ttrip - 1/T) )\n",
+ " # The saturation vapor pressure over solid ice\n",
+ " def pvstars(T):\n",
+ " return ptrip * (T/Ttrip)**((cpv-cvs)/rgasv) * math.exp( (E0v + E0s - (cvv-cvs)*Ttrip) / rgasv * (1/Ttrip - 1/T)) \n",
+ "\n",
+ " # Calculate pv from rh, rhl, or rhs\n",
+ " rh_counter = 0\n",
+ " if rh is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rhl is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rhs is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rh_counter != 1:\n",
+ " print(rh_counter)\n",
+ " exit('Error in lcl: Exactly one of rh, rhl, and rhs must be specified')\n",
+ " if rh is not None:\n",
+ " # The variable rh is assumed to be \n",
+ " # with respect to liquid if T > Ttrip and \n",
+ " # with respect to solid if T < Ttrip\n",
+ " if T > Ttrip:\n",
+ " pv = rh * pvstarl(T)\n",
+ " else:\n",
+ " pv = rh * pvstars(T)\n",
+ " rhl = pv / pvstarl(T)\n",
+ " rhs = pv / pvstars(T)\n",
+ " elif rhl is not None:\n",
+ " pv = rhl * pvstarl(T)\n",
+ " rhs = pv / pvstars(T)\n",
+ " if T > Ttrip:\n",
+ " rh = rhl\n",
+ " else:\n",
+ " rh = rhs\n",
+ " elif rhs is not None:\n",
+ " pv = rhs * pvstars(T)\n",
+ " rhl = pv / pvstarl(T)\n",
+ " if T > Ttrip:\n",
+ " rh = rhl\n",
+ " else:\n",
+ " rh = rhs\n",
+ " if pv > p:\n",
+ " return N\n",
+ "\n",
+ "# Calculate lcl_liquid and lcl_solid\n",
+ " qv = rgasa*pv / (rgasv*p + (rgasa-rgasv)*pv)\n",
+ " rgasm = (1-qv)*rgasa + qv*rgasv\n",
+ " cpm = (1-qv)*cpa + qv*cpv\n",
+ " if rh == 0:\n",
+ " return cpm*T/ggr\n",
+ " aL = -(cpv-cvl)/rgasv + cpm/rgasm\n",
+ " bL = -(E0v-(cvv-cvl)*Ttrip)/(rgasv*T)\n",
+ " cL = pv/pvstarl(T)*math.exp(-(E0v-(cvv-cvl)*Ttrip)/(rgasv*T))\n",
+ " aS = -(cpv-cvs)/rgasv + cpm/rgasm\n",
+ " bS = -(E0v+E0s-(cvv-cvs)*Ttrip)/(rgasv*T)\n",
+ " cS = pv/pvstars(T)*math.exp(-(E0v+E0s-(cvv-cvs)*Ttrip)/(rgasv*T))\n",
+ " X = bL/(aL*scipy.special.lambertw(bL/aL*cL**(1/aL),-1).real)\n",
+ " Y = bS/(aS*scipy.special.lambertw(bS/aS*cS**(1/aS),-1).real) \n",
+ " \n",
+ " lcl = cpm*T/ggr*( 1 - X)\n",
+ " ldl = cpm*T/ggr*( 1 - Y)\n",
+ "\n",
+ " # Modifications of the code to output Plcl or Pldl\n",
+ " Plcl = PPa * X**(cpm/rgasm)\n",
+ " Pldl = PPa * X**(cpm/rgasm)\n",
+ " # Return either lcl or ldl\n",
+ " if return_ldl and return_min_lcl_ldl:\n",
+ " exit('return_ldl and return_min_lcl_ldl cannot both be true')\n",
+ " elif return_ldl:\n",
+ " return Pldl\n",
+ " elif return_min_lcl_ldl:\n",
+ " return min(Plcl,Pldl)\n",
+ " else:\n",
+ " return Plcl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 146,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/m219063/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py:162: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
+ " improvement from the last ten iterations.\n",
+ " warnings.warn(msg, RuntimeWarning)\n",
+ "/Users/m219063/opt/anaconda3/lib/python3.7/site-packages/scipy/optimize/minpack.py:162: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
+ " improvement from the last five Jacobian evaluations.\n",
+ " warnings.warn(msg, RuntimeWarning)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import aes_thermo as mt\n",
+ "PPa = 101325.\n",
+ "\n",
+ "qt = np.arange(2.5e-3,8e-3,0.2e-3)\n",
+ "TK = 285.\n",
+ "Plcl_X = mt.get_Plcl(TK,PPa,qt,iterate=True)\n",
+ "Plcl_B = mt.get_Plcl(TK,PPa,qt)\n",
+ "Plcl_R = np.zeros(len(Plcl_X))\n",
+ "\n",
+ "for i,x in enumerate(qt):\n",
+ " if (x>0.1): x = x/1000.\n",
+ " RH = mt.mr2pp(x/(1.-x),PPa)/mt.es(TK)\n",
+ " Plcl_R[i] = lcl(PPa,TK,RH)\n",
+ "\n",
+ "del1 = (Plcl_B-Plcl_X)/100.\n",
+ "del2 = (Plcl_R-Plcl_X)/100.\n",
+ "\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "ax1 = plt.subplot(1,2,1)\n",
+ "ax1.set_ylabel('$P$ / hPa')\n",
+ "ax1.set_xlabel('$q_\\mathrm{t}$ / g/kg')\n",
+ "#ax1.set_ylim(-1.2,1.2)\n",
+ "\n",
+ "plt.plot(qt*1.e3,del1,label='$\\\\delta_\\mathrm{B}$, $T$=285K')\n",
+ "plt.plot(qt*1.e3,del2,label='$\\\\delta_\\mathrm{R}$, $T$=285K')\n",
+ "#plt.gca().invert_yaxis()\n",
+ "plt.legend(loc=\"best\")\n",
+ "\n",
+ "qt = np.arange(0.5e-3,28e-3,0.2e-3)\n",
+ "TK = 310.\n",
+ "Plcl_X = mt.get_Plcl(TK,PPa,qt,iterate=True)\n",
+ "Plcl_B = mt.get_Plcl(TK,PPa,qt)\n",
+ "Plcl_R = np.zeros(len(Plcl_X))\n",
+ "\n",
+ "for i,x in enumerate(qt):\n",
+ " if (x>0.1): x = x/1000.\n",
+ " RH = mt.mr2pp(x/(1.-x),PPa)/mt.es(TK)\n",
+ " Plcl_R[i] = lcl(PPa,TK,RH)\n",
+ "\n",
+ "del1 = (Plcl_B-Plcl_X)/100.\n",
+ "del2 = (Plcl_R-Plcl_X)/100.\n",
+ "\n",
+ "ax2 = plt.subplot(1,2,2)\n",
+ "ax2.set_xlabel('$q_\\mathrm{t}$ / g/kg')\n",
+ "#ax2.set_ylim(-1.2,1.2)\n",
+ "ax2.set_yticklabels([])\n",
+ "\n",
+ "plt.plot(qt*1.e3,del1,label='$\\\\delta_\\mathrm{B}$, $T$=310K')\n",
+ "plt.plot(qt*1.e3,del2,label='$\\\\delta_\\mathrm{R}$, $T$=310K')\n",
+ "#plt.gca().invert_yaxis()\n",
+ "plt.legend(loc=\"best\")\n",
+ "\n",
+ "sns.set_context(\"talk\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "fig.savefig(plot_dir+'Plcl.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Acknowledgments ##\n",
+ "\n",
+ "Jiawei Bao, Geet George, and Hauke Schulz are thanked for comments on these notes, and the identification of some errors in earlier versions. Axel Seifert is thanked for his comments and insights, and for pointing out the TEOS routines of Feisel et al. (2010) which extend the IAPWS libraries."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/data/cp-ice-vap.dat b/examples/data/cp-ice-vap.dat
new file mode 100644
index 0000000..755e422
--- /dev/null
+++ b/examples/data/cp-ice-vap.dat
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diff --git a/examples/data/cp-liq-vap.dat b/examples/data/cp-liq-vap.dat
new file mode 100644
index 0000000..312b14d
--- /dev/null
+++ b/examples/data/cp-liq-vap.dat
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diff --git a/examples/data/psat.dat b/examples/data/psat.dat
new file mode 100644
index 0000000..4924bfe
--- /dev/null
+++ b/examples/data/psat.dat
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+ 0.32866000000E+03 0.16151277415E+05 0.99999999900E+99
+ 0.32881000000E+03 0.16267291149E+05 0.99999999900E+99
+ 0.32896000000E+03 0.16384015862E+05 0.99999999900E+99
+ 0.32911000000E+03 0.16501455088E+05 0.99999999900E+99
+ 0.32926000000E+03 0.16619612373E+05 0.99999999900E+99
+ 0.32941000000E+03 0.16738491278E+05 0.99999999900E+99
+ 0.32956000000E+03 0.16858095373E+05 0.99999999900E+99
+ 0.32971000000E+03 0.16978428244E+05 0.99999999900E+99
+ 0.32986000000E+03 0.17099493489E+05 0.99999999900E+99
+ 0.33001000000E+03 0.17221294720E+05 0.99999999900E+99
+ 0.33016000000E+03 0.17343835560E+05 0.99999999900E+99
diff --git a/moist_thermodynamics/examples.ipynb b/examples/examples.ipynb
similarity index 99%
rename from moist_thermodynamics/examples.ipynb
rename to examples/examples.ipynb
index f352919..0c75eae 100644
--- a/moist_thermodynamics/examples.ipynb
+++ b/examples/examples.ipynb
@@ -24,13 +24,13 @@
"\n",
"Usage of the moist thermodynamic functions is documented through a number of examples\n",
"\n",
- "1. moist adiabat from potential temperature and surface specific humidity.\n",
- "2. consistency check showing temperature differences following constant moist potential temperatures\n",
+ "1. constructing a moist adiabat.\n",
+ "2. sensitivity of moist adiabat on saturation vapor pressure \n",
"3. lcl computations\n",
"\n",
"## 1. Constructing a moist adiabat\n",
"\n",
- "This simple example shows how simple it is to construct a moist adiabat. For the example it is constructed by assuming a constant $\\theta_\\mathrm{l}$ but the same answer (with the caveats of the next example) would arise if we were to define it in terms of constant $\\theta_\\mathrm{e}$ or $\\theta_\\mathrm{s}$"
+ "This shows how simple it is to construct a moist adiabat. For the example it is constructed by assuming a constant $\\theta_\\mathrm{l}$ but the same answer (with the caveats of the next example) would arise if we were to define it in terms of constant $\\theta_\\mathrm{e}$ or $\\theta_\\mathrm{s}$"
]
},
{
@@ -92,7 +92,7 @@
"id": "b2f6c280-e7b0-48ac-acc5-15053cabe4d0",
"metadata": {},
"source": [
- "## 2. Effect of saturation vapor pressure expression on moist potential temperatures\n",
+ "## 2. Sensitivity (small) of moist adiabat on saturation vapor pressure \n",
"\n",
"The derivation of the moist potential temperatures assumes a Rankine fluid, i.e., constant specific heats. Specific heats vary with temperature however, especially $c_i$. This variation is encoded in the best fits to the saturation vapor pressure, so that an adiabat defined in terms of a best fit saturation vapor pressure will differ depending on whether it assumes $\\theta_\\mathrm{e},$ $\\theta_\\mathrm{l},$ or $\\theta_\\mathrm{s}.$ This sensitivity vanishes (right plot, note $x$-axis scale) when we replace the more accurate saturation vapor pressures with less accurate expressions, albeit consistent with a Rankine fluid."
]
@@ -163,7 +163,7 @@
"id": "b2fd8753-736f-459c-a435-0c50ad8eeae9",
"metadata": {},
"source": [
- "## 3. lifting condensation level\n",
+ "## 3. Calculations of lifting condensation level\n",
"\n",
"We compare three different formulations of the lifting condensation level, one due to Romps (2017) is not included in the moist_thermodynamics library, but is included here for sake of comparision. The analysis shows that the simple bolton approximations work very well, as well as those of Romps if one uses the wagner saturation vapor pressure data. Had we performed this comparison with the analytic formula using the specific heats specified by Romps, the comparison would have been more favorable for the Romps formulation."
]
diff --git a/examples/saturation-water-vapor.ipynb b/examples/saturation-water-vapor.ipynb
new file mode 100644
index 0000000..2e695b9
--- /dev/null
+++ b/examples/saturation-water-vapor.ipynb
@@ -0,0 +1,1103 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Notes on calculations of vapor pressures and the specific heats #\n",
+ "\n",
+ "These notes do not use the moist_thermodynamic libararies, rather they were the basis for the choice for the particular formulations for the specific heats and saturation vapor pressures within that library.\n",
+ "\n",
+ "There are a large number of expressions for the saturation and sublimation vapor pressure in the literature, and many of these, even recent ones, seem to reference previous studies in a haphazard way. So how much do these differ, is there a standard, and by what criteria should one judge them by. Here I try to develop an intuition for the answers.\n",
+ "\n",
+ "The first thing to note is that there is a community that concerns itself with this question. They call themselves the international association for the physical properties of water and steam, and mostly concern themselves with the behavior of water at high temperature. The approach of the IAPWS is to develop an empirical equation of state for water, in the form of a specification of its Helmholtz free energy, or potential, from which all other properties can be derived. The standard reference for the IAPWS equation of state is the publication by Wagner and Pru{\\ss} (Thermodynamic Properties of Ordinary Water) published in 2002 and which describes the IAPWS-95 approved formulation. Minor corrections have since been made to this, which as best I can tell are relevant at high temperatures. The most substantial change has been the TEOS-10 work by Rainer Feistel of IOW, which extends these approaches to composite systems, thereby allowing for representations of sea-water and moist air. By working with an equation of state, all properties of water, from the specific heats to the gas constants to the phase-change enthalpies can be derived consistently. The disadvantage of this approach is that the equation is derived by positing an analytic form that is then fit to a very wide and diverse abundance of existing data. The resultant equation is described in an ideal part, which involves a summation of nine terms and thirteen coefficients, and a residual part, with more than 50 terms and over 200 constants.\n",
+ "\n",
+ "For the case of the saturation vapor pressure over water Wagner and Pru{\\ss} suggest a much simpler equation that is described in terms of only six coefficients. First, below I compare the relative error to the IAPWS standard as has been formlated and distributed in the iapws python package, version (1.4). There has been some discussion on the web of its implementation, but the similarity with the Wagner and Pru{\\ss} formulation gives me confidence. Next we look at the TEOS-10 Sea-Ice-Air formulations of Feistel et al (Ocean Sci., 6, 91–141, 2010) which are distributed as FORTRAN90 code which I downloaded, ran, and tabulated to assess some empirical fits later used as a reference.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "zsh:fg:1: no job control in this shell.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "import os\n",
+ "from scipy import interpolate, optimize\n",
+ "\n",
+ "os.makedirs(\"plots\", exist_ok=True)\n",
+ "\n",
+ "!%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gravity = 9.8076\n",
+ "\n",
+ "cpd = 1006.\n",
+ "Rd = 287.05\n",
+ "\n",
+ "Rv = 461.53 # IAPWS97 at 273.15\n",
+ "cpv = 1865.01 # ''\n",
+ "lv0 = 2500.93e3 # IAPWS97 at 273.15\n",
+ "lf0 = 333.42e3 #''\n",
+ "\n",
+ "cl = 4179.57 # IAPWS97 at 305 and P=0.1 MPa (chosen to give a good fit for es over ice)\n",
+ "ci = 1905.43 # IAPWS97 at 247.065 and P=0.1 MPa (chosen to give a good fit for es over ice)\n",
+ "\n",
+ "eps1 = Rd/Rv\n",
+ "eps2 = Rv/Rd -1.\n",
+ "\n",
+ "P0 = 100000. # Standard Pressure\n",
+ "T0 = 273.15 # Standard Temperature\n",
+ "PvC = 22.064e6 # Critical pressure of water vapor\n",
+ "TvC = 647.096 # Critical temperature of water vapor\n",
+ "TvT = 273.16 # Triple point temperature of water\n",
+ "PvT = 611.655\n",
+ "lvT = lv0 + (cpv-cl)*(TvT-T0)\n",
+ "lfT = lf0 + (cpv-ci)*(TvT-T0)\n",
+ "lsT = lvT + lfT\n",
+ "\n",
+ "es_default = 'sonntag'\n",
+ "\n",
+ "def thermo_input(x, xtype='none'):\n",
+ " \n",
+ " import numpy as np\n",
+ "\n",
+ " x = np.asarray(x).flatten()\n",
+ " scalar_input = False\n",
+ " if x.ndim == 0:\n",
+ " x = x[None] # Makes x 1D\n",
+ " scalar_input = True\n",
+ "\n",
+ " if (xtype == 'Kelvin' and x.max() < 100 ): x = x+273.15\n",
+ " if (xtype == 'Celcius'and x.max() > 100 ): x = x-273.15\n",
+ " if (xtype == 'Pascal' and x.max() < 1200): x = x*100.\n",
+ " if (xtype == 'kg/kg' and x.max() > 1.0) : x = x/1000.\n",
+ " if (xtype == 'meter' and x.max() < 10.0): print('Warning: input should be in meters, max value less than 10, not corrected')\n",
+ "\n",
+ " return x, scalar_input\n",
+ "\n",
+ "def eslf(T, formula=es_default):\n",
+ " \"\"\" Returns the saturation vapour pressure [Pa] over liquid given \n",
+ "\tthe temperature. Temperatures can be in Celcius or Kelvin.\n",
+ "\tFormulas supported are\n",
+ "\t - Goff-Gratch (1994 Smithsonian Tables)\n",
+ "\t - Sonntag (1994) \n",
+ "\t - Flatau\n",
+ "\t - Magnus Tetens (MT)\n",
+ " - Romps (2017)\n",
+ " - Murphy-Koop\n",
+ " - Bolton\n",
+ " - Wagner and Pruss (WP, 2002) is the default\n",
+ "\t>>> eslf(273.16)\n",
+ "\t611.657\n",
+ " \"\"\"\n",
+ " import numpy as np\n",
+ "\n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ "\n",
+ " if formula == \"flatau\":\n",
+ " if (np.min(x) > 100): x = x-273.16\n",
+ " np.maximum(x,-80.)\n",
+ " c_es= np.asarray([0.6105851e+03, 0.4440316e+02, 0.1430341e+01, 0.2641412e-01,\n",
+ " 0.2995057e-03,0.2031998e-05,0.6936113e-08,0.2564861e-11,-0.3704404e-13])\n",
+ " es = np.polyval(c_es[::-1],x)\n",
+ " elif formula == \"bolton\":\n",
+ " if (np.min(x) > 100): x = x-273.15\n",
+ " es = 611.2*np.exp((17.67*x)/(243.5+x))\n",
+ " elif formula == \"sonntag\":\n",
+ " xx = -6096.9385/x + 16.635794 - 2.711193e-2*x + 1.673952e-5*x*x + 2.433502 * np.log(x)\n",
+ " es = 100.*np.exp(xx)\n",
+ " elif formula =='goff-gratch':\n",
+ " x1 = 273.16/x\n",
+ " x2 = 373.16/x\n",
