From 2927145aa8d7a2793ab96ec99fb342aa2c85d3df Mon Sep 17 00:00:00 2001
From: bjorn-stevens <64255981+bjorn-stevens@users.noreply.github.com>
Date: Mon, 15 Aug 2022 22:58:48 +0200
Subject: [PATCH] restructure treatment of saturation vapor pressure

In this branch additional impelmentations of the saturation vapor
pressure were impelmented.  These different treatments are not meant to
be exhaustive, but the teten's murray formulations were introduced
because these are used by ICON.  The murphy-koop formulation is the
standard for super-cooled liquid water.  The examples ipynb script was
augmented to present the new functionality.
---
 examples/examples.ipynb                       | 118 ++++++---
 moist_thermodynamics/functions.py             | 241 ++----------------
 .../saturation_vapor_pressures.py             | 213 ++++++++++++++++
 3 files changed, 317 insertions(+), 255 deletions(-)
 create mode 100644 moist_thermodynamics/saturation_vapor_pressures.py

diff --git a/examples/examples.ipynb b/examples/examples.ipynb
index f1ad1e9..d36be7e 100644
--- a/examples/examples.ipynb
+++ b/examples/examples.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "b7c5c488-b68c-4504-85a0-bbe1575c7f65",
    "metadata": {},
    "outputs": [],
@@ -13,6 +13,7 @@
     "\n",
     "from moist_thermodynamics import functions as mt\n",
     "from moist_thermodynamics import constants\n",
+    "from moist_thermodynamics import saturation_vapor_pressures as svp\n",
     "\n",
     "i4T = np.vectorize(mt.invert_for_temperature)"
    ]
@@ -26,14 +27,17 @@
     "\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 to saturation vapor pressure \n",
-    "3. lcl computations\n",
-    "4. Integrating the first law to arrive at the moist adiabat\n",
+    "1. saturation vapor pressure\n",
+    "2. constructing a moist adiabat.\n",
+    "3. sensitivity of moist adiabat to saturation vapor pressure \n",
+    "4. lcl computations\n",
+    "5. Integrating the first law to arrive at the moist adiabat\n",
     "\n",
-    "## 1. Constructing a moist adiabat\n",
+    "## 1. saturation vapor pressure \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 $\\theta_\\mathrm{s}$"
+    "We compare the error of the much simpler Teten's formulae to those of the reference formulae for the three cases of liquid, super-cooled liquid and ice.  The reference fits for these are respectively Wagner and Pruss, Koop and Murray, and Wagner et al.  For liquid the fits are quite good, to within better than 0.15%.  For ice and super-cooled water they simple formulae are less accurate, with errors of a few percent at 230 K and larger for colder temperatures.\n",
+    "\n",
+    "In the second plot we compare the extrapolation of the Wanger and Pruss formula, which was derived for temperatures between the triple and critical points, for saturation with respect to super cooled liquid using Murphy and Koop as the super-cooled reference.   Murphy and Koop is fit for temperatures (123K-332K) spanning conditions of earth's atmosphere. The comparison is for extreme temperatures.  Generally extrapolating Wagner and Pruss for super cooled water is a better fit from the Murray specification of the Teten's formual."
    ]
   },
   {
@@ -44,22 +48,74 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "array([611.65715494, 222.65143353])"
+       "<Figure size 648x288 with 2 Axes>"
       ]