+ " xl = np.log10(1013.246 ) - 7.90298*(x2 - 1) + 5.02808*np.log10(x2) - 1.3816e-7*(10**(11.344*(1.-1./x2)) - 1.0) + 8.1328e-3 * (10**(-3.49149*(x2-1)) - 1.0)\n",
+ " es =10**(xl+2) # plus 2 converts from hPa to Pa\n",
+ " elif formula == 'wagner-pruss':\n",
+ " vt = 1.-x/TvC\n",
+ " es = PvC * np.exp(TvC/x * (-7.85951783*vt + 1.84408259*vt**1.5 - 11.7866497*vt**3 + 22.6807411*vt**3.5 - 15.9618719*vt**4 + 1.80122502*vt**7.5))\n",
+ " elif formula == 'hardy98':\n",
+ " y = -2.8365744e+3/(x*x) - 6.028076559e+3/x + 19.54263612 - 2.737830188e-2*x + 1.6261698e-5*x**2 + 7.0229056e-10*x**3 - 1.8680009e-13*x**4 + 2.7150305 * np.log(x)\n",
+ " es = np.exp(y)\n",
+ " elif formula == 'romps':\n",
+ " Rr = 461.\n",
+ " cvl_r = 4119\n",
+ " cvv_r = 1418\n",
+ " cpv_r = cvv_r + Rr\n",
+ " es = 611.65 * (x/TvT) **((cpv_r-cvl_r)/Rr) * np.exp((2.37403e6 - (cvv_r-cvl_r)*TvT)*(1/TvT - 1/x)/Rr)\n",
+ " elif formula == \"murphy-koop\":\n",
+ " es = np.exp(54.842763 - 6763.22/x - 4.210*np.log(x) + 0.000367*x + np.tanh(0.0415*(x - 218.8)) * (53.878 - 1331.22/x - 9.44523 * np.log(x) + 0.014025*x))\n",
+ " elif formula == \"standard-analytic\":\n",
+ " c1 = (cpv-cl)/Rv\n",
+ " c2 = lvT/(Rv*TvT) - c1\n",
+ " es = PvT * np.exp(c2*(1.-TvT/x)) * (x/TvT)**c1\n",
+ " else:\n",
+ " exit(\"formula not supported\")\n",
+ "\n",
+ " es = np.maximum(es,0)\n",
+ " if scalar_input:\n",
+ " return np.squeeze(es)\n",
+ " return es\n",
+ "\n",
+ "def esif(T, formula=es_default):\n",
+ " \"\"\" Returns the saturation vapour pressure [Pa] over ice given \n",
+ "\tthe temperature. Temperatures can be in Celcius or Kelvin.\n",
+ "\tuses the Goff-Gratch (1994 Smithsonian Tables) formula\n",
+ "\t>>> esli(273.15)\n",
+ "\t6.112\n",
+ "m \"\"\"\n",
+ " import numpy as np\n",
+ "\n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ "\n",
+ " if formula == \"sonntag\":\n",
+ " es = 100 * np.exp(24.7219 - 6024.5282/x + 0.010613868*x - 0.000013198825*x**2 - 0.49382577*np.log(x))\n",
+ " elif formula == \"goff-gratch\":\n",
+ " x1 = 273.16/x\n",
+ " xi = np.log10( 6.1071) - 9.09718*(x1 - 1) - 3.56654*np.log10(x1) + 0.876793*(1 - 1./x1)\n",
+ " es = 10**(xi+2)\n",
+ " elif formula == \"wagner-pruss\": #(actually wagner et al, 2011)\n",
+ " a1 = -0.212144006e+2\n",
+ " a2 = 0.273203819e+2\n",
+ " a3 = -0.610598130e+1\n",
+ " b1 = 0.333333333e-2\n",
+ " b2 = 0.120666667e+1\n",
+ " b3 = 0.170333333e+1\n",
+ " theta = T/TvT\n",
+ " es = PvT * np.exp((a1*theta**b1 + a2 * theta**b2 + a3 * theta**b3)/theta)\n",
+ " elif formula == \"murphy-koop\":\n",
+ " es = np.exp(9.550426 - 5723.265/x + 3.53068 * np.log(x) - 0.00728332*x)\n",
+ " elif formula == \"romps\":\n",
+ " Rr = 461.\n",
+ " cvv_r = 1418.\n",
+ " cvs_r = 1861.\n",
+ " cpv_r = cvv_r + Rr\n",
+ " es = 611.65 * (x/TvT) **((cpv_r-cvs_r)/Rr) * np.exp((2.37403e6 + 0.33373e6 - (cvv_r-cvs_r)*TvT)*(1/TvT - 1/x)/Rr)\n",
+ " elif formula == \"standard-analytic\":\n",
+ " c1 = (cpv-ci)/Rv\n",
+ " c2 = lsT/(Rv*TvT) - c1\n",
+ " es = PvT * np.exp(c2*(1.-TvT/x)) * (x/TvT)**c1\n",
+ " else:\n",
+ " exit(\"formula not supported\")\n",
+ "\n",
+ " es = np.maximum(es,0)\n",
+ " if scalar_input:\n",
+ " return np.squeeze(es)\n",
+ " return es\n",
+ " \n",
+ "def esilf(T,formula=es_default):\n",
+ " import numpy as np\n",
+ " return np.minimum(esif(T,formula),eslf(T,formula))\n",
+ "\n",
+ "def es(T,formula=es_default,state='liq'):\n",
+ "\n",
+ " import numpy as np\n",
+ " \n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ "\n",
+ " if (state == 'liq'):\n",
+ " return eslf(x,formula)\n",
+ " if (state == 'ice'):\n",
+ " return esif(x,formula)\n",
+ " if (state == 'mxd'):\n",
+ " return esilf(x,formula)\n",
+ "\n",
+ "def des(T,formula=es_default,state='liq'):\n",
+ "\n",
+ " import numpy as np\n",
+ " \n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ " dx = 0.01; xp = x+dx/2; xm = x-dx/2\n",
+ " return (es(xp,formula,state)-es(xm,formula,state))/dx\n",
+ "\n",
+ "def dlnesdlnT(T,formula=es_default,state='liq'):\n",
+ "\n",
+ " import numpy as np\n",
+ " \n",
+ " x, scalar_input = thermo_input(T, 'Kelvin')\n",
+ " dx = 0.01; xp = x+dx/2; xm = x-dx/2\n",
+ " return ((es(xp,formula,state)-es(xm,formula,state))/es(x,formula,state) * (x/dx))\n",
+ " \n",
+ "def phase_change_enthalpy(Tx,fusion=False):\n",
+ " \"\"\" Returns the enthlapy [J/g] of vaporization (default) of water vapor or \n",
+ " (if fusion=True) the fusion anthalpy. Input temperature can be in degC or Kelvin\n",
+ " >>> phase_change_enthalpy(273.15)\n",
+ " 2500.8e3\n",
+ " \"\"\"\n",
+ " import numpy as np\n",
+ "\n",
+ " TC, scalar_input = thermo_input(Tx, 'Celcius')\n",
+ " TK, scalar_input = thermo_input(Tx, 'Kelvin')\n",
+ "\n",
+ " if (fusion):\n",
+ " el = lfT + (cl-ci)*(TK-TvT)\n",
+ " else:\n",
+ " el = lvT + (cpv-cl)*(TK-TvT)\n",
+ "\n",
+ " if scalar_input:\n",
+ " return np.squeeze(el)\n",
+ " return el\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Selected properties of IAWPS water ##\n",
+ "\n",
+ "These routines only provide information on $c_{p,\\mathrm{liq}}$ to a temperature of 253 K or -20$^\\circ$C, but already demonstrate its divergent behavior (exponential increase) with increased super-cooling. They also demonstrate the near linearity of $c_{p,\\mathrm{ice}}.$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using IAPWS Version 1.5.2\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/m219063/opt/miniforge3/lib/python3.9/site-packages/iapws/_iapws.py:124: UserWarning: Metastable ice\n",
+ " warnings.warn(\"Metastable ice\")\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'plot_dir' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 26>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m sns\u001b[38;5;241m.\u001b[39mdespine(offset\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[1;32m 25\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[0;32m---> 26\u001b[0m fig\u001b[38;5;241m.\u001b[39msavefig(\u001b[43mplot_dir\u001b[49m\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcp-Tdependance.pdf\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 28\u001b[0m TK \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m273.15\u001b[39m,\u001b[38;5;241m315.15\u001b[39m,\u001b[38;5;241m0.01\u001b[39m)\n\u001b[1;32m 29\u001b[0m es_iapws \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;28mlen\u001b[39m(TK))\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'plot_dir' is not defined"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 288x216 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import iapws\n",
+ "\n",
+ "print ('Using IAPWS Version %s\\n'%(iapws.__version__,))\n",
+ "T = np.arange(183.15,313.15)\n",
+ "ci_iapws = np.full(len(T),np.nan)\n",
+ "cl_iapws = np.full(len(T),np.nan)\n",
+ "for i,Tx in enumerate(T):\n",
+ " if (Tx < 283): ci_iapws[i] = iapws._iapws._Ice(Tx, 0.1)['cp']*1000 / ci\n",
+ " if (Tx > 253.15): cl_iapws[i] = iapws._iapws._Liquid(Tx, 0.1)['cp']*1000 / cl\n",
+ "\n",
+ "fig = plt.figure(figsize=(4,3))\n",
+ "\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_xlabel('$T$ / K')\n",
+ "ax1.set_ylabel('$c_\\mathrm{i}$ / %5.2f, $c_\\mathrm{l}$ / %5.2f'%(ci,cl))\n",
+ "ax1.set_xticks([185,247.07,273.15,305.00])\n",
+ "plt.scatter([247.065],[1.])\n",
+ "plt.scatter([305.000],[1.])\n",
+ "plt.plot(T,ci_iapws)\n",
+ "plt.plot(T,cl_iapws)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "fig.savefig(plot_dir+'cp-Tdependance.pdf')\n",
+ "\n",
+ "TK = np.arange(273.15,315.15,0.01)\n",
+ "es_iapws = np.zeros(len(TK))\n",
+ "for i, x in enumerate(TK):\n",
+ " es_iapws[i] = iapws.iapws97._PSat_T(x) *1.e6 #Temperature, [K]; Returns:Pressure, [MPa]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Comparison with Sea-Air-Ice library of Feistel et al (2010) v4.0.1\n",
+ "\n",
+ "Here we compare different thermodynamic constants or empirical formula to the IAPWS-10 and TEOS-10 standards taken from the Sea-Air-Ice library. These are calculated based on fits to potential functions as described above. The libraries are run off-line and the output is tabulated for comparison. The Feistel et al formulation extends to the IAPWS formulation shown above to allow for representations of liquid to the homogeneous freezing point of ice."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "sia_dir = './data/'\n",
+ "d0 = pd.read_csv(sia_dir + 'psat.dat',sep=' ',names=['T','Psat_liq','Psat_ice'])\n",
+ "x0 = d0.to_xarray()\n",
+ "d1 = pd.read_csv(sia_dir + 'cp-liq-vap.dat',sep=' ',names=['T','liq_density','vap_density','cp_liq','cp_vap','lv'])\n",
+ "x1 = d1.to_xarray()\n",
+ "d2 = pd.read_csv(sia_dir + 'cp-ice-vap.dat',sep=' ',names=['T','liq_density','vap_density','cp_ice','cp_vap','ls'])\n",
+ "x2 = d2.to_xarray()\n",
+ "\n",
+ "fig = plt.figure(figsize=(7,14/1.610834))\n",
+ "\n",
+ "ax1 = plt.subplot(2,1,2)\n",
+ "ax1.set_ylabel('difference / %')\n",
+ "ax1.set_xlabel('T / K')\n",
+ "\n",
+ "formula = 'wagner-pruss'\n",
+ "es_r = es(x0.T,formula=formula,state='ice')\n",
+ "diff = es_r/x0.Psat_ice\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',label=formula)\n",
+ "es_r = es(x0.T,formula=formula,state='liq')\n",
+ "diff = es_r/x0.Psat_liq\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',label=formula)\n",
+ "\n",
+ "formula = 'romps'\n",
+ "es_r = es(x0.T,formula=formula,state='ice')\n",
+ "diff = es_r/x0.Psat_ice\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',ls='dashed',label=formula)\n",
+ "es_r = es(x0.T,formula=formula,state='liq')\n",
+ "diff = es_r/x0.Psat_liq\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',ls='dashed',label=formula)\n",
+ "\n",
+ "formula = 'murphy-koop'\n",
+ "es_r = es(x0.T,formula=formula,state='ice')\n",
+ "diff = es_r/x0.Psat_ice\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',ls='dotted',label=formula)\n",
+ "es_r = es(x0.T,formula=formula,state='liq')\n",
+ "diff = es_r/x0.Psat_liq\n",
+ "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',ls='dotted',label=formula)\n",
+ "plt.legend(loc=\"best\",ncol=3)\n",
+ "\n",
+ "###\n",
+ "ax2 = plt.subplot(2,1,1)\n",
+ "ax2.set_ylabel('difference / %')\n",
+ "ax2.set_xlabel('T / K')\n",
+ "ax2.set_ylim(0.8,1.2)\n",
+ "\n",
+ "plt.plot(x1.T,x1.cp_vap/cpv,c='green',ls='solid',label='$c_{p,\\mathrm{vap}}$ (vap-liq)')\n",
+ "plt.plot(x2.T,x2.cp_vap/cpv,c='green',ls='dashed',label='$c_{p,\\mathrm{vap}}$ (vap-ice)')\n",
+ "\n",
+ "plt.plot(x1.T,x1.cp_liq/cl,c='orange',ls='solid',label='$c_{p,\\mathrm{liq}}$')\n",
+ "plt.plot(x2.T,x2.cp_ice/ci,c='dodgerblue',ls='solid',label='$c_{p,\\mathrm{ice}}$')\n",
+ "\n",
+ "lvx = phase_change_enthalpy(x1.T,fusion=False)\n",
+ "lsx = phase_change_enthalpy(x2.T,fusion=True) + phase_change_enthalpy(x2.T,fusion=False)\n",
+ "\n",
+ "plt.plot(x1.T,x1.lv/lvx,c='gray',ls='solid',label='$\\\\ell_v$')\n",
+ "plt.plot(x2.T,x2.ls/lsx,c='gray',ls='dashed',label='$\\\\ell_s$')\n",
+ "\n",
+ "plt.legend(loc=\"lower right\",ncol=3)\n",
+ "sns.set_context(\"paper\")\n",
+ "sns.despine(offset=10)\n",
+ "plt.tight_layout()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Comparing formulations of saturation vapor pressure ##\n",
+ "\n",
+ "This comparison of relative error suggests that the Wagner-Pru{\\ss}, Murphy and Koop, Hardy, and Sonntag formulations lie closest to the IAPWS-97 reference. Romps (2017) and Bolton (1980) are similarly accurate and may have advantages. Hardy is interesting as it appears in a technical document and is rarely mentioned in the subsequent literature, but used by Vaisala in the calibration of their sondes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Temperatures above the triple point ###"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "state = 'liq'\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_xlabel('$T$ / K')\n",
+ "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "es_ref = es_iapws\n",
+ "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "es_r = es(TK,formula='romps',state=state)\n",
+ "es_g = es(TK,formula='goff-gratch',state=state)\n",
+ "es_m = es(TK,formula='murphy-koop',state=state)\n",
+ "es_s = es(TK,formula='sonntag',state=state)\n",
+ "es_b = es(TK,formula='bolton',state=state)\n",
+ "es_f = es(TK,formula='flatau',state=state)\n",
+ "es_h = es(TK,formula='hardy98',state=state)\n",
+ "es_a = es(TK,formula='standard-analytic',state=state)\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_h/es_ref-1),c='tab:blue',ls='solid',label='Hardy (1998)')\n",
+ "plt.plot(TK,np.abs(es_f/es_ref-1),c='tab:orange',label='Flatau (1992)')\n",
+ "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(es_b/es_ref-1),c='tab:red',ls='dotted',label='Bolton (1980)')\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:brown',label='Wagner-Pruss (2002)')\n",
+ "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "plt.legend(loc=\"lower right\",ncol=3)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'es_l-error.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Extension to the freezing regime ###\n",
+ "\n",
+ "To extend over the entire temperature range a different reference is required, for this any of the Hardy, Sonntag, Murphy-Koop and Wagner-Pru{\\ss} formulations could suffice. We choose Wagner-Pru{\\ss} because Wagner's group is responsible for the standard, and has also developed the IAPWS standard for saturation vapor pressure over ice. Below the results are plooted with respect to this standard over a much larger temperature range.\n",
+ "\n",
+ "It is not clear how accurate Wagner and Pru{\\ss} wis hen extended well beyond the IAPWS range, based on which it might be that the grouping of errors of similar magnitude from the Bolton, Flatau and Goff-Gratch formulations are indicative of a low temperature bias in the Wagner-Pru{\\ss} formualtion. I doubt that this is the case, as the poor performance of all these formulations in the higher temperature range, and the simplicity of their formulation make it unlikely. The agreement of the Murphy-Koop formulation with these simpler formulations at low temperature may be indicative of Murphy and Koops focus on saturation over ice rather than liquid."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "state = 'liq'\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_xlabel('$T$ / K')\n",
+ "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "TK = np.arange(180,320,0.5)\n",
+ "\n",
+ "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "es_r = es(TK,formula='romps',state=state)\n",
+ "es_g = es(TK,formula='goff-gratch',state=state)\n",
+ "es_m = es(TK,formula='murphy-koop',state=state)\n",
+ "es_s = es(TK,formula='sonntag',state=state)\n",
+ "es_b = es(TK,formula='bolton',state=state)\n",
+ "es_f = es(TK,formula='flatau',state=state)\n",
+ "es_h = es(TK,formula='hardy98',state=state)\n",
+ "es_a = es(TK,formula='standard-analytic',state=state)\n",
+ "\n",
+ "es_ref = es_w\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_h/es_ref-1),c='tab:blue',ls='solid',label='Hardy (1998)')\n",
+ "plt.plot(TK,np.abs(es_f/es_ref-1),c='tab:orange',label='Flatau (1992)')\n",
+ "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(es_b/es_ref-1),c='tab:red',ls='dotted',label='Bolton (1980)')\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "#plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:olive',label='Wagner-Pruss (2002)')\n",
+ "\n",
+ "plt.legend(loc=\"lower left\",ncol=2)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'es_lsc-error.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Sublimation vapor pressure ##\n",
+ "\n",
+ "A subset of the formulations also postulate forms for the saturation vapor pressure over ice. For the reference in this quantity we use Wagner et al., (2011) as this has been adopted as the IAPWS standard. Here is seems that Murphy and Koop's (2005) formulation behaves very well in comparision to Wagner et al., but Sonntag is also quite adequate, particularly at lower ($T<273.15$ K) temperatures where it is likely to be applied."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "state = 'ice'\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_xlabel('$T$ / K')\n",