      },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "mt.es_liq_hardy(np.asarray([273.16, 260.0]))"
+    "Tw = np.arange(273.15, 330.0)\n",
+    "es = svp.liq_tetens_murray(Tw)\n",
+    "es_def = svp.liq_wagner_pruss(Tw)\n",
+    "err1 = (es / es_def - 1.0) * 100.0\n",
+    "\n",
+    "Tc = np.arange(230.0, 273.15)\n",
+    "es = svp.liq_tetens_murray(Tc)\n",
+    "es_def = svp.liq_murphy_koop(Tc)\n",
+    "err2 = (es / es_def - 1.0) * 100.0\n",
+    "\n",
+    "es = svp.ice_tetens_murray(Tc)\n",
+    "es_def = svp.ice_wagner_etal(Tc)\n",
+    "err3 = (es / es_def - 1.0) * 100.0\n",
+    "\n",
+    "sns.set_context(\"talk\")\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(9, 4), constrained_layout=True)\n",
+    "\n",
+    "ax[0].plot(Tw, err1, label=f\"liquid\", c=\"dodgerblue\")\n",
+    "ax[0].plot(Tc, err2, label=f\"super cooled\", ls=\"dotted\", c=\"dodgerblue\")\n",
+    "ax[0].plot(Tc, err3, label=f\"ice\", c=\"violet\")\n",
+    "\n",
+    "Tw = np.arange(273.16, 430)\n",
+    "es = svp.liq_wagner_pruss(Tw)\n",
+    "es_def = svp.liq_murphy_koop(Tw)\n",
+    "err1 = (es / es_def - 1.0) * 100.0\n",
+    "ax[1].plot(Tw, err1, label=f\"liquid\", c=\"dodgerblue\")\n",
+    "Tw = np.arange(150, 273.16)\n",
+    "es_def = svp.liq_wagner_pruss(Tw)\n",
+    "es = svp.liq_murphy_koop(Tw)\n",
+    "err1 = (es / es_def - 1.0) * 100.0\n",
+    "ax[1].plot(Tw, err1, label=f\"super-cooled\", c=\"dodgerblue\", ls=\"dotted\")\n",
+    "\n",
+    "ax[0].hlines(0, 230, 330.0, ls=\"dashed\", color=\"grey\")\n",
+    "ax[0].legend()\n",
+    "ax[1].legend()\n",
+    "ax[1].set_xlabel(\"$T$ / K\")\n",
+    "ax[0].set_xlabel(\"$T$ / K\")\n",
+    "ax[0].set_ylabel(\"Error / %\")\n",
+    "ax[1].set_ylabel(\"Error / %\")\n",
+    "\n",
+    "sns.despine(offset=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "292f88d8-2b95-4a3f-9f20-9cb09ceeada3",
+   "metadata": {},
+   "source": [
+    "## 2. 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": 6,
+   "execution_count": 3,
    "id": "0f765565-ed26-4cc7-a859-bebf9b020aea",
    "metadata": {},
    "outputs": [
@@ -84,7 +140,7 @@
     }
    ],
    "source": [
-    "es = mt.es_liq_analytic\n",
+    "es = svp.liq_analytic\n",
     "p2q = mt.partial_pressure_to_specific_humidity\n",
     "theta_l = mt.theta_l\n",
     "i4T = np.vectorize(mt.invert_for_temperature)\n",
@@ -124,20 +180,20 @@
    "id": "b2f6c280-e7b0-48ac-acc5-15053cabe4d0",
    "metadata": {},
    "source": [
-    "## 2. Sensitivity (small) of moist adiabat on saturation vapor pressure \n",
+    "## 3. 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": 8,
+   "execution_count": 4,
    "id": "321bddff-0bb6-4b3a-a3f0-1dae1c50c852",
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 648x504 with 2 Axes>"
       ]
@@ -162,21 +218,21 @@
     "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",
+    "es = svp.liq_analytic\n",
     "TKl = np.maximum(i4T(theta_l, theta_l(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "TKe = np.maximum(i4T(theta_e, theta_e(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "TKs = np.maximum(i4T(theta_s, theta_s(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "ax[1].plot(TKe - TKl, P / 100.0, label=f\"$\\\\theta_e-\\\\theta_l$\")\n",
     "ax[1].plot(TKs - TKl, P / 100.0, label=f\"$\\\\theta_s-\\\\theta_l$\")\n",
-    "ax[1].set_title(\"es_liq_analytic\")\n",