+ "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "TK = np.arange(180,320,0.5)\n",
+ "\n",
+ "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "es_r = es(TK,formula='romps',state=state)\n",
+ "es_g = es(TK,formula='goff-gratch',state=state)\n",
+ "es_m = es(TK,formula='murphy-koop',state=state)\n",
+ "es_s = es(TK,formula='sonntag',state=state)\n",
+ "es_a = es(TK,formula='standard-analytic',state=state)\n",
+ "es_ref = es_w\n",
+ "\n",
+ "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "#plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:olive',label='Wagner-Pruss (2002)')\n",
+ "\n",
+ "plt.legend(loc=\"lower left\",ncol=2)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'es_i-error.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Clausius Clapeyron ##\n",
+ "\n",
+ "Often over looked is that many conceptual models are built on the application of the Clausius-Clapeyron equation,\n",
+ "\\begin{equation} \n",
+ "\\frac{\\mathrm{d} \\ln e_\\mathrm{s}}{\\mathrm{d \\ln T}} \\left(\\frac{\\ell_\\mathrm{v}}{R_\\mathrm{v} T}\\right)^{-1} = 1 \n",
+ "\\end{equation}\n",
+ "with the assumption that the vaporization enthalpy, $\\ell_\\mathrm{v}$ that appears in this equation, is linear in temperature following Kirchoff's relation. This is similar to assuming that the specific heats are independent of temeprature, an idealization which is, unfortunately, just that, and idealization.\n",
+ "\n",
+ "But because of this it is interesting to compare this expression as given by the above formulation of the saturation vapor pressure (through their numerical derivative) and independent expressions of $\\ell_\\mathrm{v}$ based on the assumption of constant specific heats. \n",
+ "\n",
+ "This is shown below for ice and liquid saturation. The analytic expression, which has larger errors for es is constructued to satisfy this relationship and is exact to the precision of the numerical calculations. The various formulations using more accurate expressions for $e_s$ which implicityl don't assume constancy in specific heats are similarly accurate, with the exception of Goff-Gratch, and Romps for Ice. Hardy is only shown for water. For ice Sonntag does not behave well for $T> 290$ K, but it is not likely to be used at these temperatures. Note that Romps would be perfect had we adopted his modified specific heats.\n",
+ "\n",
+ "Based on the above my recommendation is to use the formulations by Wagner's group, unless one is interested in very low temperatures ($T<180$K) in which case the formulation of Koop and Murphy may be desirable. For just liquid processes Hardy might be a good choice, it is less well known but used by Vaisala for its sondes. There may be advantages to using Sonntag if there is interest in liquid and ice as it might allow more efficient implementations, but for my tests all formulations were within 30% of one another.\n",
+ "\n",
+ "Another alternative, would be to use the analytic approach, either using Romps' formulae if getthing the staturation vapor pressure as close to measurements as possible is preferred, or using the analytic formula with the correct (at the standard temperature and pressure) specific heats and gast constants."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "state = 'liq'\n",
+ "\n",
+ "fig = plt.figure(figsize=(10,10))\n",
+ "\n",
+ "ax1 = plt.subplot(2,1,1)\n",
+ "\n",
+ "ax1.set_ylabel('$|\\mathrm{CC}_\\mathrm{liq} - 1|$')\n",
+ "ax1.set_yscale('log')\n",
+ "ax1.set_xticklabels([])\n",
+ "\n",
+ "TK = np.arange(180,320,0.5)\n",
+ "\n",
+ "lv = phase_change_enthalpy(TK)\n",
+ "if (state == 'ice'): lv += phase_change_enthalpy(TK,fusion=True)\n",
+ "\n",
+ "y = lv/(Rv * TK)\n",
+ "cc_w = dlnesdlnT(TK,formula=\"wagner-pruss\",state=state) / y\n",
+ "cc_r = dlnesdlnT(TK,formula='romps',state=state) /y\n",
+ "cc_g = dlnesdlnT(TK,formula='goff-gratch',state=state) /y\n",
+ "cc_m = dlnesdlnT(TK,formula='murphy-koop',state=state) /y\n",
+ "cc_s = dlnesdlnT(TK,formula='sonntag',state=state) /y\n",
+ "cc_h = dlnesdlnT(TK,formula='hardy98',state=state) /y\n",
+ "cc_a = dlnesdlnT(TK,formula='standard-analytic',state=state) /y\n",
+ "\n",
+ "plt.plot(TK,np.abs(cc_h/1 -1.),c='tab:blue',label='Hardy (1998)')\n",
+ "plt.plot(TK,np.abs(cc_g/1 -1.),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(cc_r/1 -1.),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(cc_s/1 -1.),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(cc_m/1 -1.),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(cc_w/1 -1.),c='tab:olive',label='Wagner-Pruss (2002)')\n",
+ "plt.plot(TK,np.abs(cc_a/1 -1.),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "plt.legend(loc=\"lower left\",ncol=1)\n",
+ "\n",
+ "state = 'ice'\n",
+ "TK = np.arange(180,320,0.5)\n",
+ "\n",
+ "lv = phase_change_enthalpy(TK)\n",
+ "if (state == 'ice'): lv = phase_change_enthalpy(TK,fusion=True) + phase_change_enthalpy(TK)\n",
+ "\n",
+ "y = lv/(Rv * TK)\n",
+ "cc_w = dlnesdlnT(TK,formula=\"wagner-pruss\",state=state) / y\n",
+ "cc_r = dlnesdlnT(TK,formula='romps',state=state) /y\n",
+ "cc_g = dlnesdlnT(TK,formula='goff-gratch',state=state) /y\n",
+ "cc_m = dlnesdlnT(TK,formula='murphy-koop',state=state) /y\n",
+ "cc_s = dlnesdlnT(TK,formula='sonntag',state=state) /y\n",
+ "cc_a = dlnesdlnT(TK,formula='standard-analytic',state=state) /y\n",
+ "\n",
+ "ax2 = plt.subplot(2,1,2)\n",
+ "ax2.set_xlabel('$T$ / K')\n",
+ "ax2.set_ylabel('$|\\mathrm{CC}_\\mathrm{ice} - 1|$')\n",
+ "ax2.set_yscale('log')\n",
+ "\n",
+ "plt.plot(TK,np.abs(cc_g/1 -1.),c='tab:green',label='Goff-Gratch (1957)')\n",
+ "plt.plot(TK,np.abs(cc_r/1 -1.),c='tab:purple',label='Romps (2017)')\n",
+ "plt.plot(TK,np.abs(cc_s/1 -1.),c='tab:grey',label='Sonntag (1990)')\n",
+ "plt.plot(TK,np.abs(cc_m/1 -1.),c='tab:pink',label='Murphy-Koop (2005)')\n",
+ "plt.plot(TK,np.abs(cc_w/1 -1.),c='tab:olive',label='Wagner-Pruss (2002)')\n",
+ "plt.plot(TK,np.abs(cc_a/1. -1.),c='tab:purple',ls='dotted',label='Analytic')\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'cc-error.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Optimizing analytic fits for saturation and sublimation vapor pressure ##\n",
+ "\n",
+ "Romps suggests modifying the specific heats of liquid, ice and the gas constant of vapor to arrive at an optimal fit for the saturation vapor pressure using the analytic form. One can do almost as good by just modifying the specific heat of the condensate phases. Here we show how the maximum error in the fit depends on the specific heat of the condensate phases as compared to the reference, and how we arrive at our optimal fit by only manipulating the condensate phase specific heats to values that they anyway adopt within the range of temperatures spanned by the atmosphere. This justifys the default choice for saturation vapor pressure and the specific heats used in aes_thermo.py\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig = plt.figure(figsize=(10,5))\n",
+ "\n",
+ "cl_1 = (iapws._iapws._Liquid(265, 0.1)['cp'])*1000.\n",
+ "cl_2 = (iapws._iapws._Liquid(305, 0.1)['cp'])*1000\n",
+ "ci_1 = (iapws._iapws._Ice(193, 0.01)['cp'])*1000.\n",
+ "ci_2 = (iapws._iapws._Ice(273, 0.10)['cp'])*1000\n",
+ "\n",
+ "cls = np.arange(cl_2,cl_1)\n",
+ "err = np.zeros(len(cls))\n",
+ "\n",
+ "ax1 = plt.subplot(1,2,1)\n",
+ "ax1.set_xlabel('$c_\\mathrm{liq}$ / Jkg$^{-1}$K$^{-1}$')\n",
+ "ax1.set_ylabel('$(e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1)_\\mathrm{max}$ / %')\n",
+ "ax1.set_yscale('log')\n",
+ "\n",
+ "state = 'liq'\n",
+ "TK = np.arange(260,300,0.5)\n",
+ "es_ref = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "for i,cx in enumerate(cls):\n",
+ " c1 = (cpv-cx)/Rv\n",
+ " c2 = lvT/(Rv*TvT) - c1\n",
+ " es_a = PvT * np.exp(c2*(1.-TvT/TK)) * (TK/TvT)**c1\n",
+ " err[i] = np.max(np.abs(es_a/es_ref -1.))*100.\n",
+ "ax1.plot(cls,err,c='tab:purple',ls='dotted',label='Analytic $c_\\mathrm{liq}$ for $T\\in$ (260K,305K)')\n",
+ "ax1.legend(loc=\"upper left\",ncol=2)\n",
+ "\n",
+ "cis = np.arange(ci_1,ci_2)\n",
+ "err = np.zeros(len(cis))\n",
+ "\n",
+ "ax2 = plt.subplot(1,2,2)\n",
+ "ax2.set_xlabel('$c_\\mathrm{ice}$ / Jkg$^{-1}$K$^{-1}$')\n",
+ "ax2.set_ylabel('$(e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1)_\\mathrm{max}$ / %')\n",
+ "ax2.set_yscale('log')\n",
+ "\n",
+ "state = 'ice'\n",
+ "TK = np.arange(180,273,0.5)\n",
+ "es_ref = es(TK,formula=\"wagner-pruss\",state=state)\n",
+ "for i,cx in enumerate(cis):\n",
+ " c1 = (cpv-cx)/Rv\n",
+ " c2 = lsT/(Rv*TvT) - c1\n",
+ " es_a = PvT * np.exp(c2*(1.-TvT/TK)) * (TK/TvT)**c1\n",
+ " err[i] = np.max(np.abs(es_a/es_ref -1.))*100.\n",
+ "ax2.plot(cis,err,c='tab:purple',ls='dotted',label='Analytic $c_\\mathrm{ice}$ for $T\\in$ (193K,273K)')\n",
+ "ax2.legend(loc=\"upper right\",ncol=2)\n",
+ "\n",
+ "sns.set_context(\"paper\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "\n",
+ "fig.savefig(plot_dir+'es-analytic-fits.pdf')\n",
+ "Tfit = 305\n",
+ "print ('Taking fit for $c_\\mathrm{liq}=$ %3.2f J/(kg K) at $T=$ %3.2f K'%(iapws._iapws._Liquid(Tfit, 0.1)['cp']*1000.,Tfit))\n",
+ "Tfit = 247.065\n",
+ "print ('Taking fit for $c_\\mathrm{ice}=$ %3.2f J/(kg K) at $T=$ %3.2f K'%(iapws._iapws._Ice(Tfit, 0.1)['cp']*1000.,Tfit))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RCEMIP comparision ##\n",
+ "\n",
+ "During RCEMIP (Wing et al.) different models output different RH, differing in ways of calculating it and also whether or not it was calculated relative to liquid or ice. In this analysis we create a python implementation of the intial RCEMIP sounding and then for the given state estimate the RH using different formulat and different assumptions regarding the reference condensate (liquid/ice). We also show the difference associated with 1 K of temperature. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def rcemip_on_z(z,SST):\n",
+ " # function [T,q,p] = rcemip_on_z(z,SST)\n",
+ " # \n",
+ " # Inputs:\n",
+ " # z: array of heights (low to high, m)\n",
+ " # SST: sea surface temperature (K)\n",
+ " # \n",
+ " # Outputs:\n",
+ " T = np.zeros(len(z)) # temperature (K)\n",
+ " q = np.zeros(len(z)) # specific humidity (g/g)\n",
+ " p = np.zeros(len(z)) # pressure (Pa)\n",
+ "\n",
+ " ## Constants\n",
+ " g = 9.79764 #m/s^2\n",
+ " Rd = 287.04 #J/kgK\n",
+ " \n",
+ " ## Parameters\n",
+ " p0 = 101480 #Pa surface pressure\n",
+ " qt = 10**(-11) #g/g specific humidity at tropopause\n",
+ " zq1 = 4000 #m\n",
+ " zq2 = 7500 #m\n",
+ " zt = 15000 #m tropopause height\n",
+ " gamma = 0.0067 #K/m lapse rate\n",
+ " \n",
+ " ## Scratch\n",
+ " Tv = np.zeros(len(z)) # temperature (K)\n",
+ "\n",
+ " if SST == 295:\n",
+ " q0 = 0.01200; #g/g specific humidity at surface (adjusted from 300K value so RH near surface approx 80%)\n",
+ " elif SST == 300:\n",
+ " q0 = 0.01865; #g/g specific humidity at surface\n",
+ " elif SST == 305:\n",
+ " q0 = 0.02400 #g/g specific humidity at surface (adjusted from 300K value so RH near surface approx 80%)\n",
+ " \n",
+ " T0 = SST - 0 #surface air temperature adjusted to be 0K less than SST\n",
+ " \n",
+ " ## Virtual Temperature at surface and tropopause\n",
+ " Tv0 = T0*(1 + 0.608*q0) #virtual temperature at surface\n",
+ " Tvt = Tv0 - gamma*zt #virtual temperature at tropopause z=zt\n",
+ " \n",
+ " ## Pressure\n",
+ " pt = p0*(Tvt/Tv0)**(g/(Rd*gamma)); #pressure at tropopause z=zt\n",
+ " p = p0*((Tv0-gamma*z)/Tv0)**(g/(Rd*gamma)) #0 <= z <= zt\n",
+ " p[z>zt] = pt*np.exp(-g*(z[z>zt]-zt)/(Rd*Tvt)) #z > zt\n",
+ " \n",
+ " ## Specific humidity\n",
+ " q = q0*np.exp(-z/zq1)*np.exp(-(z/zq2)**2)\n",
+ " q[z>zt] = qt #z > zt\n",
+ " \n",
+ " ## Temperature\n",
+ " #Virtual Temperature\n",
+ " Tv = Tv0 - gamma*z #0 <= z <= zt\n",
+ " Tv[z>zt] = Tvt #z > zt\n",
+ " \n",
+ " #Absolute Temperature at all heights\n",
+ " T = Tv/(1 + 0.608*q)\n",
+ " \n",
+ " return T, q, p\n",
+ "\n",
+ "z = np.arange(0,17000,100)\n",
+ "T, q , p = rcemip_on_z(z,300)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_rh (T,q,p,formula='wagner-pruss',state='liq'):\n",
+ " es_w = es(T,formula=formula,state=state)\n",
+ " x = es_w * eps1/(p-es_w)\n",
+ " return 100.*q*(1+x)/x\n",
+ " \n",
+ "fig = plt.figure(figsize=(4,5))\n",
+ "\n",
+ "ax1 = plt.subplot(1,1,1)\n",
+ "ax1.set_ylabel('$z$ / km')\n",
+ "ax1.set_xlabel('RH / %')\n",
+ "ax1.set_ylim(0,14.5)\n",
+ "ax1.set_yticks([0,4,8,12])\n",
+ "\n",
+ "plt.plot(get_rh(T,q,p,state='mxd'),z/1000.,label = 'Wagner Pruss (ice/liq)')\n",
+ "plt.plot(get_rh(T+1,q,p,state='mxd'),z/1000.,label = 'Wagner Pruss (ice/liq) + 1 K')\n",
+ "plt.plot(get_rh(T,q,p,state='ice'),z/1000.,label = 'Wagner Pruss (ice)')\n",
+ "plt.plot(get_rh(T,q,p,formula='romps',state='mxd'),z/1000.,label = 'Romps (ice/liq)')\n",
+ "plt.plot(get_rh(T,q,p),z/1000.,label = 'Wagner Pruss (liq)')\n",
+ "plt.plot(get_rh(T,q,p,formula='flatau'),z/1000.,label = 'Flatau (liq)')\n",
+ "\n",
+ "plt.legend(loc=\"lower left\",ncol=1)\n",
+ "\n",
+ "sns.set_context(\"paper\")\n",
+ "sns.despine(offset=10)\n",
+ "plt.tight_layout()\n",
+ "\n",
+ "fig.savefig(plot_dir+'RCEMIP-RHerror.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## lifting-condensation level approximations\n",
+ "\n",
+ "Here we compare the LCL base predictions to those proposed by Romps and Bolton as well as the differences between density potential temperatures.\n",
+ "\n",
+ "For the estimation of the LCL we modify the Romps expressions (using his code) to output pressure at the LCL, as this eliminates an assumption as to how pressure is distributed in the atmosphere, and thus only depends on the parcel state. What we find is that the much simpler Bolton expression is as good as the more complex expression by Romps, and differences between the two are commensurate with those arising from slight differences in how the saturation vapor pressure is calculated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Version 1.0 released by David Romps on September 12, 2017.\n",
+ "# \n",
+ "# When using this code, please cite:\n",
+ "# \n",
+ "# @article{16lcl,\n",
+ "# Title = {Exact expression for the lifting condensation level},\n",
+ "# Author = {David M. Romps},\n",
+ "# Journal = {Journal of the Atmospheric Sciences},\n",
+ "# Year = {2017},\n",
+ "# Volume = {in press},\n",
+ "# }\n",
+ "#\n",
+ "# This lcl function returns the height of the lifting condensation level\n",
+ "# (LCL) in meters. The inputs are:\n",
+ "# - p in Pascals\n",
+ "# - T in Kelvins\n",
+ "# - Exactly one of rh, rhl, and rhs (dimensionless, from 0 to 1):\n",
+ "# * The value of rh is interpreted to be the relative humidity with\n",
+ "# respect to liquid water if T >= 273.15 K and with respect to ice if\n",
+ "# T < 273.15 K. \n",
+ "# * The value of rhl is interpreted to be the relative humidity with\n",
+ "# respect to liquid water\n",
+ "# * The value of rhs is interpreted to be the relative humidity with\n",
+ "# respect to ice\n",
+ "# - ldl is an optional logical flag. If true, the lifting deposition\n",
+ "# level (LDL) is returned instead of the LCL. \n",
+ "# - min_lcl_ldl is an optional logical flag. If true, the minimum of the\n",
+ "# LCL and LDL is returned.\n",
+ "\n",
+ "def lcl(p,T,rh=None,rhl=None,rhs=None,return_ldl=False,return_min_lcl_ldl=False):\n",
+ "\n",
+ " import math\n",
+ " import scipy.special\n",
+ "\n",
+ " # Parameters\n",
+ " Ttrip = 273.16 # K\n",
+ " ptrip = 611.65 # Pa\n",
+ " E0v = 2.3740e6 # J/kg\n",
+ " E0s = 0.3337e6 # J/kg\n",
+ " ggr = 9.81 # m/s^2\n",
+ " rgasa = 287.04 # J/kg/K \n",
+ " rgasv = 461 # J/kg/K \n",
+ " cva = 719 # J/kg/K\n",
+ " cvv = 1418 # J/kg/K \n",
+ " cvl = 4119 # J/kg/K \n",
+ " cvs = 1861 # J/kg/K \n",
+ " cpa = cva + rgasa\n",
+ " cpv = cvv + rgasv\n",
+ "\n",
+ " # The saturation vapor pressure over liquid water\n",
+ " def pvstarl(T):\n",
+ " return ptrip * (T/Ttrip)**((cpv-cvl)/rgasv) * math.exp( (E0v - (cvv-cvl)*Ttrip) / rgasv * (1/Ttrip - 1/T) )\n",