+    "ax[1].set_title(\"liq_analytic\")\n",
     "\n",
-    "es = mt.es_liq\n",
+    "es = svp.liq_wagner_pruss\n",
     "TKl = np.maximum(i4T(theta_l, theta_l(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "TKe = np.maximum(i4T(theta_e, theta_e(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "TKs = np.maximum(i4T(theta_s, theta_s(Tsfc, Psfc, qt, es=es), P, qt, es=es), Tmin)\n",
     "ax[0].plot(TKe - TKl, P / 100.0, label=f\"$\\\\theta_e-\\\\theta_l$\")\n",
     "ax[0].plot(TKs - TKl, P / 100.0, label=f\"$\\\\theta_s-\\\\theta_l$\")\n",
-    "ax[0].set_title(\"es_liq\")\n",
+    "ax[0].set_title(\"liq\")\n",
     "\n",
     "plt.gca().invert_yaxis()\n",
     "\n",
@@ -194,14 +250,14 @@
    "id": "b2fd8753-736f-459c-a435-0c50ad8eeae9",
    "metadata": {},
    "source": [
-    "## 3. Calculations of lifting condensation level\n",
+    "## 4. 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": 9,
+   "execution_count": 5,
    "id": "a53539ae-7920-41b9-aa41-fed0031ce16b",
    "metadata": {},
    "outputs": [],
@@ -344,13 +400,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "9b2830db-855d-467d-ac66-cc9154ab7caa",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 504x360 with 2 Axes>"
       ]
@@ -370,7 +426,7 @@
     "Plcl_R = np.zeros(len(qt))\n",
     "\n",
     "for i, x in enumerate(qt):\n",
-    "    RH = mt.mixing_ratio_to_partial_pressure(x / (1.0 - x), PPa) / mt.es_liq(TK)\n",
+    "    RH = mt.mixing_ratio_to_partial_pressure(x / (1.0 - x), PPa) / svp.liq_analytic(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",
@@ -394,7 +450,9 @@
     "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 / (1.0 - x), PPa) / mt.es_liq(TK)\n",
+    "    RH = mt.mixing_ratio_to_partial_pressure(x / (1.0 - x), PPa) / svp.liq_wagner_pruss(\n",
+    "        TK\n",
+    "    )\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",
@@ -417,14 +475,14 @@
    "id": "cb6f2331-c0f2-471b-bffe-b21097f6ff49",
    "metadata": {},
    "source": [
-    "## 4. Integrating the first law to arrive at the moist adiabat\n",
+    "## 5. Integrating the first law to arrive at the moist adiabat\n",
     "\n",
     "This example shows how to construct a moist adiabat allowing for equilibrium freezing.  To do so it makes use of two calls of the moist_adiabat function.  The first calculates the moist adiabat assuming condensation only produces ice, the other only liquid, with the latter being valid for temperatures above T0, the former for temperatures below T0, and an isothermal T0 layer residing in between.  The result is plotted in terms of the dry potential temperature to better highlight the enhanced stability."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "4a05ea68-9c61-449b-9945-d8f87fbb057a",
    "metadata": {
     "tags": []
@@ -493,7 +551,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1417891f-4fe5-4069-99e1-ca43c26af90b",
+   "id": "0ffbde02-a695-45ca-8aa2-4c401651a913",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/moist_thermodynamics/functions.py b/moist_thermodynamics/functions.py
index 3c897f3..fec3736 100644
--- a/moist_thermodynamics/functions.py
+++ b/moist_thermodynamics/functions.py
@@ -8,101 +8,17 @@ copygright, bjorn stevens Max Planck Institute for Meteorology, Hamburg
 License: BSD-3C
 """
 #
-from . import constants
 import numpy as np
 from scipy import interpolate, optimize
 
+from . import constants
+from . import saturation_vapor_pressures
 
-def planck(T, nu):
-    """Planck source function (J/m2 per steradian per Hz)
-
-    Args:
-        T: temperature in kelvin
-        nu: frequency in Hz
-
-    Returns:
-        Returns the radiance in the differential frequency interval per unit steradian. Usually we
-        multiply by $\pi$ to convert to irradiances
-
-    >>> planck(300,1000*constants.c)
-    8.086837160291128e-15
-    """
-    c = constants.speed_of_light
-    h = constants.planck_constant
-    kB = constants.boltzmann_constant
-    return (2 * h * nu**3 / c**2) / (np.exp(h * nu / (kB * T)) - 1)
-
-
-def es_liq(T):
-    """Returns saturation vapor pressure (Pa) over planer liquid water
-
-    Encodes the empirical fits of Wagner and Pruss (2002), Eq 2.5a (page 399). Their formulation
-    is compared to other fits in the example scripts used in this package, and deemed to be the
-    best reference.
-
-    Args:
-        T: temperature in kelvin
-
-    Reference:
-        W. Wagner and A. Pruß , "The IAPWS Formulation 1995 for the Thermodynamic Properties
-    of Ordinary Water Substance for General and Scientific Use", Journal of Physical and Chemical
-    Reference Data 31, 387-535 (2002) https://doi.org/10.1063/1.1461829
-
-    >>> es_liq(np.asarray([273.16,305.]))
-    array([ 611.65706974, 4719.32683147])
-    """
-    TvC = constants.temperature_water_vapor_critical_point
-    PvC = constants.pressure_water_vapor_critical_point
-
-    vt = 1.0 - T / TvC
-    es = PvC * np.exp(
-        TvC
-        / T
-        * (
-            -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
-        )
-    )
-    return es
-
-
-def es_ice(T):
-    """Returns sublimation vapor pressure (Pa) over simple (Ih) ice
-
-    Encodes the emperical fits of Wagner et al., (2011) which also define the IAPWS standard for
-    sublimation vapor pressure over ice-Ih
-
-    Args:
-        T: temperature in kelvin
-
-    Reference:
-        Wagner, W., Riethmann, T., Feistel, R. & Harvey, A. H. New Equations for the Sublimation
-        Pressure and Melting Pressure of H 2 O Ice Ih. Journal of Physical and Chemical Reference
-        Data 40, 043103 (2011).
-
-
-    >>> es_ice(np.asarray([273.16,260.]))
-    array([611.655     , 195.80103377])
-    """
-    TvT = constants.temperature_water_vapor_triple_point
-    PvT = constants.pressure_water_vapor_triple_point
-
-    a1 = -0.212144006e2
-    a2 = 0.273203819e2
-    a3 = -0.610598130e1
-    b1 = 0.333333333e-2
-    b2 = 0.120666667e1
-    b3 = 0.170333333e1
-    theta = T / TvT
-    es = PvT * np.exp((a1 * theta**b1 + a2 * theta**b2 + a3 * theta**b3) / theta)
-    return es
+es_liq = saturation_vapor_pressures.liq_wagner_pruss
+es_ice = saturation_vapor_pressures.ice_wagner_etal
 
 
-def es_mxd(T):
+def es_mxd(T, es_liq=es_liq, es_ice=es_ice):
     """Returns the minimum of the sublimation and saturation vapor pressure
 
     Calculates both the sublimation vapor pressure over ice Ih using es_ice and that over planar
@@ -120,149 +36,24 @@ def es_mxd(T):
     return np.minimum(es_liq(T), es_ice(T))
 