+ " # The saturation vapor pressure over solid ice\n",
+ " def pvstars(T):\n",
+ " return ptrip * (T/Ttrip)**((cpv-cvs)/rgasv) * math.exp( (E0v + E0s - (cvv-cvs)*Ttrip) / rgasv * (1/Ttrip - 1/T)) \n",
+ "\n",
+ " # Calculate pv from rh, rhl, or rhs\n",
+ " rh_counter = 0\n",
+ " if rh is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rhl is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rhs is not None:\n",
+ " rh_counter = rh_counter + 1\n",
+ " if rh_counter != 1:\n",
+ " print(rh_counter)\n",
+ " exit('Error in lcl: Exactly one of rh, rhl, and rhs must be specified')\n",
+ " if rh is not None:\n",
+ " # The variable rh is assumed to be \n",
+ " # with respect to liquid if T > Ttrip and \n",
+ " # with respect to solid if T < Ttrip\n",
+ " if T > Ttrip:\n",
+ " pv = rh * pvstarl(T)\n",
+ " else:\n",
+ " pv = rh * pvstars(T)\n",
+ " rhl = pv / pvstarl(T)\n",
+ " rhs = pv / pvstars(T)\n",
+ " elif rhl is not None:\n",
+ " pv = rhl * pvstarl(T)\n",
+ " rhs = pv / pvstars(T)\n",
+ " if T > Ttrip:\n",
+ " rh = rhl\n",
+ " else:\n",
+ " rh = rhs\n",
+ " elif rhs is not None:\n",
+ " pv = rhs * pvstars(T)\n",
+ " rhl = pv / pvstarl(T)\n",
+ " if T > Ttrip:\n",
+ " rh = rhl\n",
+ " else:\n",
+ " rh = rhs\n",
+ " if pv > p:\n",
+ " return N\n",
+ "\n",
+ "# Calculate lcl_liquid and lcl_solid\n",
+ " qv = rgasa*pv / (rgasv*p + (rgasa-rgasv)*pv)\n",
+ " rgasm = (1-qv)*rgasa + qv*rgasv\n",
+ " cpm = (1-qv)*cpa + qv*cpv\n",
+ " if rh == 0:\n",
+ " return cpm*T/ggr\n",
+ " aL = -(cpv-cvl)/rgasv + cpm/rgasm\n",
+ " bL = -(E0v-(cvv-cvl)*Ttrip)/(rgasv*T)\n",
+ " cL = pv/pvstarl(T)*math.exp(-(E0v-(cvv-cvl)*Ttrip)/(rgasv*T))\n",
+ " aS = -(cpv-cvs)/rgasv + cpm/rgasm\n",
+ " bS = -(E0v+E0s-(cvv-cvs)*Ttrip)/(rgasv*T)\n",
+ " cS = pv/pvstars(T)*math.exp(-(E0v+E0s-(cvv-cvs)*Ttrip)/(rgasv*T))\n",
+ " X = bL/(aL*scipy.special.lambertw(bL/aL*cL**(1/aL),-1).real)\n",
+ " Y = bS/(aS*scipy.special.lambertw(bS/aS*cS**(1/aS),-1).real) \n",
+ " \n",
+ " lcl = cpm*T/ggr*( 1 - X)\n",
+ " ldl = cpm*T/ggr*( 1 - Y)\n",
+ "\n",
+ " # Modifications of the code to output Plcl or Pldl\n",
+ " Plcl = PPa * X**(cpm/rgasm)\n",
+ " Pldl = PPa * X**(cpm/rgasm)\n",
+ " # Return either lcl or ldl\n",
+ " if return_ldl and return_min_lcl_ldl:\n",
+ " exit('return_ldl and return_min_lcl_ldl cannot both be true')\n",
+ " elif return_ldl:\n",
+ " return Pldl\n",
+ " elif return_min_lcl_ldl:\n",
+ " return min(Plcl,Pldl)\n",
+ " else:\n",
+ " return Plcl"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import aes_thermo as mt\n",
+ "PPa = 101325.\n",
+ "\n",
+ "qt = np.arange(2.5e-3,8e-3,0.2e-3)\n",
+ "TK = 285.\n",
+ "Plcl_X = mt.plcl(TK,PPa,qt)\n",
+ "Plcl_B = mt.plcl_boloton(TK,PPa,qt)\n",
+ "Plcl_R = np.zeros(len(Plcl_X))\n",
+ "\n",
+ "for i,x in enumerate(qt):\n",
+ " if (x>0.1): x = x/1000.\n",
+ " RH = mt.mr2pp(x/(1.-x),PPa)/mt.es(TK)\n",
+ " Plcl_R[i] = lcl(PPa,TK,RH)\n",
+ "\n",
+ "del1 = (Plcl_B-Plcl_X)/100.\n",
+ "del2 = (Plcl_R-Plcl_X)/100.\n",
+ "\n",
+ "fig = plt.figure(figsize=(10,5))\n",
+ "ax1 = plt.subplot(1,2,1)\n",
+ "ax1.set_ylabel('$P$ / hPa')\n",
+ "ax1.set_xlabel('$q_\\mathrm{t}$ / g/kg')\n",
+ "#ax1.set_ylim(-1.2,1.2)\n",
+ "\n",
+ "plt.plot(qt*1.e3,del1,label='$\\\\delta_\\mathrm{B}$, $T$=285K')\n",
+ "plt.plot(qt*1.e3,del2,label='$\\\\delta_\\mathrm{R}$, $T$=285K')\n",
+ "#plt.gca().invert_yaxis()\n",
+ "plt.legend(loc=\"best\")\n",
+ "\n",
+ "qt = np.arange(0.5e-3,28e-3,0.2e-3)\n",
+ "TK = 310.\n",
+ "Plcl_X = mt.get_Plcl(TK,PPa,qt,iterate=True)\n",
+ "Plcl_B = mt.get_Plcl(TK,PPa,qt)\n",
+ "Plcl_R = np.zeros(len(Plcl_X))\n",
+ "\n",
+ "for i,x in enumerate(qt):\n",
+ " if (x>0.1): x = x/1000.\n",
+ " RH = mt.mr2pp(x/(1.-x),PPa)/mt.es(TK)\n",
+ " Plcl_R[i] = lcl(PPa,TK,RH)\n",
+ "\n",
+ "del1 = (Plcl_B-Plcl_X)/100.\n",
+ "del2 = (Plcl_R-Plcl_X)/100.\n",
+ "\n",
+ "ax2 = plt.subplot(1,2,2)\n",
+ "ax2.set_xlabel('$q_\\mathrm{t}$ / g/kg')\n",
+ "#ax2.set_ylim(-1.2,1.2)\n",
+ "ax2.set_yticklabels([])\n",
+ "\n",
+ "plt.plot(qt*1.e3,del1,label='$\\\\delta_\\mathrm{B}$, $T$=310K')\n",
+ "plt.plot(qt*1.e3,del2,label='$\\\\delta_\\mathrm{R}$, $T$=310K')\n",
+ "#plt.gca().invert_yaxis()\n",
+ "plt.legend(loc=\"best\")\n",
+ "\n",
+ "sns.set_context(\"talk\", font_scale=1.2)\n",
+ "sns.despine(offset=10)\n",
+ "fig.savefig(plot_dir+'Plcl.pdf')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Acknowledgments ##\n",
+ "\n",
+ "Jiawei Bao, Geet George, and Hauke Schulz are thanked for comments on these notes, and the identification of some errors in earlier versions. Axel Seifert is thanked for his comments and insights, and for pointing out the TEOS routines of Feisel et al. (2010) which extend the IAPWS libraries."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/moist_thermodynamics/saturation-water-vapor.ipynb b/moist_thermodynamics/saturation-water-vapor.ipynb
deleted file mode 100644
index 6a3e376..0000000
--- a/moist_thermodynamics/saturation-water-vapor.ipynb
+++ /dev/null
@@ -1,1202 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Notes on calculations of vapor pressures and the specific heats #\n",
- "\n",
- "These notes do not use the moist_thermodynamic libararies, rather they were the basis for the choice for the particular formulations for the specific heats and saturation vapor pressures within that library.\n",
- "\n",
- "There are a large number of expressions for the saturation and sublimation vapor pressure in the literature, and many of these, even recent ones, seem to reference previous studies in a haphazard way. So how much do these differ, is there a standard, and by what criteria should one judge them by. Here I try to develop an intuition for the answers.\n",
- "\n",
- "The first thing to note is that there is a community that concerns itself with this question. They call themselves the international association for the physical properties of water and steam, and mostly concern themselves with the behavior of water at high temperature. The approach of the IAPWS is to develop an empirical equation of state for water, in the form of a specification of its Helmholtz free energy, or potential, from which all other properties can be derived. The standard reference for the IAPWS equation of state is the publication by Wagner and Pru{\\ss} (Thermodynamic Properties of Ordinary Water) published in 2002 and which describes the IAPWS-95 approved formulation. Minor corrections have since been made to this, which as best I can tell are relevant at high temperatures. The most substantial change has been the TEOS-10 work by Rainer Feistel of IOW, which extends these approaches to composite systems, thereby allowing for representations of sea-water and moist air. By working with an equation of state, all properties of water, from the specific heats to the gas constants to the phase-change enthalpies can be derived consistently. The disadvantage of this approach is that the equation is derived by positing an analytic form that is then fit to a very wide and diverse abundance of existing data. The resultant equation is described in an ideal part, which involves a summation of nine terms and thirteen coefficients, and a residual part, with more than 50 terms and over 200 constants.\n",
- "\n",
- "For the case of the saturation vapor pressure over water Wagner and Pru{\\ss} suggest a much simpler equation that is described in terms of only six coefficients. First, below I compare the relative error to the IAPWS standard as has been formlated and distributed in the iapws python package, version (1.4). There has been some discussion on the web of its implementation, but the similarity with the Wagner and Pru{\\ss} formulation gives me confidence. Next we look at the TEOS-10 Sea-Ice-Air formulations of Feistel et al (Ocean Sci., 6, 91–141, 2010) which are distributed as FORTRAN90 code which I downloaded, ran, and tabulated to assess some empirical fits later used as a reference.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "zsh:fg:1: no job control in this shell.\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import seaborn as sns\n",
- "from scipy import interpolate, optimize\n",
- "\n",
- "plot_dir = './plots/'\n",
- "\n",
- "!%matplotlib inline"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "gravity = 9.8076\n",
- "\n",
- "cpd = 1006.\n",
- "Rd = 287.05\n",
- "\n",
- "Rv = 461.53 # IAPWS97 at 273.15\n",
- "cpv = 1865.01 # ''\n",
- "lv0 = 2500.93e3 # IAPWS97 at 273.15\n",
- "lf0 = 333.42e3 #''\n",
- "\n",
- "cl = 4179.57 # IAPWS97 at 305 and P=0.1 MPa (chosen to give a good fit for es over ice)\n",
- "ci = 1905.43 # IAPWS97 at 247.065 and P=0.1 MPa (chosen to give a good fit for es over ice)\n",
- "\n",
- "eps1 = Rd/Rv\n",
- "eps2 = Rv/Rd -1.\n",
- "\n",
- "P0 = 100000. # Standard Pressure\n",
- "T0 = 273.15 # Standard Temperature\n",
- "PvC = 22.064e6 # Critical pressure of water vapor\n",
- "TvC = 647.096 # Critical temperature of water vapor\n",
- "TvT = 273.16 # Triple point temperature of water\n",
- "PvT = 611.655\n",
- "lvT = lv0 + (cpv-cl)*(TvT-T0)\n",
- "lfT = lf0 + (cpv-ci)*(TvT-T0)\n",
- "lsT = lvT + lfT\n",
- "\n",
- "es_default = 'sonntag'\n",
- "\n",
- "def thermo_input(x, xtype='none'):\n",
- " \n",
- " import numpy as np\n",
- "\n",
- " x = np.asarray(x).flatten()\n",
- " scalar_input = False\n",
- " if x.ndim == 0:\n",
- " x = x[None] # Makes x 1D\n",
- " scalar_input = True\n",
- "\n",
- " if (xtype == 'Kelvin' and x.max() < 100 ): x = x+273.15\n",
- " if (xtype == 'Celcius'and x.max() > 100 ): x = x-273.15\n",
- " if (xtype == 'Pascal' and x.max() < 1200): x = x*100.\n",
- " if (xtype == 'kg/kg' and x.max() > 1.0) : x = x/1000.\n",
- " if (xtype == 'meter' and x.max() < 10.0): print('Warning: input should be in meters, max value less than 10, not corrected')\n",
- "\n",
- " return x, scalar_input\n",
- "\n",
- "def eslf(T, formula=es_default):\n",
- " \"\"\" Returns the saturation vapour pressure [Pa] over liquid given \n",
- "\tthe temperature. Temperatures can be in Celcius or Kelvin.\n",
- "\tFormulas supported are\n",
- "\t - Goff-Gratch (1994 Smithsonian Tables)\n",
- "\t - Sonntag (1994) \n",
- "\t - Flatau\n",
- "\t - Magnus Tetens (MT)\n",
- " - Romps (2017)\n",
- " - Murphy-Koop\n",
- " - Bolton\n",
- " - Wagner and Pruss (WP, 2002) is the default\n",
- "\t>>> eslf(273.16)\n",
- "\t611.657\n",
- " \"\"\"\n",
- " import numpy as np\n",
- "\n",
- " x, scalar_input = thermo_input(T, 'Kelvin')\n",
- "\n",
- " if formula == \"flatau\":\n",
- " if (np.min(x) > 100): x = x-273.16\n",
- " np.maximum(x,-80.)\n",
- " c_es= np.asarray([0.6105851e+03, 0.4440316e+02, 0.1430341e+01, 0.2641412e-01,\n",
- " 0.2995057e-03,0.2031998e-05,0.6936113e-08,0.2564861e-11,-0.3704404e-13])\n",
- " es = np.polyval(c_es[::-1],x)\n",
- " elif formula == \"bolton\":\n",
- " if (np.min(x) > 100): x = x-273.15\n",
- " es = 611.2*np.exp((17.67*x)/(243.5+x))\n",
- " elif formula == \"sonntag\":\n",
- " xx = -6096.9385/x + 16.635794 - 2.711193e-2*x + 1.673952e-5*x*x + 2.433502 * np.log(x)\n",
- " es = 100.*np.exp(xx)\n",
- " elif formula =='goff-gratch':\n",
- " x1 = 273.16/x\n",
- " x2 = 373.16/x\n",
- " xl = np.log10(1013.246 ) - 7.90298*(x2 - 1) + 5.02808*np.log10(x2) - 1.3816e-7*(10**(11.344*(1.-1./x2)) - 1.0) + 8.1328e-3 * (10**(-3.49149*(x2-1)) - 1.0)\n",
- " es =10**(xl+2) # plus 2 converts from hPa to Pa\n",
- " elif formula == 'wagner-pruss':\n",
- " vt = 1.-x/TvC\n",
- " es = PvC * np.exp(TvC/x * (-7.85951783*vt + 1.84408259*vt**1.5 - 11.7866497*vt**3 + 22.6807411*vt**3.5 - 15.9618719*vt**4 + 1.80122502*vt**7.5))\n",
- " elif formula == 'hardy98':\n",
- " y = -2.8365744e+3/(x*x) - 6.028076559e+3/x + 19.54263612 - 2.737830188e-2*x + 1.6261698e-5*x**2 + 7.0229056e-10*x**3 - 1.8680009e-13*x**4 + 2.7150305 * np.log(x)\n",
- " es = np.exp(y)\n",
- " elif formula == 'romps':\n",
- " Rr = 461.\n",
- " cvl_r = 4119\n",
- " cvv_r = 1418\n",
- " cpv_r = cvv_r + Rr\n",
- " es = 611.65 * (x/TvT) **((cpv_r-cvl_r)/Rr) * np.exp((2.37403e6 - (cvv_r-cvl_r)*TvT)*(1/TvT - 1/x)/Rr)\n",
- " elif formula == \"murphy-koop\":\n",
- " es = np.exp(54.842763 - 6763.22/x - 4.210*np.log(x) + 0.000367*x + np.tanh(0.0415*(x - 218.8)) * (53.878 - 1331.22/x - 9.44523 * np.log(x) + 0.014025*x))\n",
- " elif formula == \"standard-analytic\":\n",
- " c1 = (cpv-cl)/Rv\n",
- " c2 = lvT/(Rv*TvT) - c1\n",
- " es = PvT * np.exp(c2*(1.-TvT/x)) * (x/TvT)**c1\n",
- " else:\n",
- " exit(\"formula not supported\")\n",
- "\n",
- " es = np.maximum(es,0)\n",
- " if scalar_input:\n",
- " return np.squeeze(es)\n",
- " return es\n",
- "\n",
- "def esif(T, formula=es_default):\n",
- " \"\"\" Returns the saturation vapour pressure [Pa] over ice given \n",
- "\tthe temperature. Temperatures can be in Celcius or Kelvin.\n",
- "\tuses the Goff-Gratch (1994 Smithsonian Tables) formula\n",
- "\t>>> esli(273.15)\n",
- "\t6.112\n",
- "m \"\"\"\n",
- " import numpy as np\n",
- "\n",
- " x, scalar_input = thermo_input(T, 'Kelvin')\n",
- "\n",
- " if formula == \"sonntag\":\n",
- " es = 100 * np.exp(24.7219 - 6024.5282/x + 0.010613868*x - 0.000013198825*x**2 - 0.49382577*np.log(x))\n",
- " elif formula == \"goff-gratch\":\n",
- " x1 = 273.16/x\n",
- " xi = np.log10( 6.1071) - 9.09718*(x1 - 1) - 3.56654*np.log10(x1) + 0.876793*(1 - 1./x1)\n",
- " es = 10**(xi+2)\n",
- " elif formula == \"wagner-pruss\": #(actually wagner et al, 2011)\n",
- " a1 = -0.212144006e+2\n",
- " a2 = 0.273203819e+2\n",
- " a3 = -0.610598130e+1\n",
- " b1 = 0.333333333e-2\n",
- " b2 = 0.120666667e+1\n",
- " b3 = 0.170333333e+1\n",
- " theta = T/TvT\n",
- " es = PvT * np.exp((a1*theta**b1 + a2 * theta**b2 + a3 * theta**b3)/theta)\n",
- " elif formula == \"murphy-koop\":\n",
- " es = np.exp(9.550426 - 5723.265/x + 3.53068 * np.log(x) - 0.00728332*x)\n",
- " elif formula == \"romps\":\n",
- " Rr = 461.\n",
- " cvv_r = 1418.\n",
- " cvs_r = 1861.\n",
- " cpv_r = cvv_r + Rr\n",
- " es = 611.65 * (x/TvT) **((cpv_r-cvs_r)/Rr) * np.exp((2.37403e6 + 0.33373e6 - (cvv_r-cvs_r)*TvT)*(1/TvT - 1/x)/Rr)\n",
- " elif formula == \"standard-analytic\":\n",
- " c1 = (cpv-ci)/Rv\n",
- " c2 = lsT/(Rv*TvT) - c1\n",
- " es = PvT * np.exp(c2*(1.-TvT/x)) * (x/TvT)**c1\n",
- " else:\n",
- " exit(\"formula not supported\")\n",
- "\n",
- " es = np.maximum(es,0)\n",
- " if scalar_input:\n",
- " return np.squeeze(es)\n",
- " return es\n",
- " \n",
- "def esilf(T,formula=es_default):\n",
- " import numpy as np\n",
- " return np.minimum(esif(T,formula),eslf(T,formula))\n",
- "\n",
- "def es(T,formula=es_default,state='liq'):\n",
- "\n",
- " import numpy as np\n",
- " \n",
- " x, scalar_input = thermo_input(T, 'Kelvin')\n",
- "\n",
- " if (state == 'liq'):\n",
- " return eslf(x,formula)\n",
- " if (state == 'ice'):\n",
- " return esif(x,formula)\n",
- " if (state == 'mxd'):\n",
- " return esilf(x,formula)\n",
- "\n",
- "def des(T,formula=es_default,state='liq'):\n",
- "\n",
- " import numpy as np\n",
- " \n",
- " x, scalar_input = thermo_input(T, 'Kelvin')\n",
- " dx = 0.01; xp = x+dx/2; xm = x-dx/2\n",
- " return (es(xp,formula,state)-es(xm,formula,state))/dx\n",
- "\n",
- "def dlnesdlnT(T,formula=es_default,state='liq'):\n",
- "\n",
- " import numpy as np\n",
- " \n",
- " x, scalar_input = thermo_input(T, 'Kelvin')\n",
- " dx = 0.01; xp = x+dx/2; xm = x-dx/2\n",
- " return ((es(xp,formula,state)-es(xm,formula,state))/es(x,formula,state) * (x/dx))\n",
- " \n",
- "def phase_change_enthalpy(Tx,fusion=False):\n",
- " \"\"\" Returns the enthlapy [J/g] of vaporization (default) of water vapor or \n",
- " (if fusion=True) the fusion anthalpy. Input temperature can be in degC or Kelvin\n",
- " >>> phase_change_enthalpy(273.15)\n",
- " 2500.8e3\n",
- " \"\"\"\n",
- " import numpy as np\n",
- "\n",
- " TC, scalar_input = thermo_input(Tx, 'Celcius')\n",
- " TK, scalar_input = thermo_input(Tx, 'Kelvin')\n",
- "\n",
- " if (fusion):\n",
- " el = lfT + (cl-ci)*(TK-TvT)\n",
- " else:\n",
- " el = lvT + (cpv-cl)*(TK-TvT)\n",
- "\n",
- " if scalar_input:\n",
- " return np.squeeze(el)\n",
- " return el\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Selected properties of IAWPS water ##\n",
- "\n",
- "These routines only provide information on $c_{p,\\mathrm{liq}}$ to a temperature of 253 K or -20$^\\circ$C, but already demonstrate its divergent behavior (exponential increase) with increased super-cooling. They also demonstrate the near linearity of $c_{p,\\mathrm{ice}}.$"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Using IAPWS Version 1.5.2\n",
- "\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/m219063/opt/miniforge3/lib/python3.9/site-packages/iapws/_iapws.py:124: UserWarning: Metastable ice\n",
- " warnings.warn(\"Metastable ice\")\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x216 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import iapws\n",
- "\n",
- "print ('Using IAPWS Version %s\\n'%(iapws.__version__,))\n",
- "T = np.arange(183.15,313.15)\n",
- "ci_iapws = np.full(len(T),np.nan)\n",
- "cl_iapws = np.full(len(T),np.nan)\n",
- "for i,Tx in enumerate(T):\n",
- " if (Tx < 283): ci_iapws[i] = iapws._iapws._Ice(Tx, 0.1)['cp']*1000 / ci\n",
- " if (Tx > 253.15): cl_iapws[i] = iapws._iapws._Liquid(Tx, 0.1)['cp']*1000 / cl\n",
- "\n",
- "fig = plt.figure(figsize=(4,3))\n",
- "\n",
- "ax1 = plt.subplot(1,1,1)\n",
- "ax1.set_xlabel('$T$ / K')\n",
- "ax1.set_ylabel('$c_\\mathrm{i}$ / %5.2f, $c_\\mathrm{l}$ / %5.2f'%(ci,cl))\n",
- "ax1.set_xticks([185,247.07,273.15,305.00])\n",
- "plt.scatter([247.065],[1.])\n",
- "plt.scatter([305.000],[1.])\n",
- "plt.plot(T,ci_iapws)\n",
- "plt.plot(T,cl_iapws)\n",
- "\n",
- "sns.set_context(\"paper\", font_scale=1.2)\n",
- "sns.despine(offset=10)\n",
- "\n",
- "plt.tight_layout()\n",
- "fig.savefig(plot_dir+'cp-Tdependance.pdf')\n",
- "\n",
- "TK = np.arange(273.15,315.15,0.01)\n",
- "es_iapws = np.zeros(len(TK))\n",
- "for i, x in enumerate(TK):\n",
- " es_iapws[i] = iapws.iapws97._PSat_T(x) *1.e6 #Temperature, [K]; Returns:Pressure, [MPa]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Comparison with Sea-Air-Ice library of Feistel et al (2010) v4.0.1\n",
- "\n",
- "Here we compare different thermodynamic constants or empirical formula to the IAPWS-10 and TEOS-10 standards taken from the Sea-Air-Ice library. These are calculated based on fits to potential functions as described above. The libraries are run off-line and the output is tabulated for comparison. The Feistel et al formulation extends to the IAPWS formulation shown above to allow for representations of liquid to the homogeneous freezing point of ice."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 504x625.763 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "sia_dir = './data/'\n",
- "d0 = pd.read_csv(sia_dir + 'psat.dat',sep=' ',names=['T','Psat_liq','Psat_ice'])\n",
- "x0 = d0.to_xarray()\n",
- "d1 = pd.read_csv(sia_dir + 'cp-liq-vap.dat',sep=' ',names=['T','liq_density','vap_density','cp_liq','cp_vap','lv'])\n",
- "x1 = d1.to_xarray()\n",
- "d2 = pd.read_csv(sia_dir + 'cp-ice-vap.dat',sep=' ',names=['T','liq_density','vap_density','cp_ice','cp_vap','ls'])\n",
- "x2 = d2.to_xarray()\n",
- "\n",
- "fig = plt.figure(figsize=(7,14/1.610834))\n",
- "\n",
- "ax1 = plt.subplot(2,1,2)\n",
- "ax1.set_ylabel('difference / %')\n",
- "ax1.set_xlabel('T / K')\n",
- "\n",
- "formula = 'wagner-pruss'\n",
- "es_r = es(x0.T,formula=formula,state='ice')\n",
- "diff = es_r/x0.Psat_ice\n",
- "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',label=formula)\n",
- "es_r = es(x0.T,formula=formula,state='liq')\n",
- "diff = es_r/x0.Psat_liq\n",
- "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',label=formula)\n",
- "\n",
- "formula = 'romps'\n",
- "es_r = es(x0.T,formula=formula,state='ice')\n",
- "diff = es_r/x0.Psat_ice\n",
- "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',ls='dashed',label=formula)\n",
- "es_r = es(x0.T,formula=formula,state='liq')\n",
- "diff = es_r/x0.Psat_liq\n",
- "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',ls='dashed',label=formula)\n",
- "\n",
- "formula = 'murphy-koop'\n",
- "es_r = es(x0.T,formula=formula,state='ice')\n",
- "diff = es_r/x0.Psat_ice\n",
- "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='dodgerblue',ls='dotted',label=formula)\n",
- "es_r = es(x0.T,formula=formula,state='liq')\n",
- "diff = es_r/x0.Psat_liq\n",
- "plt.plot(x0.T,100*(diff.where(diff>1.e-4)-1),c='orange',ls='dotted',label=formula)\n",
- "plt.legend(loc=\"best\",ncol=3)\n",
- "\n",
- "###\n",
- "ax2 = plt.subplot(2,1,1)\n",
- "ax2.set_ylabel('difference / %')\n",
- "ax2.set_xlabel('T / K')\n",
- "ax2.set_ylim(0.8,1.2)\n",
- "\n",
- "plt.plot(x1.T,x1.cp_vap/cpv,c='green',ls='solid',label='$c_{p,\\mathrm{vap}}$ (vap-liq)')\n",
- "plt.plot(x2.T,x2.cp_vap/cpv,c='green',ls='dashed',label='$c_{p,\\mathrm{vap}}$ (vap-ice)')\n",
- "\n",
- "plt.plot(x1.T,x1.cp_liq/cl,c='orange',ls='solid',label='$c_{p,\\mathrm{liq}}$')\n",
- "plt.plot(x2.T,x2.cp_ice/ci,c='dodgerblue',ls='solid',label='$c_{p,\\mathrm{ice}}$')\n",
- "\n",
- "lvx = phase_change_enthalpy(x1.T,fusion=False)\n",
- "lsx = phase_change_enthalpy(x2.T,fusion=True) + phase_change_enthalpy(x2.T,fusion=False)\n",
- "\n",
- "plt.plot(x1.T,x1.lv/lvx,c='gray',ls='solid',label='$\\\\ell_v$')\n",
- "plt.plot(x2.T,x2.ls/lsx,c='gray',ls='dashed',label='$\\\\ell_s$')\n",
- "\n",
- "plt.legend(loc=\"lower right\",ncol=3)\n",
- "sns.set_context(\"paper\")\n",
- "sns.despine(offset=10)\n",
- "plt.tight_layout()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Comparing formulations of saturation vapor pressure ##\n",
- "\n",
- "This comparison of relative error suggests that the Wagner-Pru{\\ss}, Murphy and Koop, Hardy, and Sonntag formulations lie closest to the IAPWS-97 reference. Romps (2017) and Bolton (1980) are similarly accurate and may have advantages. Hardy is interesting as it appears in a technical document and is rarely mentioned in the subsequent literature, but used by Vaisala in the calibration of their sondes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Temperatures above the triple point ###"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "state = 'liq'\n",
- "fig = plt.figure(figsize=(10,5))\n",
- "ax1 = plt.subplot(1,1,1)\n",
- "ax1.set_xlabel('$T$ / K')\n",
- "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
- "ax1.set_yscale('log')\n",
- "\n",
- "es_ref = es_iapws\n",
- "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
- "es_r = es(TK,formula='romps',state=state)\n",
- "es_g = es(TK,formula='goff-gratch',state=state)\n",
- "es_m = es(TK,formula='murphy-koop',state=state)\n",
- "es_s = es(TK,formula='sonntag',state=state)\n",
- "es_b = es(TK,formula='bolton',state=state)\n",
- "es_f = es(TK,formula='flatau',state=state)\n",
- "es_h = es(TK,formula='hardy98',state=state)\n",
- "es_a = es(TK,formula='standard-analytic',state=state)\n",
- "\n",
- "plt.plot(TK,np.abs(es_h/es_ref-1),c='tab:blue',ls='solid',label='Hardy (1998)')\n",
- "plt.plot(TK,np.abs(es_f/es_ref-1),c='tab:orange',label='Flatau (1992)')\n",
- "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
- "plt.plot(TK,np.abs(es_b/es_ref-1),c='tab:red',ls='dotted',label='Bolton (1980)')\n",
- "\n",
- "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
- "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
- "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
- "plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:brown',label='Wagner-Pruss (2002)')\n",
- "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
- "\n",
- "plt.legend(loc=\"lower right\",ncol=3)\n",
- "\n",
- "sns.set_context(\"paper\", font_scale=1.2)\n",
- "sns.despine(offset=10)\n",
- "\n",
- "fig.savefig(plot_dir+'es_l-error.pdf')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Extension to the freezing regime ###\n",
- "\n",
- "To extend over the entire temperature range a different reference is required, for this any of the Hardy, Sonntag, Murphy-Koop and Wagner-Pru{\\ss} formulations could suffice. We choose Wagner-Pru{\\ss} because Wagner's group is responsible for the standard, and has also developed the IAPWS standard for saturation vapor pressure over ice. Below the results are plooted with respect to this standard over a much larger temperature range.\n",
- "\n",
- "It is not clear how accurate Wagner and Pru{\\ss} wis hen extended well beyond the IAPWS range, based on which it might be that the grouping of errors of similar magnitude from the Bolton, Flatau and Goff-Gratch formulations are indicative of a low temperature bias in the Wagner-Pru{\\ss} formualtion. I doubt that this is the case, as the poor performance of all these formulations in the higher temperature range, and the simplicity of their formulation make it unlikely. The agreement of the Murphy-Koop formulation with these simpler formulations at low temperature may be indicative of Murphy and Koops focus on saturation over ice rather than liquid."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "state = 'liq'\n",
- "fig = plt.figure(figsize=(10,5))\n",
- "ax1 = plt.subplot(1,1,1)\n",
- "ax1.set_xlabel('$T$ / K')\n",
- "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
- "ax1.set_yscale('log')\n",
- "\n",
- "TK = np.arange(180,320,0.5)\n",
- "\n",
- "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
- "es_r = es(TK,formula='romps',state=state)\n",
- "es_g = es(TK,formula='goff-gratch',state=state)\n",
- "es_m = es(TK,formula='murphy-koop',state=state)\n",
- "es_s = es(TK,formula='sonntag',state=state)\n",
- "es_b = es(TK,formula='bolton',state=state)\n",
- "es_f = es(TK,formula='flatau',state=state)\n",
- "es_h = es(TK,formula='hardy98',state=state)\n",
- "es_a = es(TK,formula='standard-analytic',state=state)\n",
- "\n",
- "es_ref = es_w\n",
- "\n",
- "plt.plot(TK,np.abs(es_h/es_ref-1),c='tab:blue',ls='solid',label='Hardy (1998)')\n",
- "plt.plot(TK,np.abs(es_f/es_ref-1),c='tab:orange',label='Flatau (1992)')\n",
- "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
- "plt.plot(TK,np.abs(es_b/es_ref-1),c='tab:red',ls='dotted',label='Bolton (1980)')\n",
- "\n",
- "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
- "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
- "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
- "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
- "\n",
- "#plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:olive',label='Wagner-Pruss (2002)')\n",
- "\n",
- "plt.legend(loc=\"lower left\",ncol=2)\n",
- "\n",
- "sns.set_context(\"paper\", font_scale=1.2)\n",
- "sns.despine(offset=10)\n",
- "\n",
- "fig.savefig(plot_dir+'es_lsc-error.pdf')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Sublimation vapor pressure ##\n",
- "\n",
- "A subset of the formulations also postulate forms for the saturation vapor pressure over ice. For the reference in this quantity we use Wagner et al., (2011) as this has been adopted as the IAPWS standard. Here is seems that Murphy and Koop's (2005) formulation behaves very well in comparision to Wagner et al., but Sonntag is also quite adequate, particularly at lower ($T<273.15$ K) temperatures where it is likely to be applied."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x360 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "state = 'ice'\n",
- "fig = plt.figure(figsize=(10,5))\n",
- "ax1 = plt.subplot(1,1,1)\n",
- "ax1.set_xlabel('$T$ / K')\n",
- "ax1.set_ylabel('$e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1$')\n",
- "ax1.set_yscale('log')\n",
- "\n",
- "TK = np.arange(180,320,0.5)\n",
- "\n",
- "es_w = es(TK,formula=\"wagner-pruss\",state=state)\n",
- "es_r = es(TK,formula='romps',state=state)\n",
- "es_g = es(TK,formula='goff-gratch',state=state)\n",
- "es_m = es(TK,formula='murphy-koop',state=state)\n",
- "es_s = es(TK,formula='sonntag',state=state)\n",
- "es_a = es(TK,formula='standard-analytic',state=state)\n",
- "es_ref = es_w\n",
- "\n",
- "plt.plot(TK,np.abs(es_g/es_ref-1),c='tab:green',label='Goff-Gratch (1957)')\n",
- "plt.plot(TK,np.abs(es_r/es_ref-1),c='tab:purple',label='Romps (2017)')\n",
- "plt.plot(TK,np.abs(es_s/es_ref-1),c='tab:grey',label='Sonntag (1990)')\n",
- "plt.plot(TK,np.abs(es_m/es_ref-1),c='tab:pink',label='Murphy-Koop (2005)')\n",
- "plt.plot(TK,np.abs(es_a/es_ref-1),c='tab:purple',ls='dotted',label='Analytic')\n",
- "\n",
- "#plt.plot(TK,np.abs(es_w/es_ref-1),c='tab:olive',label='Wagner-Pruss (2002)')\n",
- "\n",
- "plt.legend(loc=\"lower left\",ncol=2)\n",
- "\n",
- "sns.set_context(\"paper\", font_scale=1.2)\n",
- "sns.despine(offset=10)\n",
- "\n",
- "fig.savefig(plot_dir+'es_i-error.pdf')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Clausius Clapeyron ##\n",
- "\n",
- "Often over looked is that many conceptual models are built on the application of the Clausius-Clapeyron equation,\n",
- "\\begin{equation} \n",
- "\\frac{\\mathrm{d} \\ln e_\\mathrm{s}}{\\mathrm{d \\ln T}} \\left(\\frac{\\ell_\\mathrm{v}}{R_\\mathrm{v} T}\\right)^{-1} = 1 \n",
- "\\end{equation}\n",
- "with the assumption that the vaporization enthalpy, $\\ell_\\mathrm{v}$ that appears in this equation, is linear in temperature following Kirchoff's relation. This is similar to assuming that the specific heats are independent of temeprature, an idealization which is, unfortunately, just that, and idealization.\n",
- "\n",
- "But because of this it is interesting to compare this expression as given by the above formulation of the saturation vapor pressure (through their numerical derivative) and independent expressions of $\\ell_\\mathrm{v}$ based on the assumption of constant specific heats. \n",
- "\n",
- "This is shown below for ice and liquid saturation. The analytic expression, which has larger errors for es is constructued to satisfy this relationship and is exact to the precision of the numerical calculations. The various formulations using more accurate expressions for $e_s$ which implicityl don't assume constancy in specific heats are similarly accurate, with the exception of Goff-Gratch, and Romps for Ice. Hardy is only shown for water. For ice Sonntag does not behave well for $T> 290$ K, but it is not likely to be used at these temperatures. Note that Romps would be perfect had we adopted his modified specific heats.\n",
- "\n",
- "Based on the above my recommendation is to use the formulations by Wagner's group, unless one is interested in very low temperatures ($T<180$K) in which case the formulation of Koop and Murphy may be desirable. For just liquid processes Hardy might be a good choice, it is less well known but used by Vaisala for its sondes. There may be advantages to using Sonntag if there is interest in liquid and ice as it might allow more efficient implementations, but for my tests all formulations were within 30% of one another.\n",
- "\n",
- "Another alternative, would be to use the analytic approach, either using Romps' formulae if getthing the staturation vapor pressure as close to measurements as possible is preferred, or using the analytic formula with the correct (at the standard temperature and pressure) specific heats and gast constants."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 720x720 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "state = 'liq'\n",
- "\n",
- "fig = plt.figure(figsize=(10,10))\n",
- "\n",
- "ax1 = plt.subplot(2,1,1)\n",
- "\n",
- "ax1.set_ylabel('$|\\mathrm{CC}_\\mathrm{liq} - 1|$')\n",
- "ax1.set_yscale('log')\n",
- "ax1.set_xticklabels([])\n",
- "\n",
- "TK = np.arange(180,320,0.5)\n",
- "\n",
- "lv = phase_change_enthalpy(TK)\n",
- "if (state == 'ice'): lv += phase_change_enthalpy(TK,fusion=True)\n",
- "\n",
- "y = lv/(Rv * TK)\n",
- "cc_w = dlnesdlnT(TK,formula=\"wagner-pruss\",state=state) / y\n",
- "cc_r = dlnesdlnT(TK,formula='romps',state=state) /y\n",
- "cc_g = dlnesdlnT(TK,formula='goff-gratch',state=state) /y\n",
- "cc_m = dlnesdlnT(TK,formula='murphy-koop',state=state) /y\n",
- "cc_s = dlnesdlnT(TK,formula='sonntag',state=state) /y\n",
- "cc_h = dlnesdlnT(TK,formula='hardy98',state=state) /y\n",