 
-def es_liq_murphykoop(T):
-    """Returns saturation vapor pressure (Pa) over liquid water
-
-    Encodes the empirical fit (Eq. 10) of Murphy and Koop (2011) which improves on the Wagner and
-    Pruß fits for supercooled conditions.
-
-    Args:
-        T: temperature in kelvin
-
-    Reference:
-        Murphy, D. M. & Koop, T. Review of the vapour pressures of ice and supercooled water for
-        atmospheric applications. Q. J. R. Meteorol. Soc. 131, 1539–1565 (2005).
-
-    >>> es_liq_murphykoop(np.asarray([273.16,140.]))
-    array([6.11657044e+02, 9.39696372e-07])
-    """
-
-    X = np.tanh(0.0415 * (T - 218.8)) * (
-        53.878 - 1331.22 / T - 9.44523 * np.log(T) + 0.014025 * T
-    )
-    return np.exp(54.842763 - 6763.22 / T - 4.210 * np.log(T) + 0.000367 * T + X)
-
-
-def es_liq_hardy(T):
-    """Returns satruation vapor pressure (Pa) over liquid water
-
-    Encodes the empirical fit (Eq. 10) of Hardy (1998) which is often used in the postprocessing
-    of radiosondes
-
-    Args:
-        T: temperature in kelvin
-
-    Reference:
-        Hardy, B., 1998, ITS-90 Formulations for Vapor Pressure, Frostpoint Temperature, Dewpoint
-        Temperature, and Enhancement Factors in the Range –100 to +100 °C, The Proceedings of the
-        Third International Symposium on Humidity & Moisture, London, England
-
-    >>> es_liq_hardy(np.asarray([273.16,260.]))
-    array([611.65715494, 222.65143353])
-    """
-    X = (
-        -2.8365744e3 / (T * T)
-        - 6.028076559e3 / T
-        + 19.54263612
-        - 2.737830188e-2 * T
-        + 1.6261698e-5 * T**2
-        + 7.0229056e-10 * T**3
-        - 1.8680009e-13 * T**4
-        + 2.7150305 * np.log(T)
-    )
-    return np.exp(X)
-
-
-def es_liq_analytic(T, delta_cl=constants.delta_cl):
-    """Analytic approximation for saturation vapor pressure over iquid
-
-    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
-    calculate the saturation vapor pressure over liquid.  The procedure is described in Eq(4) of
-    Romps (2017) and best approximates the actual value for specific heats that differ slightly
-    from the best estimates of these quantities which are provided as default quantities.
-    Romps recommends cl = 4119 J/kg/K, and cpv = 1861 J/kg/K.
-
-    Args:
-        T: temperature in kelvin
-        delta_cl: differnce between isobaric specific heat capacity of vapor and that of liquid.
-
-    Returns:
-        value of saturation vapor pressure over liquid water in Pa
-
-    Reference:
-        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
-        Sciences 74, 3891–3900 (2017).
-        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
-        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
-
-    >>> es_liq_analytic(np.asarray([273.16,305.]))
-    array([ 611.655     , 4711.13161169])
-    """
-    TvT = constants.temperature_water_vapor_triple_point
-    PvT = constants.pressure_water_vapor_triple_point
-    lvT = constants.vaporization_enthalpy_triple_point
-    Rv = constants.water_vapor_gas_constant
-
-    c1 = delta_cl / Rv
-    c2 = lvT / (Rv * TvT) - c1
-    es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1
-    return es
-
-
-def es_ice_analytic(T, delta_ci=constants.delta_ci):
-    """Analytic approximation for saturation vapor pressure over ice
-
-    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
-    calculate the saturation vapor pressure over ice.  The procedure is described in Eq(4) of
-    Romps (2017) and best approximates the actual value for specific heats that differ slightly
-    from the best estimates of these quantities which are provided as default quantities.
-    Romps recommends ci = 1861 J/kg/K, and cpv = 1879 J/kg/K.
-
-    Args:
-        T: temperature in kelvin
-        delta_cl: differnce between isobaric specific heat capacity of vapor and that of liquid.
-
-    Returns:
-        value of saturation vapor pressure over liquid water in Pa
-
-    Reference:
-        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
-        Sciences 74, 3891–3900 (2017).
-        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
-        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
-
-
-    >>> es_ice_analytic(np.asarray([273.16,260.]))
-    array([611.655     , 195.99959431])
-    """
-    TvT = constants.temperature_water_vapor_triple_point
-    PvT = constants.pressure_water_vapor_triple_point
-    lsT = constants.sublimation_enthalpy_triple_point
-    Rv = constants.water_vapor_gas_constant
-
-    c1 = delta_ci / Rv
-    c2 = lsT / (Rv * TvT) - c1
-    es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1
-    return es
-
-
-def es_mxd_analytic(T, delta_cl=constants.delta_cl, delta_ci=constants.delta_ci):
-    """Returns the minimum of the analytic sublimation and saturation vapor pressure
-
-    Calculates both the sublimation vapor pressure over ice Ih using es_ice_analytic and
-    that over planar water using es_liq_analytic, and returns the minimum of the two
-    quantities.
+def planck(T, nu):
+    """Planck source function (J/m2 per steradian per Hz)
 
     Args:
         T: temperature in kelvin
+        nu: frequency in Hz
 
     Returns:
-        value of es_ice_analytic(T) for T < 273.15 and es_liq_analytic(T) otherwise
+        Returns the radiance in the differential frequency interval per unit steradian. Usually we
+        multiply by $\pi$ to convert to irradiances
 
-    >>> es_ice_analytic(np.asarray([273.16,260.]))
-    array([611.655     , 195.99959431])
+    >>> planck(300,1000*constants.c)
+    8.086837160291128e-15
     """
-    return np.minimum(es_liq_analytic(T, delta_cl), es_ice_analytic(T, delta_ci))
+    c = constants.speed_of_light
+    h = constants.planck_constant
+    kB = constants.boltzmann_constant
+    return (2 * h * nu**3 / c**2) / (np.exp(h * nu / (kB * T)) - 1)
 