- "cc_a = dlnesdlnT(TK,formula='standard-analytic',state=state) /y\n",
- "\n",
- "plt.plot(TK,np.abs(cc_h/1 -1.),c='tab:blue',label='Hardy (1998)')\n",
- "plt.plot(TK,np.abs(cc_g/1 -1.),c='tab:green',label='Goff-Gratch (1957)')\n",
- "plt.plot(TK,np.abs(cc_r/1 -1.),c='tab:purple',label='Romps (2017)')\n",
- "plt.plot(TK,np.abs(cc_s/1 -1.),c='tab:grey',label='Sonntag (1990)')\n",
- "plt.plot(TK,np.abs(cc_m/1 -1.),c='tab:pink',label='Murphy-Koop (2005)')\n",
- "plt.plot(TK,np.abs(cc_w/1 -1.),c='tab:olive',label='Wagner-Pruss (2002)')\n",
- "plt.plot(TK,np.abs(cc_a/1 -1.),c='tab:purple',ls='dotted',label='Analytic')\n",
- "\n",
- "plt.legend(loc=\"lower left\",ncol=1)\n",
- "\n",
- "state = 'ice'\n",
- "TK = np.arange(180,320,0.5)\n",
- "\n",
- "lv = phase_change_enthalpy(TK)\n",
- "if (state == 'ice'): lv = phase_change_enthalpy(TK,fusion=True) + phase_change_enthalpy(TK)\n",
- "\n",
- "y = lv/(Rv * TK)\n",
- "cc_w = dlnesdlnT(TK,formula=\"wagner-pruss\",state=state) / y\n",
- "cc_r = dlnesdlnT(TK,formula='romps',state=state) /y\n",
- "cc_g = dlnesdlnT(TK,formula='goff-gratch',state=state) /y\n",
- "cc_m = dlnesdlnT(TK,formula='murphy-koop',state=state) /y\n",
- "cc_s = dlnesdlnT(TK,formula='sonntag',state=state) /y\n",
- "cc_a = dlnesdlnT(TK,formula='standard-analytic',state=state) /y\n",
- "\n",
- "ax2 = plt.subplot(2,1,2)\n",
- "ax2.set_xlabel('$T$ / K')\n",
- "ax2.set_ylabel('$|\\mathrm{CC}_\\mathrm{ice} - 1|$')\n",
- "ax2.set_yscale('log')\n",
- "\n",
- "plt.plot(TK,np.abs(cc_g/1 -1.),c='tab:green',label='Goff-Gratch (1957)')\n",
- "plt.plot(TK,np.abs(cc_r/1 -1.),c='tab:purple',label='Romps (2017)')\n",
- "plt.plot(TK,np.abs(cc_s/1 -1.),c='tab:grey',label='Sonntag (1990)')\n",
- "plt.plot(TK,np.abs(cc_m/1 -1.),c='tab:pink',label='Murphy-Koop (2005)')\n",
- "plt.plot(TK,np.abs(cc_w/1 -1.),c='tab:olive',label='Wagner-Pruss (2002)')\n",
- "plt.plot(TK,np.abs(cc_a/1. -1.),c='tab:purple',ls='dotted',label='Analytic')\n",
- "\n",
- "sns.set_context(\"paper\", font_scale=1.2)\n",
- "sns.despine(offset=10)\n",
- "\n",
- "fig.savefig(plot_dir+'cc-error.pdf')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Optimizing analytic fits for saturation and sublimation vapor pressure ##\n",
- "\n",
- "Romps suggests modifying the specific heats of liquid, ice and the gas constant of vapor to arrive at an optimal fit for the saturation vapor pressure using the analytic form. One can do almost as good by just modifying the specific heat of the condensate phases. Here we show how the maximum error in the fit depends on the specific heat of the condensate phases as compared to the reference, and how we arrive at our optimal fit by only manipulating the condensate phase specific heats to values that they anyway adopt within the range of temperatures spanned by the atmosphere. This justifys the default choice for saturation vapor pressure and the specific heats used in aes_thermo.py\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Taking fit for $c_\\mathrm{liq}=$ 4179.57 J/(kg K) at $T=$ 305.00 K\n",
- "Taking fit for $c_\\mathrm{ice}=$ 1905.43 J/(kg K) at $T=$ 247.06 K\n"
- ]
- },
- {
- "data": {
- "image/png": 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qKddcc41ecsklTS6zcuVKHTp0aIOhFh3Z7pQpU5ocTmAtnFEoISmOpLR4AOISYknJSAAgJjaG1C6JSIyvlS01M5HYeN+fODk9gfhE39FaYko8iSm+18cnxpKcnhDwthcvXsyFF17YYNrFF1/Mf/7zn2ZvezVv3jy6dOnCuHHjuPTSSznppJNa3MaCBQuYMGECQ4cOrZt24403cuyxxzJ79uwGy3bv3p2nn36aK664gkmTJnHiiSeSl5fXYJnLLruM1NRUxowZw+TJk3nkkUea3XYgl2iq1adPHx5++GHOPfdcJkyYQG5uLr/61a8Cem1xcTE5OTmMHj262WXOOussbr31Vr7zne8wceLEgI7MAc477zzefvvtuucbN27kD3/4Azt37mTGjBlMnjyZW2+9FYBHH32Um266iYkTJ3L//fc3uMzVI488wi9/+UtGjBjBpk2b+OlPf3rUthISEnjxxRfZuXMn119/PQCff/45AwYMID8/n7PPPpuJEycyYcIEtmzZwgMPPNDi+g8cOMBJJ53ExIkTmThxIrGxsfzyl78MaLurVq1i5syZIW/hPLy/lLKiKgaO6ca3b5tObKyl0WCqzVN7Nx9m/44jQV+/k/nz+eef5/TTT2fv3r1kZWWRlZXFXXfd1WJL4ZEjR+jSpQsZGRnk5+ezZMmSZpc99dRT+eqrr3jooYe4+uqr66anp6c3uCRZrdmzZ7NmzRo2btwI+HoIGl+arW/fvqxfv76uN+HAgQMN5j/44IPceuut3H///Vx44YU8+OCDxMbG0q1bN3JycigoKGjxPTnuuOP45JNP2LNnD+C7bNypp57a4mvq+/LLL5vtKQL4xS9+wcUXX8z+/fsDXmeXLl1ISkri8OHDAS1f+54UFhZy33331eWkw4cPc/Dgwbrlnn/+ecaPH9/gtXFxcfznP//h8OHD/OAHP0BV+d73vseLL75Yt8xPf/pT9u7dy1NPPUVMzNH5ZvHixSxYsKDu+6u92wVfS+revXsZOXJkQPveJoFWpvY46oi/P/AP4JMAlw/0KOAobhv0HmrnnnuuvvzyyyHfTuMWzu3bt+tFF12kv/zlLxuMSw03mzdv1nnz5jmy7YKCgrpxZ53tqquu0rfeeqvZ+cH6HC17ZJ2uXrY7KOvyczyftfboSP7qCK/Xq9WVNfr+v7bUtUq2lZvy52mnnaZPP/10g2m7d+/Wnj17alVVVZMtnJWVlfrNb35Tx4wZo3PnztUFCxa02EP0u9/9TufOndtg2u23366jRo1q8qShd999V6dPn64TJ07UadOmNTiJSVW1oqJCL7nkkrrLDtVuuzZv3nrrrQ16am699VZdsWJFi+9D4ytRLFmypO6koYsvvrjZS/g0VlRUpImJiU2evV7fW2+9pSeffLJOmDAhoLHwqqrXXnutPvvss3XPL7/8cu3fv78C2r9/f7388svr5p1yyik6ZswYHT16dIMTm7Zt26bTpk1rsG/1e33q71tZWZmefPLJ+uMf/1jHjRtX1+pdewmt0aNH113+qH7LcVVVlfbo0UN37NjR4e2qqq5YsULPPffcJt+TiD5pCOgO/BfYBKwH/g4kdGB9jwDZTSVPYDzwObAdeAVID3CdSwNcLtA/ylHclDBDaceOHXrMMcfo+eef32zXQGc555xzHN1+Rz311FN114+LBjU1NfrII4+0uExHP0eH9/tOAKuqrAn2/6fjuba1h1MFp6pqaWGlvvXERq0sb/3ktKZES/6s1dkH7Hv27NHLLrtMb7vtNl2yZInu3btXL7nkEv3JT36iCxcuDHkcoeLUgbuTB+2qLR+4d7TgFFX33u1BRLoBY1T1YxGJAZ4GVqvqH+stkwakqWpuvWnDgK9U1dNofScBW4FcVZVG8z4Cfqeqy0TkPqBSVe8QkYnAfY1Cu19V3/S/bqmqnhvAvmjjbTbjqD/I7t27G3QvG2PariOfo5KCCp6+cxUX/3IGXXqltP6Ctgl8HIdDOpK/gmnTxzkU5pUza/7wgF8TLflz586dzJs3jwkTJrBkyZI2DQ8yTVu8eDHz588nPT3d6VA6hcfj4W9/+xvXXHNNk/Nb+CwF9M/m6oKzMRG5BRikqjfWm3Ya8GdgjqoeEJHxwEvAWaq6vZn1NEieItIb+FxV+/ufjwReUtWxAcRkBacxYaA9nyP1KpXlNSSlxlNaWElqZmIoQnN9ZeCWgjN3dyGlBZUMn9oL9WrdeMuWWP40Jjg6WnCGzWh3EUkCrsDXxV5HVd8B7gLe8Ldgvghc1lyx2YwBwL56z/cCA1uJJ1lEHgUmiMij/hbYxsssFJE1IrKmDbEYY1xi7dt7ef0x3yVoQlRsmjboMzST4VN7UV3p4dl7VpO7u9DpkIwxAWr5Iocu4S/mngDeVdXXG89X1WdFZCjwPjBfVZs/hbeZTdDGI3NVLQeabnf+epnHgMfA10LQxpiMMQ7xVHuJjY9h/En9GTa5h9PhmEbiEmKYeuZgegxIQ1Xx1mjdGeXGGHcKl0/oX/w/b2pqpoiMAK4C7gV+7R/72Rb7aNiiOYiGLZ7GmCihXuWF+z9n+5oDxCfGktkz6GM2TQeJCCNm9CYuPpZNH+Xw6kNrCafhYcZEI9e3cPpP4BkInK+qR91+wn+C0FJgoaq+JyJbgeUicoaqHglkG6qaKyJZInK2qi7DV7y+ELy9MMaEA1XfuMDjvz2cHgOj40SBcDdiem+6909DRKgorSYpNd7pkIwxTXB1C6eIjANuBYYDq0VkrYj8odFiKcC1qvoegKouAu4HjhpwJSKLRGSf//d9IrKo3uxrgbtFZDswlqPPTHdMfHx8kxftNcYEpqioiPj4lguR/OwSltz3GZVl1fQb0ZWEJNcfj4dUuIxBT0iOo8+wTLweL8/du4ZdXzS88YPlT2M6LpAc2pqwOks9nHXkLE+v10tOTk6zd/IxxrQsPj6efv36NXmXjlo11R62fJLLuBP7dfYlZews9SDJzy6hS58UYkSoKKsmOS3B8qcxQdBKDo28yyKFs3BJ2MZEm80rcqgorWHKGYOcCsEKziDb9HEO69/bx0W/mGHXozQm9AL6kEV3n5ExJuqlZCQSlxDrdBgmiEbP7EPf4ZmISCivn2qMaQNXj+E0xphQ8HqVVa/sovRIJYPHd2fE9N5Oh2SCKCY2hq59UvF4vCz5/WfsWpvX+ouMMSFlBacxJuqoKsX5FVSU2ri+SBYbG8N5N05m6MQeeDxeSgsrnQ7JmKhlYzg7SbiNgTImElWW17D7yzxGz+zrdCj1uX6QYSTkrw0fZLP54xwu+Nl0G9dpTHBF1q0tjTGmow7nlLLpwxxqqj1Oh2I62djj+zL3B+MREcpLqpwOx5ioYy2cnSQSWgiMCVdHDpQREydkdE/2XdzdXS1crgqmKZGUv6oqalh8+yec86OJ9BmW6XQ4xkQCuyySm0RSwjYm3Lzz1GaS0uKZ/a1jnA6lKVZwdrL87BK69UvFW+ML1+7DbkyHWMHpJpGWsI0JB4V5ZWT2TMFT7SUmVpAYV9Z2rgyqvkjNXyte2EHpkUrOuHKc06EYE85sDKcxJnpVltfw3D1ryN1VSGx8jFuLTeOgKWcOYsY5QwHw1HgdjsaYyGYtnJ0kUlsIjHGjqooaEpLiKCmoIK1rktPhtMb1lXCk568jB8p4+c9fcMFt0+0i8ca0nbVwGmOiz64v8nj+3jV4Pd5wKDaNC2T2TObk74wiNTMRa4QxJjSshbOTRHoLgTFOU6+CgNej5GeX0GtwhtMhBcqVLZwishBY6H86LRryl6ryxuMbGTqxByOP7eN0OMaECztpyE2s4DQmtN7711YyeiQx9czBTofSVq4sOOuLpvy1a20ePQakkdEj2elQjAkXVnC6STQlbGOckL2tgJSMBLr2SXU6lLaygtOFcrYXsHVlLqdcOtpOODOmZTaG0xgT2arKa1j+tw0U5ZfTf2TXcCw2jUslpSXQrV+aFZvGBIkVnMaYsBWXEENGz2QSEuOcDsVEmG59U5l0+kA8NV5WvbqLqooap0MyJqxZwWmMCTuFeeWsfWsPMbExzJo/nKS0eKdDMhGqusLDob0lVJV7nA7FmLBmBacxJuyUFVWRt7cYrzdihgwal0pKi+ecH00krWsiOdsLKC2sdDokY8KS9UMZY8JG7u5CklLj6Ts8k77DM50Ox0SZtW/tZdC47ow/qb/ToRgTduws9XYSkf7AXcAYVZ0VwPJRd5anMcH29pOb6Nonlalzw+7SRy1x/Vkplr98vB4vMbExeKq9lBVXkd7NbixgDG4/S11EHhGRbBFpMUGJyDUiskFENorIwyISG4ptish4EflcRLaLyCsikt7SulQ1W1WvBPLbG48xJjCH9pWgqpy6YEykFZsmjMTE+r4y1727j7cXbXI4GmPCi5NjOJ8Bpra0gIiMA24FjlfVcYAHuLTRMmki0qfRtGHNFKYtbfNR4HZVHQFsAX7qX9dEEXm90eOMAPbPGBMElWXVvPSnz8nbU0yMXaLGuMDE0wdwxlXjAPB4vA5HY0x4cLxLvaWuGhG5ELhAVS/2Pz8P+KGqnlNvmdOAPwNzVPWAiIwHXgLOUtXtgWxTRHoDn6tqf//zkcBLqjo2gPiXquq5HdnPRiK6S8qYtqgorSYpNZ7ykiqS0xKcDidUXF9FW/5qWt7eYpY/toELfjadpFS7UoKJWu7uUg/Ql8BsEenvb7G8EBhYfwFVfQffWMo3ROQk4EXgsuaKzWYMAPbVe7638XYaE5FkEXkUmCAij4rIUe+liCwUkTUisqYNsRhj8N1i8Pnfr8Hr8UZysWnCWPd+qcz+9jFWbBoTAFe3cPrnfwe4GagG3gbOUdWjusVF5DbgXmC+qr7clm2KyHTgIVWd6X+eDBxU1RbHcbaFtRAYExj1Kojv5+H9pfQYELSPoVtZC2eY81R7ee+ZrUybO5guvVOcDseYzhYRLZyo6jOqOkNVZwPrgK2NlxGREcBV+ArOX4tItzZuZh8NWzQH0bDF0xjTST56bjuf/TeLmNiYaCg2TQSQWCE5LZ7YeNd/pRrjGNdfh1NEevvHZmbiO5HnxkbzhwFLgYWq+p6IbAWWi8gZqnokkG2oaq6IZInI2aq6DF/x+kJw98QYE4gRM3oTn9Tui1EY0+liYoTZ3zoGgOxtBXTpnUJqZqLDURnjLk5eFmmRiOzz/75PRBb5f1/m7+Ku9W8R2QSsBB5R1RWNVpUCXKuq7wGo6iLgfuCoT3tz2/S7FrhbRLYDY4H7OryTxpiA1FR7eHvRJo4cLKPPsEy690tzOqSoZ2PQ22ftW3vJWnfI6TCMcR3Hx3BGCxsDZUzzvF5l5Us7mXjqQNK6Rl3LkI3hjCD1Lw7vqfGSkOz6jkRjOiqgHGYFZyexhG3M0SpKqtm6KpeJpw1AxPV1V6i4fsctf7XdR89vp7yoijOuHOd0KMaEWkA5zA69jDGOKT5cwZ5N+Yw7sR9xCTZu00SO6fOG4Kmxi8IbU8taODuJtRAY87XiwxXUVHno2ifV6VDcwFo4I9ieTfnsWnuIk78zMppb8U1ki4zLIhljIs+6d/ay9q29TodhTMildU2iW99UKzZN1LMWzk5iLQTGQElBJaldElCvokBsrB3zYi2cUaGmysO+rQUMmdDD6VCMCTZr4TTGuIfX4+XFP37Ori/yiImNsWLTRJXc3UWsfGknnmob12mik7VwdhJrITDRrPZSMYV5ZWR0T0ZiXN+o15lc/2ZY/gqO2s9BdZWHeDtJzriMp8ZL1rpDDJ/aq60vtRZOY4zz8nNK+NedqygvqSKzZ4oVmyZqxcTGkJ9dwj/v+ITykiqnwzGmgfzsEla9upuqipqQrN9aODuJtRCYaOX1Kru/zGPY5J524kTTXP+mWP4KHlUle2sBA0Z3czoUYwDf/2RlWQ1JqfF1rfBtZC2cxhjn7P4yjxUv7CAmRhg+pZcVm8YAIsKA0d2oKq9h1Su77FqdxnGbP97Pqw+sRVXbU2wGzC78bowJidQuiWT2THY6DGNcqabaS352CZVlNaRkJDgdjolio47rQ59hmSFvFLAu9U5iXVImWmxdlUvPQel062sXdQ+Q65t+LX+FVvHhCtK7JTkdhokyW1flUlJQwbR5Qzq6KutSN8Z0LlUlZ1sBh3NKnQ7FmLBQeqSSf925koJc+8yYzpWSkUBa18470LEWzk5iLQQm0u3bWkD/kV1srGbbuf4Ns/wVWkWHysnoYcNPTOc4+FURyekJwWxVtxZOY0znKC+u4s3HN3J4v7XSGNNWGT2SqSit5p3Fm6kqD80laYyptf69fWz5ZH+nb9daODuJtRCYSKSqVJRUk5yeYBezbj9r4TRUV3n4+LntHPuNYXYSkQkJj8dLbGwMXo8XRIgJ3jWRrYXTGBNau9ce4vn7PsPj8VqxaUwHxCfEcsqlo0nJSKAwr9zpcEwEev2vG1j/3j5iYmOCWWwGzFo4O4m1EJhIUps3VOHIgTI7I71jrIXT1CnKL+eZ36ziO786zsZ1mqDK3VVIerckUrskBnvVAeWwdhWcItINGA7sUdUDbV5BFLKEbSLJypd3ogqz5g93OpSwdfjwYXbu3Mmxxx7bx+151PJX57LLJJlg2vRxDv1GdKFLr5RQbSI0XeoicgXwFvA/wHsi8rO2rsMYE96GTe7J8Ck9nQ4jbC1atIg5c+bwpz/9CVyaR0VkoYisEZE1TscSbdK7JVF6pJJ3n96Cp9ruRGTaT1XJ3lbgimEarbZwisgMVV1d7/kK4CRVrRGRNCBLVXuEOM6wZy0EJtypV1nx4k5Gz+pD935pTocTVlavXs2MGTPqns+ePZsPPviAuLg4RCQdl+dRy1+dr7KsmhUv7GT2t4aTmBLvdDgmDFWWVZOQHNcZl6oLWgvnr0XkCRHp5X++E/ipiMwBfg5saGeAYU1E+ovIP0TkE6djMaazqKqVFO3wm9/8hu9///scPHgQgOHDh3Pffffx1ltvQRTnUdO8xJR4Tr1sNIkp8ZQVVTkdjgkzqsrL/28tm1d0/uWPmtNqwamq5wLPAktF5DbgeqAE+BZQBJzfng2LyCMiki0izX59iciZIvKZiHwhIqtFZFZ7thXINkVkvIh8LiLbReQVf6tDs1Q1W1WvBPI7EpMxbuf1KltX7geBEy4YQff+1rrZVkuXLuWiiy7i3HPP5fe//z0PPvggaWlpvPDCC9CBPGoiX/bWAv591yqqqzxOh2LCiIgw54qxjJjR2+lQ6gQ0hlNV/wvMBiqBN4GvVPVHqvp7VS1o57afAaa2ssw/gEtVdQrwa+DPjRcQkTQR6dNo2jARaeoaLS1t81HgdlUdAWzB14o7UUReb/Q4o5WYjYkoxfnlfP7GHmtl6aCzzjqLFStWkJiYyBlnnMHgwYN5+OGH6WAeNRGu38gunH/LVLvsmAnYunf3cmhfCd36pbrq/6bVglNEYkXkQnwnCW0Hzgbm+Yuvse3dsKp+EMCZmQJ09f/eFchpYpljgTdFpLc/3vHAG8CwQLfpf+1QVV3mn/R34Nuquk5V5zV6vBnQDhoT5jweL0WHysnsmcIltx9LambQL6URNTweD8899xx/+tOfGDFiBMuWLeP1119n3rx5dCSPmsgnInTtk8qRA2V8+c5ep8MxLqeqHN5fRmVptdOhHCWQk4aeB6qBd4GTgYOq+j8iMhH4A7BNVa9vdwAtDEYXkVOA54ByfMXn8aq6p4nlLgJ+ia+7/3FggaquDHSbIjINeFhVj/M/T8a3n812q/uX+RNwFvBf4Eeq6m20zEJgof/pNBt0b8LJxg+z2fhhDhf+fLrdH72DLrjgAuLj4zn11FN5//336dWrF3/6059Yt24dkyZNeoMO5tFQs5OGnLd/ZyEb3t/HnCvGIg5ctNu4XyeeJNRYcK7DKSKHgV7+s9ITgc9UdXy9+eer6ovtjrKZRCYiccBS4DZV/VJEfgBcqKpnNrOe24B7gfmq+nJbtiki04GHVHWm/3mrBWdbWcI24cLj8fruQqFQVlxlLZtB0K1bNw4ePEhcXByVlZVMmzaNDRvqzhOSjubRULP85S61tyg0pr6X/vQFQyZ0Z/KcQZ296YAKzrgAlvkP8Ib/ckgnAP+uPzOESXIy0FVVv/Q/f5omxnACiMgI4Cp8BeevReRDVT3chm3tAwbWez7IP82YqPPe01tJ75bEsecOtWIzSC6++GLOPPNMZs+ezUcffcQll1zSYL6bi03jLmuWZVFwoJQzvj/O6VCMy5x62SiSUt17Ca2A7jQkIscCQ4AtqrouqAE038LZB9gETFLVvSLybeBnqjqj0XLD8HVp/1BV3/NfmP7HwBmqeiTQbYrIx8DdqrpMRO4DqlX1l0HYxWa32QxrITCOyttbTGJynN1WL8g+/fRTsrKyGD16NBMnTqw/y/X9o5a/3OPIgTI8Hq9dC9fU2b3uEMlp8fQZlulUCKG7tWUwiMgiYA7QH8gG3lLVK0RkGfArVV0jIt8HfgLUAKXANY0LXv9JQr1U9Z160y4B3m18glBz2/TPmwg8CaQBW/GdHV8YxP21hG1cy1Pj5b2ntzB5ziC77FHns4LTtNmhfSUkJMeS0d0ODKPdqld3kZKewIRTBjgVgrsLzmhjCdu4mXqVVa/uYtyJ/e0ezp3PCk7TZv/963r6DMtkyhmdPl7PuITH4yVGxA0nkVnB6SaWsI0beaq9bP5kP+NO7GdnojvH9W+85S/38dR4iY2zE4ei2cqXd1JWVMVpC8Y4HUpwbm0pIm+IyM0iYiOUjYkwxYcr2PxxDhUuvGZbJDnzzDP54x//yMaNG50OxUSI2LgYaqo9vPXEJgrzyp0Oxzhg/EkDmHCyY93obRbIZZGOA+b6H/2Bt4HX8Y1/tLtjBMhaCIybeGq8FB+uoEuvFFTVWjdDbNWqVSxfvpzly5eTnZ3N6aefzrx585gzZw5du3Z1/Ztv+cudVJVPX93N6Fl9yOyZ4nQ4ppNUlFaTv6+E/qO6tr5w5wh+l7qIZAJn4Cs+5wC5wHJ8Begqtf75ZlnCNm6y4f19bFmZy7d/Os2KzU5WWFjIm2++yfLly3nrrbfIyspaicvzqOUv96sqryEhOZArHZpwt33NAda9s49v3TrVLfk79GM4RWQMMA9fAfqWqv5fu1cW4SxhGzeoHWQOvqPk5PQEhyMy/ltbujqPWv5yt7w9xbzy57Vc+tuZrr4Oowkel138304achNL2MYN3n5qM2ldEznuG8OcDsV8zRVNFC2x/OVuqsrBrGJ6D81wOhQTQsWHK3jv6a3MvXqc21qzg3anIWNMhJh02kASkmOdDsMYE0QiQu+hGZQWVlKUV07fY7o4HZIJgdi4GPoOzyQ+KTxzuGvaY40xoeH1eHn36S0c2ldCjwFpdqFoYyLU9tUHWPeu3ZU5EpUVVZGSkcD0s4e4Zdxmm1nBaUyEExGS0+KJTwzPo2JjTGAmnjaQM6+2KxhGmuoqD8/8dhXZ28L7wkDtLjhFZEij51M6HI0xJmi8XmXzihwAZn5zOJk9rWXTbbKysho8tzxqOiLGf8eZT17cycGvihyOxgRLfEIs3/rJVPqN6OJ0KB3SkRbOB0XkBhFJEpG78d3z3BjjEsX5FXz59j7KiqucDsU04/rrr8fyqAkmESEuIQavx87figRfbcinILeUrn1Sw7YrvVZHL4v0GHAJcLuqPhC0qCKQneVpOot6leLDFWT0SEa96ob77JoWiMjfcHketfwVnlx26RzTDu89vYUeA9IY7+47CgXn1pbNrl3kj0ACvgvBzxORa9u7LmNM8GxbfYClD32J14pN17v55pvB8qgJgYLcUv55xyeUWw9HWDvl0tGMO6m/02EERUcOfd5W1StUdRVwDr6kaYxxiKqiXmXkjN5844bJdeO5jHudfvrpWB41odClVwonXjTSLgQfpvZtOcw7T22OqFsPd7RL/RigL/7mVFX9IEhxRRzrkjKhtuqVXVRXeTjhghFOh2LaQERG4MI8KiILgYX+p9Msf4Wn6ioPlaXVpHVNcjoU0wb52SXs31nI+PBo3Qzthd9F5GEgE5gNrAKSAFckSmOi0THTeuGp8TodhmmDH/3oRwC/wYV5VFUfAx4D3wGzw+GYdlr54k4qyqo54/t2uaRwUVleQ/f+aXTvn+Z0KEHVkS718ap6KfCVql4CeIIUkzGmDT57PYu8vcV0759Gr8F2a7twsmHDBiyPmlCace5QTrtsjNNhmACVFlby1M8/piC31OlQgq4jBWeV+AYWHBGRy4GRQYrJGBMgVaWipJrqSqtTwlFCQgKWR00oJaXGI7HC5hX7qam2POF2qZmJnHfjFLr2SXU6lKBr9xhOEekL5AE9gO8C76jq2uCFFllsDKcJtj0b8xk4ppudiR7G9u/fT79+/eJxeR61/BXeaqo8vHj/55xy6Wh6Dkp3OhzTjLw9xaR1TSQ5PezOHQztZZFUdT8wAhgKrARS2rsuY0zbVJRU8+4/t5CfU+J0KKYD+vbtC5ZHTYjFJcRywc+mW7Hpcl+88RXr3892OoyQ6UgL5ytAPpDrn6Sq+otgBRZprIXABEtlWTWJKfHUVHuIi7f7o4ez8847j1dffXURLs+jlr8iw97Nh8nPLmHynEFOh2KaoF7F61Vi48LuYv2hPUsdSFHV8zrwemNMG+3fWch//7qey347k4Skjnx8jRuUlZWhqt93Og4TJQS7koULlRVVsfHDbKbOHRyOxWbAOvKN9biI3AdsxH9Uq6pPBSUqY0yT+gzL4NwfT7RiM0JcddVVWB41nWXg6G4MHN3N6TBMI6VHKinYXxrxN+voSCn9I6AIX9Ea738YY0Jg97pDvPevrQB26aMI8vDDD4PlUdOJaqo8LLlvDfnZNv7bLXoOSufMq8dH/AmgHWkmOaKq/xu0SMKIiPQH7gLGqOosp+MxkS+jexK9BqdHzC3OjE+XLl2I1jxqnBGXEMuY4/uF45nQEenNf2xk1Mw+DBrb3elQQq4jBWesiLwIbODrrqBfBfpiEXkEOA/o19RgdBEZBfyn3qS+wApVPb89wba0PREZDzwFpAObgUtVtbi5dalqNnCliCxtTyzGBCp7WwEJSXH0HJQecXedMODxeOhIHjWmPcYe3w/w3fYyPsFOPHSKqtJvRBe69IqOi1N05Cz1kxtPU9X32/D6k4CtQG4gZz+KyPvAI6r673rT0oA0Vc2tN20Yvrt2eBq9vtntichHwO9UdZl/PFWlqt4hIhOB+xqFcr+qvul/3VJVPTfA/bWzPE2brViyg6S0eKbOHex0KCYE3n//fU455ZRT6k9rSx7tLJa/Is8Xb+5h3+bDfOOGyU6HEpVUFa8nLM9Ib0pAXW8BF5wiEgNMASbiu4d6IbAe+FxV233aWyCJTEQGA2vxtU6W15t+GvBnYI6qHvC3VL4EnKWq2wPZnoj09u9Df//zkcBLqjo2gNit4DQhUXSonPRuSXUfY+tKjwxer5cvvviCdevWUVhYSGZmJldeeeUMOphHQ83yV+QpKajEU+Mls2ey06FEpZ1fHGT10iwu/uWMSBi7GZzLIolIT+BWYAFwBF8rYTG+7uefAl1EZDHwf6p6sL3RtmIBsKR+sQmgqu+IyF3AGyJyPfA4cFlzxWYzBgD76j3fCwxs6QUikgz8CZggIo8CP2rqy0JEFgIL2xCLMahXee3hdUw9cxCjZvZ1OhwTBHl5efzhD39g8eLFdOnShVGjRpGenk5xcTHAYjonjxpTJ61rIgCFeWVk9oyOLl03GTS2O8lpCZFQbAas1RZOEfkSeAx4XlUPNDG/F3ARcJWqTmlzAIG1cG4BfqCqHzYz/zbgXmC+qr7clu2JyHTgIVWd6X+eDBxU1aDeksFaCEwgVBURoaSggtTMxKhKRpFs0qRJLFy4kAsuuIDevXs3ni0dzaOhZvkrMpUVVfHkLz7mktuPjch7d7tVYV4ZGd2TIym/B6dLXURiAunqCXS5Jl7XYiITkeOAZ4Dh2kSwIjICeA1YAszF171+ONDtiUgf4LN6Xeqj8HWpj2nrvrTEErZpTXlJFa/8eS1nXDmObn0t+UcSr9dLTEyzY7Xq56N25dFQs/wVuUqPVJLaJdHpMKKGqvLMb1Yxde5gRs+KmB6s4HSpB5r8QpgkFwCLmyk2hwFLgYWq+p6IbAWWi8gZqnokkJWraq6IZInI2aq6DLgKeCGI8RsTkKTUeCacMsDGVEWgForNBtxYbJrIltolkcK8MrwetVbOTiAifOvWaVF5dYAOnR4lPie187WLRGSf//d9IrLI//syfzc3IhIPXIzvkkVNSQGuVdX3AFR1EXA/cNThWnPb87sWuFtEtgNjOfrMdGNCpqK0mtWv7UbVd7mSCDlr0QSoI3nUmGBYsyyLrStzW1/QdEhNlYc9m/JJTIkjNj768ny7L4sEICKJQJmqRl+p3kbWJWWaU5Bbyicv7uSMK8cRn2gfpWgjIkm4PI9a/opsHo+X2NjoK4A6W872At5ZvIXv3HFcpBWcQRvDeVELsxOAJ92cKN3CErZprKbaQ1FeBd36WTdWpHv22WebnXfxxRcvwOV51PJX5CstrGTflgJGHdfH6VAiWoQW98EZw4nvhJ1VQGUT8yLuXTOms2xbdYCNH2Zzwc+m23U2I9x3vvMdjjvuOBITmzw54wedHY8xjRUfrmDjh9mMmN6LmMgriBy3e90hUtIT6D00w+lQHBNIC+dG4AZVfbuJebVdQfbf2QprITC1vF71HQ4KVJbVkJQa73RIJsTGjRvHAw88wOmnn37UPP+l2FydRy1/GdMxn7y4k7SuiUw4ZYDToYRCQC0mgSS454CjLhznVwM8GWhExhhY+dJOPnpuOyJixWaUuPDCCzlw4KjLGNeyPGpcwVPtZdWruygvqXI6lIgz6/zhkVpsBqxDJw2ZwFkLgal15EAZNdVeegxIczoU4w6uH09h+Ss6qCpvPr6RqfOGWH4KEq9X+fi57Uw5c3Dd3Z0iUNDGcBpjguDLt/fSe1gGfYZmOh2KMcYcRUQ48+rxTocRUarKa6goqyY+ybXnBHYa144ZMiaSqCplxVVUV3icDsUYY1q04YNstq9pdgiIaYOk1HjO+P44EpOtfa9dBaeI3BDsQIyJVLm7ClGvMmv+cAaO6eZ0OMYlHnjgAadDMKZJMbFCTOTc59sxOduP8N7TW7Chiz7tGsMpImWqmhKCeCKWjYGKTjXVHv515ypOXTCagaOt2DRfS0lJoaysDGwMpzERKW9vMft3HGHiqQOdDiXUgnPh9yZfJFKuqnbD5zawhB19PDVeYuNiqKqoISHJulNMQ8nJyZSXl4MVnMaF9m46zK61eZz83VFOh2LcL2iXRWqKJRVjWlB8uIJ/3vEJRYfKrdg0TbKL/Rs3S+2SSJc+KdYd3E4f/GcbGz/MdjoMV7Eu9U5iLQTRRVX5akM+g8d3t8LCNMm61I2JXHs25pOUFk+vwVFxZ6GQtnAaY5pQUlDBR89vx+tVhkzoYcWmMSZsFR+u4Ll7VlNZVu10KGFFVRk0rnu0FJsBa2/Bad+ixjShpsprlz4yAbGuSuN2aV0SGTWzL2JnrAfM61X+87+rydl+xOlQXKe9XerLVXVuCOKJWNYlFdmqKmooPFhOz0HpTodiwsTcuXNZvnw5hMEBvOWv6KaqoFjhGQCvV9m9No+BY7tF0/j90J2lbtrOEnZk2/RRDptX5PCtW6dZN7ppK9f/w1j+il6qykt//IKJpw1g+JReTofjerVXJ4kyVnC6iSXsyOT1+v5cMTFilz8y7WUFp3G1PRvz6TEwnZSMBKdDcbXDOaW89KfPufQ3M0lMiXc6nM5kBaebWMKOTJ+8tJPK0mpOuXS006GY8GUFpwkLNdUe4uLtnuDN8dR4ydl+JBrvKBeaglNE4lS1pl0hRTFL2JGp6FA5VRUeegxIczoUE0ZqamqIi6trDXdlwSkiC4GF/qfTLH9Ft09e3EFpYRVzrhjrdCiu5KnxgkBsbNR1p0MIL4v0FoCIPN6O1xoTETZ8kE3O9gIyeiRbsWnabM6cOQBcddVVDkfSPFV9TFWnq+p0p2Mxzhszux/HnTfM6TBca9unB3j+3jV29YkWtGfAWbGI/As4TUSe8k8TQFX1e8ELzRj3KiuqsvFMpt3S09P57ne/yzvvvIPlURMOuvT23euloqSapLSoGp8YkGOm96LHgDQ7abQF7b0sUj/g98AvqdeUqqpfBS+0yGJd6pHh0L5iuvZNjdZuExNEOTk53Hbbbfzzn/8cjMvzqOUvA5C1/hDvLt7C5ffMJsZyYJ2qihri4mOi+T0J7UlDIpKBb3xPN+AO4CxVXdqulUUBS9jhz+tV/n3Xp8w4ewgjZvR2OhwTAYqKisjMzLwVl+dRy18GfCcNlRRU0qWX3dm6vo+X7KDkcAVzfzDe6VCcEvJbW/4T2AKcoKoe4KYOrMsYV/N6lZgY4Vs/mcox0+1adCY4LrvsMrA8asJEXHwsXXqlUHSo3OlQXGXGOUOYOd/Gt7amIwVniv9IvPaMdRu4YCJSZXkNz979KXl7iklKjbcxOiZoysrKsDxqwklZURVP/2olBbmlTofiCuXFVcQnxJLZ01p9W9ORgnObiNwGdBOR/wE2BimmsCAi/UXkHyLyidOxmNBKSIpl2rwhdOuX6nQoJsKMHDmSaM6jJvykZCSw4O5ZdO1j+RDgncVbWPXKLqfDCAvtKjjF18TzOr7k+DSwU1VvaOM6HhGRbBFpccyPiKSKyJMisk1EtojID9sTc2vbFJHxIvK5iGwXkVdEpMWbYqtqtqpeCeS3Nx7jbjXVHtYs242nxsuIGb2j8XZlJoRUlXnz5kEH8qgxTkjrmkTRoXIqSqudDsVxc64Yw8TTBjodRlho1zeo+s40+r6qLlXVP6jqK+1YzTPA1ACWux/YqqojgTHAi/VnikiaiPRpNG2YiDR1O4SWtvkocLuqjsA3puqn/nVNFJHXGz3OCCBuE+YqSqrZv7OI6gqP06GYCCQiPPHEE3QwjxrjiGWPrGP3l4ecDsNRZUVVJCTH2SXyAtSRs9RfAhKBzwAvgKr+qh3rafbsR38r43ZgoKo2eSglIqcBfwbmqOoBERkPvITvbM/tgWxTRHoDn6tqf//zkcBLqtrqLRVEZKmqnhvAcnaWZ5hQVQrzyu1MTBNy8+fP5+WXX36dDubRULP8ZRqrLKuOtvuFH2XJfZ8xfGpPJs8Z5HQoTgv5ZZFObjxNVd9vx3paKjgn4Tsb/kNgJrAPuElVdzVa7iJ81wS9HngcWKCqKwPdpohMAx5W1eP8z5OBg6rabLe6f5k/AWcB/wV+pKreRsvYreHCUPbWApb/fQML7p5NfILdN9iEzvvvv88pp5xySv1p7cmjoWYFp2lKSUElleXVdO8XnXdbqyipBoGk1OguvAl1wRksrRSc04HVwDmqukxErgSuUNWTmlj2NuBeYL6qvtyWbfq385CqzvQ/b7XgbCtL2OFBVRERSgoqSOua5HQ4Jjq4/sx0y1+mKR/+Zxtej3Lyd0c5HUqnKy2sJCUjwa5a4hMRBWdvYJuqZvqfp+ArBNMaLTcCeA1YAszF171+ONBt+seAflavS30Uvi71MR3bu+a32QJL2A7ZszGfjR/mMG/heCTGkojpNK7/Z7P8ZZriqfYSEydRV3SpKv+6cxXT5g1m9Ky+TofjBiG/8PvXWxLpJyJviMgVwVhfLVU9AHwpIsf5J50BrG+07WHAUmChqv4ceABYLiJd2rCdXCBLRM72T7oKeKGD4Zsw07VvKkMn9bBi0zgiVHnUmFCJjY+hoqSarzZG18VaRITzb5nKsCk9nQ4lrASl4FTVHHxjGQO+hpyILBKRff7f94nIIv/vy/xd3LWuAf6fiKwDbgGubLSqFOBaVX3PH8sifGe2Jwa6Tb9rgbtFZDswFrgv0H0x4e3IgTL2bTlMerckO1o1jmlPHjXGaQd2F7F66W6c7i3tTGVFVSSnx5OQFOd0KGHF8S71aGFdUu614f195O4uYs4VrV6UwJhQcH2TuuUv05zaGiJautVVlad+sYLZ3z6GEdN7Ox2OW4S2S11Evu//OcPfKnlhe9dljBOqKz14qr2MP3kAp18etOG6xgTsiSeeACyPmvAlIlRXetjwQTbqjfzjDRHhgp9NZ/D47k6HEnY60qW+wP/zx/i6uW/reDjGdJ6Pnt/OR8/7LtUaLUfnxl0WL15c+6vlURO2vB5lwwfZlBypdDqUkKsoqSY1M9G609uhI+9YkojMAkpVNVdESoMVlDGd4bhvDMPrifwjcuNeFRUVWB414S4pNZ5Lbj/W6TBCTlV59nermfWt4dad3g4daeG8Bd8A99+KSBJ2VrcJE2v+m0XW+kOkZCSQ1vWoc8uM6TT3338/WB41EUBVWf/ePooOlTsdSsiICOf/ZKp1p7dTm1o4/eOLpgGFqnoP8Em92X8OZmDGhEpCUizxiXYHIeOM5557js8++4zMzEx+/vOfN76VpeVRE5ZEhP07C+naN5WMHslOhxMSlWXVpHezG4K0V8BnqYvIQ/iKzeeBX6lqpoicDoxQ1UdDGGNEsLM8nXdgdxHd+6cSZ7erNA657rrr+Oyzz7jgggv47W9/S2FhISIyB5fnUctfJtqpKv+84xNmzrfu9CYE905DInIIGKaqRSJSoKpdRaQX8KGqRt99rdrIEraz1Ks8//s1TDx1AKNm2rU2jTN69OjBrl27yMjIoGvXrhQUFNTeUc3VedTylwnU9jUHiEuIZejEHk6HEnRFh8pJTI0nMdlOGGokoIKzLe9aAY0upq6qB/23hTTGtbxeJSZGmH/zVOISgnKvA2PapWvXrlRWNjyT1/KoiSTlxVUROWSppsoTsUMFOktbvn3/DjwtIj3wH8X672FeFIrAjAkGT7WX5+5ZTfa2AuITY+3yR8ZRV199NZdeeimHDh2q+1+0PGoiycRTBzJmdj+nwwi6F+//nE0f5TgdRlhrSwvnH4DuwC58l0R6BN/ZlQ+GIjBjgiE2PoYZZw+l15AMp0MxhltvvZX8/HyGDRtGRUUF1157LcCbWB41ESR7awEHsoqYOnew06EEzRlXjiPButI7pNUxnCLyBvA6sFxVN4pIP+B0oCuwVlU/CH2Y4c/GQHUu9SpfvLWH8Sf2tyRhHHfmmWcyb9485s6dy7hx48jJyeHtt9+moKCAG2+88WS351HLX6YtsrcWkLPjCDPOGep0KEHh8XiJjbXhWC0IzklDInIcMNf/6A+8ja8AfUtVCzoYZNSwhN25KkqrWf63DZz83VF06ZXidDgmyq1atYrly5ezfPlysrOzOf3005k3bx5z5syha9eurh/nYfnLRLNlj6yj7/AuTDlzkNOhuFVwz1IHEJFM4Ax8xeccIBdYjq8AXaVtWVmUsYTdeUqPVJLaxS7obtypsLCQN998k+XLl/PWW2+RlZW1EpfnUctfpq0O7y/li+VfcdrlY8J+7Pzh/aXExcfYSUPNC37BedSLRcYA8/AVoG+p6v+1e2URzhJ25yg6VM4zd33Kd351LBndLTkY9xORsbg8j1r+Mm1VXlLF569/xXHnDQvrax+rV5GY8C6YO0HoC04TOEvYnefIgTK69LZudBM2XP9tZvnLRKsPntlKQkocM7853OlQ3CygHNbuUbAiMqTR8yntXZcxHVV8uILlf9tAVXmNFZsmbGRlZTV4bnnURJLK8hpeffBLSgsrW1/Ypcad1J9jpvVyOoyI0JHTrh4UkRtEJElE7gZ+EqygjGmruPgYMnom24XdTVi5/vrrsTxqIlVCUix9hmWEdbt39/5p9BiQ7nQYEaGjYzgfAy4BblfVB4IWVQSyLqnQ8Hi8HNhVSL8RXZ0OxZh2EZG/4fI8avnLRKONH2aTu6uQ0y8f63QobhfyLvU/AvH4zlqfJyLXtnddxrTX/h2FvP3kZmqqPE6HYkyb3XzzzWB51ES4Nx7fyFcb8p0Oo836HtOFEdN7Ox1GxGh3C6eInKOqr/l/F+AGVf1zMIOLJNZCEHy190ivrvIQH8ZnQZro9dprr3HOOecIuDuPWv4yHbF1VS69BqfTtU+q06GY0LCz1N3EEnZwZa0/xGf//YrzfzKVGLtkhQlvrv8Htvxlok3urkLWLMvinB9PDPvriHaC0HapG+OkvsMzmTp3kBWbxhgTBla9uotVr+5yOoyAJacnMGxKTys2gygoBaeI9BORN0TkimCsz5jmFB0qZ9vqXBJT4hk6qafT4RgTNJZHTSQbOLobQyb0cDqMgGX2TGbs8f2cDiOiBKXgVNUc4CxgYzDWZ0xzDu0tYfeXh7ChICbSWB41kazfiC70HpKBet2fu6vKa1j6ly8pL6lyOpSI0pGz1L/v/zlDRF4DzlfV1UGLzJh6PB4vVeU1DJvSkzOvGmfdHCYiPPHEE4DlURMdtq7KZelDXzodRqs8NV569E8jMSXe6VAiSkdaOBf4f/4YuAr4WcfDMaZpa9/cw/K/bwCwYtNEjMWLF9f+annURLy+x2Qyde5gp8NoVXJ6AjPnD7dzBIKsIwVnkojMAkpVNRcoDVJMxhxl4qkDOfGikU6HYUxQVVRUYHnURIuM7sn0H9UVr8frdCgt+uTFHeTtKXY6jIjTkYLzFnzjjX4rIknAC8EJyZiv7Vqbx4b39xGfGGv3SDcR5/777wfLoyaK5O4uZPHtn1BT7c6bdagqXi/ExFrrZrC16TqcInIhMA0oVNV7QhZVGBCR/sBdwBhVnRXA8nYdu3bY9UUepYWVTDhlgNOhGBMUzz33HJ999hmZmZn8/Oc/B7sOp4kinmov2dsLGDi6G2Jd1pEiuNfhFJGHgJuBPPzjjETkdBG5pl3RiTwiItki0mKCEpEsEdkkImv9j3bf1LSlbYrIeBH5XES2i8grIpLe0rpUNVtVrwTC735dYaC0sJLykiqGTelpxaaJGNdddx1//OMf6dmzJ/feey/QsTxqTLiJjY9h0NjueFzarb59zQG+2mhf66EQcAuniBwChqlqkYgUqGpXEekFfKiqo9q8YZGTgK1AbktHziKSBZyiqlnNzE8D0vzjn2qnDQO+UlVPo2Wb3aaIfAT8TlWXich9QKWq3iEiE4H7Gm32flV90/+6pap6bgD7ay0EbfDev7aiHi+nLhjjdCjGBE2PHj3YtWsXGRkZdO3alYKCAkSkN+3Mo53F8pcJpvLiKv55xydc+IsZdOnlrqFSa9/aQ2JKPGNm93U6lHAS3Ftbish2YLaq5tUWnP7phaqa2e4oW0lkARScpwF/Buao6gERGQ+8BJylqtsD2aY/4X+uqv39z0cCL6lqq62pVnCGhqfai6fGS0JynNOhGBM0I0aMYMWKFfTs2bOu4MR3G/UO5dFQs/xlgi1n+xH6DMsgJtZueBgBgn5ry78DT4tID/xJRURGAEVtj63NXhKRL0Xk9yKSUH+Gqr6DbyzlG/4WzBeBy5orNpsxANhX7/leYGBLLxCRZBF5FJggIo+KyFHvpYgsFJE1IrKmDbFEtVWv7GLdu/uIjY+xYtNEnKuvvppLL72UQ4cO1V3eqxPzqDGu0W9EFzweddUZ6/nZJWxfc8DpMCJWW77R/wB0B3YBiSLyCL6zKx8MRWD1nKCq+/xd54uBnwL/W38BVX1WRIYC7wPzVXVlG7chtPHIXFXLgRbHXanqY8Bj4GshaGNMUanP8EziE2OdDsOYkLj11lvJz89n2LBhVFZWcu211wK8SejzqDGuol7l379dxfHfHsGwKe64TXHhwXL2bDrMiOm9nQ4lIrXpLHXw3e8XOB3oCqxV1Q86FEDgXTWIyLnAD1X1G42mjwBeA5YAc/F1rx8OdJsi0gf4rF6X+ih8XepBG0BoXVItO5BVRFqXRFK7JDodijEhl5OTw9tvv01BQQE33njjyR3No6Fm+cuEwqF9xXTtm0qsdauHu+B0qYvIBfWfq2qOqi5W1QfqJ8nGywWDiKSKSIb/9zjg28C6RssMA5YCC1X158ADwHIR6RLodvwnHGWJyNn+SVdh18PrVF++vZfNn+x3OgxjQuL5559v8Lxfv34sWLCAG264gVDnUWPcqseAdPD67l3uNPUq29ccoLrSndcHjQSBHFacLiJbReReETlHREaKSD//z3P807cCp7ZlwyKySET2+X/fJyKL/L8vE5Hp/sV6Ax+IyDp8haYHuLvRqlKAa1X1PQBVXQTcDxzVVNbcNv2uBe72nxw1lqPPTDchUDt+Z84VY5gWBrc8M6Y93n77bUaNGsXPfvYzXnvtNbZt20ZOTg7btm2jI3nUmHC37JF1fPnOXqfDoLSwkhVLduCpcc+Y0kgTUJe6/yLn38PXXT0OyASOABuA5cBTqmrNUy2wLqmm/fev6xkwqqtda9NEvOzsbJ566imWL1/Oxo0bKSwspEuXLuTl5b2Ly/Oo5S8TKoV55aR2SSAu3sbuh7HgXhbJdIwl7Kbl7DhCerck0rslOR2KMU5x/e1WLH+ZUPJ4vFSUVJOa6dwY/ry9xaR3TSIpLd6xGMJY0C+LZEzQ7PziIIV55fQ7posVm8YYE8U+eXEnHz3blisZBt8Hz2xl5xcHHY0h0lkLZyexFoKvqSpv/mMTQyf2YMQMu/yEiXrWwmmiWnlxFXGJscQnONetrqp4vWpnzLePdam7iSVsn6qKGhKS4lDVugtfGxPlXP9BsPxlQs3r8VJ8uILMnp1/q8uKkmokBhJTrDu9naxL3biLqvLKn9ey6eMcKzaNcYCIpIjIkyLyFxH5pdPxGFNry8pclj2yHicawda/v4/XH9vQ6duNNtbC2UmshcDn0L5iMnokk5Bkt600xq/dR1/+O76dB/RrdDOL8cBTQDqwGbhUVYtF5DKgWlX/IyJPATe1dJOMeuuz/GVCqrrKg3rVke8Gr8dLZVkNyekJrS9smmItnMY9vnx7L4dzSukxIN2KTWOC5xlgahPTHwVuV9URwBZ8twQGGAjs8f++F+gX8giNCUB8gm8MZ97e4k7ftterVmx2Ais4TcipVzmcU0JFabXToRgTUVT1A1U9UH+aiPQGhqrqMv+kv+O7Sxv4isyB/t8HADmdEqgxATj4VTEv3f95p97tp7Swkr/d+AElBZWdts1oZV3qnSRau6RKCytJyUiwMZvGNK/DH476+UVEpgEPq+px/ufJwEFVTReRFOBhoNA/rfGd2xCRO4FfN54ejfnLdC5VparCQ2Jy5/WCeTxeDu0podeQdPueaj87S91NorHgVK/yzF2fMm3eYEYd18fpcIxxq2AXnNOBh1R1pv95XcEZjPW3ImLyl3GGqpK7q4g+wzKsAAwfNobTOEtihHN+NIER03s5HYox0WQfX3ebAwzyTzPG9SpLa3j1gbUcOVDWKdtb/vcNrH/PPh6dwVo4O0k0tRCoKp+8uJMR03vTc1C7G1WMiRZBbeH0P/8YuFtVl4nIffjOTG/3ZZCiKX8Z51VXeTrtIvD7dxaSlBpH1z6pnbK9CGUtnMYZqr5HTJx1hxgTSiKySET2+X/fJyKL/LOuBe4Wke3AWOA+h0I0ps3i4mPI3VVIVUVNyLfVd3imFZudxFo4O0m0tBAcOVBGZs9kJMaKTWMC5PoPS7TkL+MOXq/y9K8+4eTvjmLQ2O4h287eTYfZtCKHuVePD9k2ooS1cJrO5fUqS//yJTs+P+h0KMYYY8JUTIzw3V/PDGmxCZDWLZHB40O7DfM1a+HsJNHSQmCXQTKmzVz/YYmW/GXc5eBXRSSnJ5DeLcnpUEzLrIXTdA71Ku8s3kzurkJSMxOt2DTGGNNhK17Yyd7Nrd55td0+eGarI3c2ilZ2j0ETFBndk0nJsFuDGWOMCY7zbphETGxo2sVUlfik2E47G95Yl3qnidQuqby9xXTvlxqypGBMFHBll4CILAQW+p9Oi8T8ZdwvP7sEVegxIM3pUEzzrEvdhJbX4+WNv29k19pDTodijAkyVX1MVaer6nSnYzHRa/17+9i1Ni/o683ZXsCWlfuDvl7TPGvh7CSR2sJZWVZNQnKcjds0pv1c/+GJ1Pxl3E+9GpLL7G1fc4DD+0s57hvDgr7uKGT3UneTSErYXq/y5j82Mv6k/vQf2dXpcIwJd1ZwGtOCIwfKKC2stO8b97IudRMaItBnWCaZPVOcDsUYY0yEy1p/iK2rcoO2PlVl84ocKkqrg7ZO0zo7S920Se7uQnoOSGfSaQOdDsUYY0wUmHT6wKAO26qu8LD2rb0MGN2NpNT4oK3XtMy61DtJJHRJeb3Ks3d/yrSzhjBiem+nwzEmUliXujGtKD5cQd6eYoZN7ul0KOZoNobTTSIlYVdXeohLiLGThIwJHtd/mCIlf5nwtXvdITa8v49zr5vU4e+fIwfKkBhsWFjwWMHpJuGcsNWrLP/bBsae0I9B4+y+s8YEmRWcxrRCVYPW0PHxkh1UllVz2oIxQVmfsYLTVcI5YfsGWO9n0NhupHW1e9oaE2RWcBoTgLKiKr7acIgxs/t1eF2hutxSlLKz1E3H5e4qpKbKy9jj+1mxaYwxxjGVZdVseD+bmipPh9ZTUlARBod5kccKTtMsVeWj57az84uDTodijOlkIrJQRNaIyBqnYzEGoGufVC78+QziOnD/8/KSKp78+QoKD5YHMTITCOtSbycR6Q/cBYxR1VkBLB9WXVK142VqqjzExttJQsaEkOs/XOGWv0zkqiyrZsvKXCaeMqBdXeKqSumRKlIyE4ixLvVgcXeXuog8IiLZIhJQghKRvwe6bHu2KSLjReRzEdkuIq+ISHpL61LVbFW9EsjvSExupKos/9tGdn5xkLiEWCs2jTHGuIIqbFuVS1lxVTtXAGldE63YdICTXerPAFMDWVBETqeZi9SLSJqI9Gk0bZiINNXm3tI2HwVuV9URwBbgp/51TRSR1xs9zggk7nAlIgyf0pOeA1usuY0xxphOlZQaz4U/n0FqZmK7Xr/ixZ28s3hzkKMygXC8S721rhoRSQXeAc4GDjVeVkROA/4MzFHVAyIyHngJOEtVtweyTRHpDXyuqv39z0cCL6nq2ADiX6qq53Z0P+tx9A+Su7uQLr1S7O4LxnQe1ze1hEv+MtGhpsrDhg+yGXdif+IT2zaes+hQOdVVHrr3SwtRdFHJ3V3qbXAP8GdVbbLrWlXfwTeW8g0ROQl4EbisuWKzGQOAffWe7wVavHejiCSLyKPABBF5VESOei/DcdD96ld3s331AafDMMYYY5oUEytkrTtEUX7bT/zJ6JFsxaZDXN3CKSKzgV+q6jkBLHsbcC8wX1Vfbss2RWQ68JCqzvQ/TwYOqmrQ+pTd3kJQe5KQp9pLTJzYuE1jOo/rP2xuz1/GBKKm2sNz96zh7Gsnktkz2elwIklEtHCeBEwRkSwRyQLw/97gZqoiMgK4Cl/B+WsR6dbG7eyjYYvmIBq2eEa8d/+5hc0r9tsZ6cYYY1xPvcq6d/dSfLgi8NcoTDp9ICkZCSGMzDTH1QWnqt6rqv1UdYiqDvFPG6KqebXLiMgwYCmwUFV/DjwALBeRLm3YTi6QJSJn+yddBbwQpN0IC0Mn9aTXEDtJyBhjjPtJjJCzvbBNBWd8Qixjj+/X5nGfJjgc61IXkUXAHKA/kA28papXiMgy4FeqetS4x6a6dfwnCfXyj+WsnXYJ8K6qHmi0bJPb9M+bCDwJpAFbgUtVtTA4e+veLqlD+4pJyUi0Iz5jnOP6LgW35i9j2mLbp7mUHKlk6pmDnQ4l0ti91N3ErQn7jcc30qVXMsd+Y1hnbtYY8zUrOI1pp00f59BzUHpAl/HbszGf8pJqRh3Xp9VlTZtYwekmbk3YXo8XBWJjXT26wphIZgWnMe30wX+2MXB0V4ZO6tn6wiZUrOB0E7cl7JUv7yQpNZ7JcwZ1xuaMMc1zZcEpIguBhf6n09yUv4xpj9Wv7WbksX3sDPXgCyiHNXn3HhP5BozuRkysK7/njDEuoKqPAY+B74DZ4XCMadb2NQdISotn4OjmL1CjqhzOKcXr8XZiZKY+KzijTGFeOTGxwoBRXZ0OxRhjjOmwwrxyPNUtF5IiwtwfjO+kiExTrEu9k7ilS/2j57ZTVVHDaQvGhHIzxpjAub6rwS35y5j2OnKgjIIDZQyd2MPpUCKRdambox3/7WPw1FiXgjHGmMiRtf4Q1ZUeRkzv3eT8/OwStq0+YAWng6zgjBKbPsqhMK+cWecPJy7BLnprjDEmcpQVVVFVXtPs/OFTezF8aq9OjMg0ZgVnlOjeP43EVPtzG2OMiTxjj+/X4vy8vcWkd00iKS2+kyIyjdnFFyNcRWk1Rw6U0XtoBsOn2NGdMcaYyJS9rYCNH2Y3Oe+dpzaTteFQJ0dk6rOCM8Jt+WQ/7z+z1ekwjDHGmJCqqvBQcqSyyXkX/WIGI4+1Oww5yc5S7yROneWpqlRVeEhMtu50Y1zKzlI3JoQ8Hi+eKi8J9j0YKgHlMGvhjFA52wt49+ktAFZsGmOMiQoHsor4/I2vGkzL+6qYx2/5EK/XjoecZAVnhEpMiadb31REXN94YowxxgSF16OUHalqMK3noHS+c+dxxMTY96GTrEu9k3RWl5TXqxTlldOld0pHVmOM6Tyu/xa0LnVjTAusSz0aZa07xKsPrsVj94s1xhgThQ7nlLLqlV11zz96fjsrluxwMCIDdh3OiDNsck/6DMskNtaOJYwx7SciC4GFTsdhTFtJDJQVVuL1eImJjWHMrL5Yb67zrEu9k4S6S6qkoILVr2VxwkUjiLc7CRkTTqxL3RgTzqxLPZp4apS4hBji4uxPaowxJroVHSrnw/9sQ1V5+f99QX52idMhRT3rUo8AJQWVZPZM5sSLRjodijHGGOO4+MRYqsprqKrwMHh8d7ulpQtYl3onCVWX1OGcUp77/RoW3DWLlIyEdkZnjHGQdakbY8JZQDnMCs5OEsqEfeRgGV162WWQjAlTVnAaEwJlRVW8+uBaBo3txqzzj3E6nEhmYzgjmcfjZcULO6goqbZi0xhjjGkkITkWT7WXitIap0Mx2BjOsFVVXkPRoQqnwzDGGGNcKS4+lu/eOdPpMIyfdal3EuuSMsY0w7rUjTHhzLrUjTHGGGOM86zgNMYYY4wxIWUFpzHGGGOMCSkrOI0xxhhjTEhZwWmMMcYYY0LKCk5jjDHGGBNSVnAaY4wxxpiQsoLTGGOMMcaElN1pyBhjzFFEZCGw0Ok4jDGRwe401EnsTh3GmGbYnYaMMeHM7jRkjDHGGGOcZ13q7uP61g5jjGmG5S9jTJOsS90YY4wxxoSUdakbY4wxxpiQsoLTGGOMMcaElBWcxhhjjDEmpKzgNMYYY4wxIWUFpzHGGGOMCSkrOI0xxhhjTEhZwWmMMcYYY0LKCk5jjDHGGBNSdqchlxERuxK/MVEmwPuUu57lL2OiUyA5zO40FOZERCPly6o10bKv0bKfED37Gi372Zjb99vi6xiLr2OiLT7rUjfGGGOMMSFlBacxxhhjjAkpKziNMcYYY0xIWcEZ/n7jdACdKFr2NVr2E6JnX6NlPxtz+35bfB1j8XVMVMVnJw0ZY4wxxpiQshZOY4wxxhgTUlZwupSI/L32mnYikiEib4nIERF5r9FyiSLylIisE5FNIvKQiMTUe92rIrJdRD4TkfEO7EqrGu3rbBH5RETWi8gGEbmh0bL3isgOEdkmIhfVm+76fQ10P0Vkof/v+aWIrBGR0+rNc/1+Qtv+pv5lRohImYjcWW+a6/e1jf+7Z/v/rhv8f9tw+5w+IiLZ9a+1KSJDRKRKRNb6HysavabTPq9NxeefPlJE3hWRzf7HNLfEJyKX1nvv1opIqYjc6ER8zcXon36L//92nYi8LiK9nIixmfcwTkQe9ce3UUQWNnpNp8QnIt1F5L/i+x5e788NCf55F/m3v0NE7nFTfNJCfRH0+FTVHi57AKcDi3x/HgVIAk4BzgXea7Ts1cC//b/HAp8AZ/uf/y9wn//3s4EPnd63APZ1PDDU/3sGsA2Y6n8+B/gY3w0L+gP7gIxw2Nc27udJ9fZrAnAQiA2H/WzrvvqnCfAG8G/gznrTXb2vbfybDga2AP39z3vx9ZAmV+9nvf09Cehdu7/+aUOArGaW79TPazPxxQBfAif5nycBmW6Jr9H8NKCw3v9Ip+e7Zt7DEcBuINn//L5623b8PQSuBZ73/62TgJXAkM6OD+gGHF/v/+4Z4GYg07/d/v44VgCnuyi+luqLoMbX4X9QewT3AaQCq4DujROT/5+i8T/ED4ClQLz/tZ/z9ZfcZmBkvWWzgd5O72Mg+1pvmVeB8/2/PwIsrDfvX8BFbt/Xtu5no+mC70uoq9v3s737CvwYuAO4k4YFp2v3tR3/u/cAtzSznGv3s5l4td7vQ2i+4HTk89oovrnAq26Nr9H07wFvOB1fE+/hSHyFRnd/PvorcJNb3kPgL8C19Z7/EbjVBe/hLcCfgYuBf9WbvhB42C3x1Xt+CkfXF0GNz7rU3ecefP8E+QEu/yRQAuwHDgCvqern/nkDgL31lt3nn+YWLe6riIwAjgU+8k9qvD97gYHNzHPTvrZ1P+v7LrBZVQv8z928n9DGfRWRQcAC4PdNLO7mfW3r33Q00FdE3heRz0XkpnqLu3k/A9Hb36W2WkS+X2+6Gz6vo4FSEXlFRL7wd8mmuCi++r4HPFXvuSviU9VtwP1AFpCLrwB90EUxfgacLyJJItIV30FGczF0SnwikgRcAfy3jTE4EV9LghqfFZwuIiKzgeGq+q82vOxMoAjoAwwCzhOR42tX2XgTHY8yOFrbVxHpDrwI/FhV82onA1p/sWZ+b+q5I9q5n7XzZgB34/siqpvceBVBDLdD2rmvjwL/o6pVTb2kleeOaOd+xgPTgbOA04Dvy9djc125nwHaDwxU1WnABcAvRORE/zw3fF7j8Q19uA7f+58E/MxF8flWLtIfX3wvNNqm4/GJyGBgHr5hIf2BPcBtLopxEbAGX1f6c/i6gGucik98Y7OfAN5V1dcDiMHp+FpcnCDGZwWnu5wETBGRLBHJAvD/3rOF11wDvKiqNap6GFgO1Bac9Y9GwHf0sS/4YbdLs/sqIun4jrweVtXn671mL76iutZAvt4ft+5re/YTERkHPIuv+2JbvVlu3U9o377OBJ7xL38TcJOI3O2f59Z9bc9+7gFeVtUyVT2C73M61T/PrfvZKlWtVNVD/t+/Al7G17IL7vi87gFWqOoeVfXgK0jqv+9Ox1frMnx5vKzeNLfEdyGwVlUPq2oN8B/gBLfEqKpeVf2Fqk5W1TlANbDVwfj+4v95U4AxOB1fS4IbX7DGA9gj+A8CG8P5F+BP/t+TgE+Bc/zP76bhoN6Pnd6n1vYVSAbeB37exDJn8PUA5n74xoxkhtO+Brifw4FdwKlNzAuL/Qx0XxstfycNx3CGxb4G+Dc9Dd+YzlggEd/JfXPDaT8b76//915AnP/3rsA64Ez/c0c+r43iy8BXfHTzP/8jcI9b4qs3bUPjz7uT+a7Re3ghvtbDRP/zB4A/uOU99H/u0v2/jwV21nveqfHhO6FqKRBfb1qmf7v9/HF8DJzhlvjqzTuFo+uLoMYXtH9QewT/0ehDtRXIAyrxHUX80D+9l/8faAOwCfhdvddk+udtB74AJji9T63tK74TSGqAtfUeF9db7j5/QtkOXBJu+xrIfuJrhSloNG94OO1nW/6m9Za/k4YFZ1jsaxv+d+/CN9B+I/DrMNzPRf7co/6fi4Bv+XPPl/6ftzR6Tad9XpuKzz+9Nsb1+K6EkO6y+KYAX+G/aoFT718Lf+MYfCe/bPa/j0uALm55D/G1sm3B9/33KTDTifcQGOePazNff/5rC/NL/NvZib9Ic1l8TdYXwY7P7jRkjDHGGGNCysZwGmOMMcaYkLKC0xhjjDHGhJQVnMYYY4wxJqSs4DTGGGOMMSFlBacxxhhjjAkpKziNMcYYY0xIWcFpTJgSkX7++3JXiEic29ZnjDGtsTwWPazgNCZ8HcZ3n+iVLl2fMca0xvJYlLDq35gwpaoVQIWIuHJ9xhjTGstj0cNaOI3pIBGZKSJvNjMvS0Rm+n9XEenTSTHVbdf//Lcisk1E+nXG9o0x4cXymAk1a+E0puPmAstDsWIR6Q883WjyYVX9VhvW8RvgO8ApvqfyXkfWZ4yJSCHLY9DxXGZ5LPxZwWmijvj6Wq4BrgMGAduAM1T1cDtXORf4YRtjuBj4LXCmqn7lP4p/HOgPPARcAFyjqu/hS7DtIiK/Ai4FTlHVbP/kdq/PGOOsEOSvWiHNY/78c0p7ArM8FhmsS91EozuBBcB8oAvwM+BIe1YkIl2AAaq6vg2vWQD8BpjjT9KJwBLg90BPoAIYHsB6kkTkLWASsFxEZjda5DbgcuBUVd0XhPUZY5x3J0HKX7Usj5nOYC2cJqqISF/gRmCyqmb5J78pIqeIyBzgeWCaqj4e4CrPAN5uQwhXApcBp6lqjn/aLKBUVZ/yx/h7fF8iLfIPjp/TwiJzgKdVdW8ggQWwPmOMg1rIX5NFpC15qzHLYybkrIXTRJs5wBf1knUDqrq2jUl7LvB6G5a/CXisXpIG6APUHbmrajWQ14Z1Nudq4BwRuSMI6zLGOK/J/NWOvNWY5TETclZwmmjTDdjf3Ex/S+f/iki8iLwoIm+KyD9E5M5mXjIHeKsN2z8fuEVE5teblotvzFNtDPH4uqQ66ivgTOAGEflxENZnjHFWk/mrXt4SEXlMRD4QkeX+eSIij4jIOyKyVEQym1iv5TETclZwmmizDjhNRIaLSIyIzBCRppLi+cDnqnoGkNXUikRkLHBAVfPbsP2dwNnAwyJyun/aJ0C6iFzmT9K3AoltWGezVHUzMA+4W0QuDcY6jTGOaS1/fRMoUNWTgLP8084F9qjqacBfgYX1V2h5zHQWKzhNVFHVd4FHgY/xDbR/AN/g9saGAZ/7f/+smdXNI7DLiGijGNYD3wL+KSIzVbUSuBD4BXAIyMB3VF8ZwLpb37jqZ/hOMHhERL4RjHUaYzpfAPlrFPChf1mvf9oY4BL/ZYRuA7o3Wq3lMdMpRFVbX8qYCCcip/B1t9IcfC0JI1X1f0XkdiBOVe9s9JrlwG9UdUUL6z0EzFTVHW2IJREoAEar6p427ooxJsrUy19rgONU9eciEqOqXhH5JnCMqt7vXzbeP76y9rWWx0ynsBZOY5r2EjDNf+eNY5pZ5m1gVXMrEJFT8bUKtJps/WOwuvuT9G+BrZakjTFt9ArQS0Q+AP5bb9oQ/xjOd/i6q72W5THTKayF05hW+C+XdELjFs5WXvNXfAPdb1HVFwJY/nrgV/jGPK0FFqrqlnYFbIwxQWB5zASTFZzGtKI9BacxxhhjvmYFpzHGGGOMCSkbw2mMMcYYY0LKCk5jjDHGGBNSVnAaY4wxxpiQsoLTGGOMMcaElBWcxhhjjDEmpKzgNMYYY4wxIWUFpzHGGGOMCSkrOI0xxhhjTEhZwWmMMcYYY0LKCk5jjDHGGBNS/x8XV7cJHQSVcgAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 720x360 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "fig = plt.figure(figsize=(10,5))\n",
- "\n",
- "cl_1 = (iapws._iapws._Liquid(265, 0.1)['cp'])*1000.\n",
- "cl_2 = (iapws._iapws._Liquid(305, 0.1)['cp'])*1000\n",
- "ci_1 = (iapws._iapws._Ice(193, 0.01)['cp'])*1000.\n",
- "ci_2 = (iapws._iapws._Ice(273, 0.10)['cp'])*1000\n",
- "\n",
- "cls = np.arange(cl_2,cl_1)\n",
- "err = np.zeros(len(cls))\n",
- "\n",
- "ax1 = plt.subplot(1,2,1)\n",
- "ax1.set_xlabel('$c_\\mathrm{liq}$ / Jkg$^{-1}$K$^{-1}$')\n",
- "ax1.set_ylabel('$(e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1)_\\mathrm{max}$ / %')\n",
- "ax1.set_yscale('log')\n",
- "\n",
- "state = 'liq'\n",
- "TK = np.arange(260,300,0.5)\n",
- "es_ref = es(TK,formula=\"wagner-pruss\",state=state)\n",
- "for i,cx in enumerate(cls):\n",
- " c1 = (cpv-cx)/Rv\n",
- " c2 = lvT/(Rv*TvT) - c1\n",
- " es_a = PvT * np.exp(c2*(1.-TvT/TK)) * (TK/TvT)**c1\n",
- " err[i] = np.max(np.abs(es_a/es_ref -1.))*100.\n",
- "ax1.plot(cls,err,c='tab:purple',ls='dotted',label='Analytic $c_\\mathrm{liq}$ for $T\\in$ (260K,305K)')\n",
- "ax1.legend(loc=\"upper left\",ncol=2)\n",
- "\n",
- "cis = np.arange(ci_1,ci_2)\n",
- "err = np.zeros(len(cis))\n",
- "\n",
- "ax2 = plt.subplot(1,2,2)\n",
- "ax2.set_xlabel('$c_\\mathrm{ice}$ / Jkg$^{-1}$K$^{-1}$')\n",
- "ax2.set_ylabel('$(e_{\\mathrm{s,x}}/e_{\\mathrm{s,ref}} - 1)_\\mathrm{max}$ / %')\n",
- "ax2.set_yscale('log')\n",
- "\n",
- "state = 'ice'\n",
- "TK = np.arange(180,273,0.5)\n",
- "es_ref = es(TK,formula=\"wagner-pruss\",state=state)\n",
- "for i,cx in enumerate(cis):\n",
- " c1 = (cpv-cx)/Rv\n",
- " c2 = lsT/(Rv*TvT) - c1\n",
- " es_a = PvT * np.exp(c2*(1.-TvT/TK)) * (TK/TvT)**c1\n",
- " err[i] = np.max(np.abs(es_a/es_ref -1.))*100.\n",
- "ax2.plot(cis,err,c='tab:purple',ls='dotted',label='Analytic $c_\\mathrm{ice}$ for $T\\in$ (193K,273K)')\n",
- "ax2.legend(loc=\"upper right\",ncol=2)\n",
- "\n",
- "sns.set_context(\"paper\", font_scale=1.2)\n",
- "sns.despine(offset=10)\n",
- "\n",
- "fig.savefig(plot_dir+'es-analytic-fits.pdf')\n",
- "Tfit = 305\n",
- "print ('Taking fit for $c_\\mathrm{liq}=$ %3.2f J/(kg K) at $T=$ %3.2f K'%(iapws._iapws._Liquid(Tfit, 0.1)['cp']*1000.,Tfit))\n",
- "Tfit = 247.065\n",
- "print ('Taking fit for $c_\\mathrm{ice}=$ %3.2f J/(kg K) at $T=$ %3.2f K'%(iapws._iapws._Ice(Tfit, 0.1)['cp']*1000.,Tfit))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## RCEMIP comparision ##\n",
- "\n",
- "During RCEMIP (Wing et al.) different models output different RH, differing in ways of calculating it and also whether or not it was calculated relative to liquid or ice. In this analysis we create a python implementation of the intial RCEMIP sounding and then for the given state estimate the RH using different formulat and different assumptions regarding the reference condensate (liquid/ice). We also show the difference associated with 1 K of temperature. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "def rcemip_on_z(z,SST):\n",
- " # function [T,q,p] = rcemip_on_z(z,SST)\n",
- " # \n",
- " # Inputs:\n",
- " # z: array of heights (low to high, m)\n",
- " # SST: sea surface temperature (K)\n",
- " # \n",
- " # Outputs:\n",
- " T = np.zeros(len(z)) # temperature (K)\n",
- " q = np.zeros(len(z)) # specific humidity (g/g)\n",
- " p = np.zeros(len(z)) # pressure (Pa)\n",
- "\n",
- " ## Constants\n",
- " g = 9.79764 #m/s^2\n",
- " Rd = 287.04 #J/kgK\n",
- " \n",
- " ## Parameters\n",
- " p0 = 101480 #Pa surface pressure\n",
- " qt = 10**(-11) #g/g specific humidity at tropopause\n",
- " zq1 = 4000 #m\n",
- " zq2 = 7500 #m\n",
- " zt = 15000 #m tropopause height\n",
- " gamma = 0.0067 #K/m lapse rate\n",
- " \n",
- " ## Scratch\n",
- " Tv = np.zeros(len(z)) # temperature (K)\n",
- "\n",
- " if SST == 295:\n",
- " q0 = 0.01200; #g/g specific humidity at surface (adjusted from 300K value so RH near surface approx 80%)\n",
- " elif SST == 300:\n",
- " q0 = 0.01865; #g/g specific humidity at surface\n",
- " elif SST == 305:\n",
- " q0 = 0.02400 #g/g specific humidity at surface (adjusted from 300K value so RH near surface approx 80%)\n",
- " \n",
- " T0 = SST - 0 #surface air temperature adjusted to be 0K less than SST\n",
- " \n",
- " ## Virtual Temperature at surface and tropopause\n",
- " Tv0 = T0*(1 + 0.608*q0) #virtual temperature at surface\n",
- " Tvt = Tv0 - gamma*zt #virtual temperature at tropopause z=zt\n",
- " \n",
- " ## Pressure\n",
- " pt = p0*(Tvt/Tv0)**(g/(Rd*gamma)); #pressure at tropopause z=zt\n",
- " p = p0*((Tv0-gamma*z)/Tv0)**(g/(Rd*gamma)) #0 <= z <= zt\n",
- " p[z>zt] = pt*np.exp(-g*(z[z>zt]-zt)/(Rd*Tvt)) #z > zt\n",
- " \n",
- " ## Specific humidity\n",
- " q = q0*np.exp(-z/zq1)*np.exp(-(z/zq2)**2)\n",
- " q[z>zt] = qt #z > zt\n",
- " \n",
- " ## Temperature\n",
- " #Virtual Temperature\n",
- " Tv = Tv0 - gamma*z #0 <= z <= zt\n",
- " Tv[z>zt] = Tvt #z > zt\n",
- " \n",
- " #Absolute Temperature at all heights\n",
- " T = Tv/(1 + 0.608*q)\n",
- " \n",
- " return T, q, p\n",
- "\n",
- "z = np.arange(0,17000,100)\n",
- "T, q , p = rcemip_on_z(z,300)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 288x360 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "def get_rh (T,q,p,formula='wagner-pruss',state='liq'):\n",
- " es_w = es(T,formula=formula,state=state)\n",
- " x = es_w * eps1/(p-es_w)\n",
- " return 100.*q*(1+x)/x\n",
- " \n",
- "fig = plt.figure(figsize=(4,5))\n",
- "\n",
- "ax1 = plt.subplot(1,1,1)\n",
- "ax1.set_ylabel('$z$ / km')\n",
- "ax1.set_xlabel('RH / %')\n",
- "ax1.set_ylim(0,14.5)\n",
- "ax1.set_yticks([0,4,8,12])\n",
- "\n",
- "plt.plot(get_rh(T,q,p,state='mxd'),z/1000.,label = 'Wagner Pruss (ice/liq)')\n",
- "plt.plot(get_rh(T+1,q,p,state='mxd'),z/1000.,label = 'Wagner Pruss (ice/liq) + 1 K')\n",
- "plt.plot(get_rh(T,q,p,state='ice'),z/1000.,label = 'Wagner Pruss (ice)')\n",
- "plt.plot(get_rh(T,q,p,formula='romps',state='mxd'),z/1000.,label = 'Romps (ice/liq)')\n",
- "plt.plot(get_rh(T,q,p),z/1000.,label = 'Wagner Pruss (liq)')\n",
- "plt.plot(get_rh(T,q,p,formula='flatau'),z/1000.,label = 'Flatau (liq)')\n",
- "\n",
- "plt.legend(loc=\"lower left\",ncol=1)\n",
- "\n",
- "sns.set_context(\"paper\")\n",
- "sns.despine(offset=10)\n",
- "plt.tight_layout()\n",
- "\n",
- "fig.savefig(plot_dir+'RCEMIP-RHerror.pdf')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## lifting-condensation level approximations\n",
- "\n",
- "Here we compare the LCL base predictions to those proposed by Romps and Bolton as well as the differences between density potential temperatures.\n",
- "\n",
- "For the estimation of the LCL we modify the Romps expressions (using his code) to output pressure at the LCL, as this eliminates an assumption as to how pressure is distributed in the atmosphere, and thus only depends on the parcel state. What we find is that the much simpler Bolton expression is as good as the more complex expression by Romps, and differences between the two are commensurate with those arising from slight differences in how the saturation vapor pressure is calculated."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Version 1.0 released by David Romps on September 12, 2017.\n",
- "# \n",
- "# When using this code, please cite:\n",
- "# \n",
- "# @article{16lcl,\n",
- "# Title = {Exact expression for the lifting condensation level},\n",
- "# Author = {David M. Romps},\n",
- "# Journal = {Journal of the Atmospheric Sciences},\n",
- "# Year = {2017},\n",
- "# Volume = {in press},\n",
- "# }\n",
- "#\n",
- "# This lcl function returns the height of the lifting condensation level\n",
- "# (LCL) in meters. The inputs are:\n",
- "# - p in Pascals\n",
- "# - T in Kelvins\n",
- "# - Exactly one of rh, rhl, and rhs (dimensionless, from 0 to 1):\n",
- "# * The value of rh is interpreted to be the relative humidity with\n",
- "# respect to liquid water if T >= 273.15 K and with respect to ice if\n",
- "# T < 273.15 K. \n",
- "# * The value of rhl is interpreted to be the relative humidity with\n",
- "# respect to liquid water\n",
- "# * The value of rhs is interpreted to be the relative humidity with\n",
- "# respect to ice\n",
- "# - ldl is an optional logical flag. If true, the lifting deposition\n",
- "# level (LDL) is returned instead of the LCL. \n",
- "# - min_lcl_ldl is an optional logical flag. If true, the minimum of the\n",
- "# LCL and LDL is returned.\n",
- "\n",
- "def lcl(p,T,rh=None,rhl=None,rhs=None,return_ldl=False,return_min_lcl_ldl=False):\n",
- "\n",
- " import math\n",
- " import scipy.special\n",
- "\n",
- " # Parameters\n",
- " Ttrip = 273.16 # K\n",
- " ptrip = 611.65 # Pa\n",
- " E0v = 2.3740e6 # J/kg\n",
- " E0s = 0.3337e6 # J/kg\n",
- " ggr = 9.81 # m/s^2\n",
- " rgasa = 287.04 # J/kg/K \n",
- " rgasv = 461 # J/kg/K \n",
- " cva = 719 # J/kg/K\n",
- " cvv = 1418 # J/kg/K \n",
- " cvl = 4119 # J/kg/K \n",
- " cvs = 1861 # J/kg/K \n",
- " cpa = cva + rgasa\n",
- " cpv = cvv + rgasv\n",
- "\n",
- " # The saturation vapor pressure over liquid water\n",
- " def pvstarl(T):\n",
- " return ptrip * (T/Ttrip)**((cpv-cvl)/rgasv) * math.exp( (E0v - (cvv-cvl)*Ttrip) / rgasv * (1/Ttrip - 1/T) )\n",
- " # The saturation vapor pressure over solid ice\n",
- " def pvstars(T):\n",
- " return ptrip * (T/Ttrip)**((cpv-cvs)/rgasv) * math.exp( (E0v + E0s - (cvv-cvs)*Ttrip) / rgasv * (1/Ttrip - 1/T)) \n",
- "\n",
- " # Calculate pv from rh, rhl, or rhs\n",
- " rh_counter = 0\n",
- " if rh is not None:\n",
- " rh_counter = rh_counter + 1\n",
- " if rhl is not None:\n",
- " rh_counter = rh_counter + 1\n",
- " if rhs is not None:\n",
- " rh_counter = rh_counter + 1\n",
- " if rh_counter != 1:\n",
- " print(rh_counter)\n",
- " exit('Error in lcl: Exactly one of rh, rhl, and rhs must be specified')\n",
- " if rh is not None:\n",
- " # The variable rh is assumed to be \n",
- " # with respect to liquid if T > Ttrip and \n",
- " # with respect to solid if T < Ttrip\n",
- " if T > Ttrip:\n",
- " pv = rh * pvstarl(T)\n",
- " else:\n",
- " pv = rh * pvstars(T)\n",
- " rhl = pv / pvstarl(T)\n",
- " rhs = pv / pvstars(T)\n",
- " elif rhl is not None:\n",
- " pv = rhl * pvstarl(T)\n",
- " rhs = pv / pvstars(T)\n",
- " if T > Ttrip:\n",
- " rh = rhl\n",
- " else:\n",
- " rh = rhs\n",
- " elif rhs is not None:\n",
- " pv = rhs * pvstars(T)\n",
- " rhl = pv / pvstarl(T)\n",
- " if T > Ttrip:\n",
- " rh = rhl\n",
- " else:\n",
- " rh = rhs\n",
- " if pv > p:\n",
- " return N\n",
- "\n",
- "# Calculate lcl_liquid and lcl_solid\n",
- " qv = rgasa*pv / (rgasv*p + (rgasa-rgasv)*pv)\n",
- " rgasm = (1-qv)*rgasa + qv*rgasv\n",
- " cpm = (1-qv)*cpa + qv*cpv\n",
- " if rh == 0:\n",
- " return cpm*T/ggr\n",
- " aL = -(cpv-cvl)/rgasv + cpm/rgasm\n",
- " bL = -(E0v-(cvv-cvl)*Ttrip)/(rgasv*T)\n",
- " cL = pv/pvstarl(T)*math.exp(-(E0v-(cvv-cvl)*Ttrip)/(rgasv*T))\n",
- " aS = -(cpv-cvs)/rgasv + cpm/rgasm\n",
- " bS = -(E0v+E0s-(cvv-cvs)*Ttrip)/(rgasv*T)\n",
- " cS = pv/pvstars(T)*math.exp(-(E0v+E0s-(cvv-cvs)*Ttrip)/(rgasv*T))\n",
- " X = bL/(aL*scipy.special.lambertw(bL/aL*cL**(1/aL),-1).real)\n",
- " Y = bS/(aS*scipy.special.lambertw(bS/aS*cS**(1/aS),-1).real) \n",
- " \n",
- " lcl = cpm*T/ggr*( 1 - X)\n",
- " ldl = cpm*T/ggr*( 1 - Y)\n",
- "\n",
- " # Modifications of the code to output Plcl or Pldl\n",
- " Plcl = PPa * X**(cpm/rgasm)\n",
- " Pldl = PPa * X**(cpm/rgasm)\n",
- " # Return either lcl or ldl\n",
- " if return_ldl and return_min_lcl_ldl:\n",
- " exit('return_ldl and return_min_lcl_ldl cannot both be true')\n",
- " elif return_ldl:\n",
- " return Pldl\n",
- " elif return_min_lcl_ldl:\n",
- " return min(Plcl,Pldl)\n",
- " else:\n",
- " return Plcl"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "ename": "AttributeError",
- "evalue": "module 'aes_thermo' has no attribute 'get_Plcl'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m qt \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m2.5e-3\u001b[39m,\u001b[38;5;241m8e-3\u001b[39m,\u001b[38;5;241m0.2e-3\u001b[39m)\n\u001b[1;32m 5\u001b[0m TK \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m285.\u001b[39m\n\u001b[0;32m----> 6\u001b[0m Plcl_X \u001b[38;5;241m=\u001b[39m \u001b[43mmt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_Plcl\u001b[49m(TK,PPa,qt,iterate\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 7\u001b[0m Plcl_B \u001b[38;5;241m=\u001b[39m mt\u001b[38;5;241m.\u001b[39mget_Plcl(TK,PPa,qt)\n\u001b[1;32m 8\u001b[0m Plcl_R \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;28mlen\u001b[39m(Plcl_X))\n",
- "\u001b[0;31mAttributeError\u001b[0m: module 'aes_thermo' has no attribute 'get_Plcl'"
- ]
- }
- ],
- "source": [
- "import aes_thermo as mt\n",
- "PPa = 101325.\n",
- "\n",
- "qt = np.arange(2.5e-3,8e-3,0.2e-3)\n",
- "TK = 285.\n",
- "Plcl_X = mt.plcl(TK,PPa,qt)\n",
- "Plcl_B = mt.plcl_boloton(TK,PPa,qt)\n",
- "Plcl_R = np.zeros(len(Plcl_X))\n",
- "\n",
- "for i,x in enumerate(qt):\n",
- " if (x>0.1): x = x/1000.\n",
- " RH = mt.mr2pp(x/(1.-x),PPa)/mt.es(TK)\n",
- " Plcl_R[i] = lcl(PPa,TK,RH)\n",
- "\n",
- "del1 = (Plcl_B-Plcl_X)/100.\n",
- "del2 = (Plcl_R-Plcl_X)/100.\n",
- "\n",
- "fig = plt.figure(figsize=(10,5))\n",
- "ax1 = plt.subplot(1,2,1)\n",
- "ax1.set_ylabel('$P$ / hPa')\n",
- "ax1.set_xlabel('$q_\\mathrm{t}$ / g/kg')\n",
- "#ax1.set_ylim(-1.2,1.2)\n",
- "\n",
- "plt.plot(qt*1.e3,del1,label='$\\\\delta_\\mathrm{B}$, $T$=285K')\n",
- "plt.plot(qt*1.e3,del2,label='$\\\\delta_\\mathrm{R}$, $T$=285K')\n",
- "#plt.gca().invert_yaxis()\n",
- "plt.legend(loc=\"best\")\n",
- "\n",
- "qt = np.arange(0.5e-3,28e-3,0.2e-3)\n",
- "TK = 310.\n",
- "Plcl_X = mt.get_Plcl(TK,PPa,qt,iterate=True)\n",
- "Plcl_B = mt.get_Plcl(TK,PPa,qt)\n",
- "Plcl_R = np.zeros(len(Plcl_X))\n",
- "\n",
- "for i,x in enumerate(qt):\n",
- " if (x>0.1): x = x/1000.\n",
- " RH = mt.mr2pp(x/(1.-x),PPa)/mt.es(TK)\n",
- " Plcl_R[i] = lcl(PPa,TK,RH)\n",
- "\n",
- "del1 = (Plcl_B-Plcl_X)/100.\n",
- "del2 = (Plcl_R-Plcl_X)/100.\n",
- "\n",
- "ax2 = plt.subplot(1,2,2)\n",
- "ax2.set_xlabel('$q_\\mathrm{t}$ / g/kg')\n",
- "#ax2.set_ylim(-1.2,1.2)\n",
- "ax2.set_yticklabels([])\n",
- "\n",
- "plt.plot(qt*1.e3,del1,label='$\\\\delta_\\mathrm{B}$, $T$=310K')\n",
- "plt.plot(qt*1.e3,del2,label='$\\\\delta_\\mathrm{R}$, $T$=310K')\n",
- "#plt.gca().invert_yaxis()\n",
- "plt.legend(loc=\"best\")\n",
- "\n",
- "sns.set_context(\"talk\", font_scale=1.2)\n",
- "sns.despine(offset=10)\n",
- "fig.savefig(plot_dir+'Plcl.pdf')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Acknowledgments ##\n",
- "\n",
- "Jiawei Bao, Geet George, and Hauke Schulz are thanked for comments on these notes, and the identification of some errors in earlier versions. Axel Seifert is thanked for his comments and insights, and for pointing out the TEOS routines of Feisel et al. (2010) which extend the IAPWS libraries."
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.13"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/setup.py b/setup.py
index 845c25c..f37c2d6 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
setup(
name="moist_thermodynamics",
- version="0.2",
+ version="0.3",
description="Constants and functions for the treatment of moist atmospheric thermodynamics",
packages=find_packages(),
)
--
GitLab