 
 def vaporization_enthalpy(TK, delta_cl=constants.delta_cl):
diff --git a/moist_thermodynamics/saturation_vapor_pressures.py b/moist_thermodynamics/saturation_vapor_pressures.py
new file mode 100644
index 0000000..fcecb6e
--- /dev/null
+++ b/moist_thermodynamics/saturation_vapor_pressures.py
@@ -0,0 +1,213 @@
+# -*- coding: utf-8 -*-
+"""
+Provides a collection of fits for saturation and sublimation vapor pressure
+
+Author: Bjorn Stevens (bjorn.stevens@mpimet.mpg.de)
+copygright, bjorn stevens Max Planck Institute for Meteorology, Hamburg
+
+License: BSD-3C
+"""
+#
+from . import constants
+import numpy as np
+
+def liq_wagner_pruss(T):
+    """Returns saturation vapor pressure (Pa) over planer liquid water
+
+    Encodes the empirical fits of Wagner and Pruss (2002), Eq 2.5a (page 399). Their formulation
+    is compared to other fits in the example scripts used in this package, and deemed to be the
+    best reference.
+    
+    The fit has been verified for TvT <= T < = TvC.  For super cooled water (T<TvT) it deviates
+    from the results of Murphy and Koop where were developed for super-cooled water.  It is about
+    10% larger at 200 K, 25 % larter at 150 K, and then decreases again so it is 12% smaller at
+    the limit (123K) of the Murphy and Koop fit.  For accurate fits for super-cooled water the
+    function of Murphy and Koop should be used.
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        W. Wagner and A. Pruß , "The IAPWS Formulation 1995 for the Thermodynamic Properties
+    of Ordinary Water Substance for General and Scientific Use", Journal of Physical and Chemical
+    Reference Data 31, 387-535 (2002) https://doi.org/10.1063/1.1461829
+
+    >>> liq_wagner_pruss(np.asarray([273.16,305.]))
+    array([ 611.65706974, 4719.32683147])
+    """
+    TvC = constants.temperature_water_vapor_critical_point
+    PvC = constants.pressure_water_vapor_critical_point
+
+    vt = 1.0 - T / TvC
+    es = PvC * np.exp(
+        TvC
+        / T
+        * (
+            -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
+        )
+    )
+    return es
+
+
+def ice_wagner_etal(T):
+    """Returns sublimation vapor pressure (Pa) over simple (Ih) ice
+
+    Encodes the emperical fits of Wagner et al., (2011) which also define the IAPWS standard for
+    sublimation vapor pressure over ice-Ih
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Wagner, W., Riethmann, T., Feistel, R. & Harvey, A. H. New Equations for the Sublimation
+        Pressure and Melting Pressure of H 2 O Ice Ih. Journal of Physical and Chemical Reference
+        Data 40, 043103 (2011).
+
+
+    >>> ice_wagner_etal(np.asarray([273.16,260.]))
+    array([611.655     , 195.80103377])
+    """
+    TvT = constants.temperature_water_vapor_triple_point
+    PvT = constants.pressure_water_vapor_triple_point
+
+    a1 = -0.212144006e2
+    a2 = 0.273203819e2
+    a3 = -0.610598130e1
+    b1 = 0.333333333e-2
+    b2 = 0.120666667e1
+    b3 = 0.170333333e1
+    theta = T / TvT
+    es = PvT * np.exp((a1 * theta**b1 + a2 * theta**b2 + a3 * theta**b3) / theta)
+    return es
+
+def liq_murphy_koop(T):
+    """Returns saturation vapor pressure (Pa) over liquid water
+
+    Encodes the empirical fit (Eq. 10) of Murphy and Koop (2011) which improves on the Wagner and
+    Pruß fits for supercooled conditions. 
+    
+    The fit has been verified for 123K <= T < = 332 K
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Murphy, D. M. & Koop, T. Review of the vapour pressures of ice and supercooled water for
+        atmospheric applications. Q. J. R. Meteorol. Soc. 131, 1539–1565 (2005).
+
+    >>> liq_murphy_koop(np.asarray([273.16,140.]))
+    array([6.11657044e+02, 9.39696372e-07])
+    """
+
+    X = np.tanh(0.0415 * (T - 218.8)) * (
+        53.878 - 1331.22 / T - 9.44523 * np.log(T) + 0.014025 * T
+    )
+    return np.exp(54.842763 - 6763.22 / T - 4.210 * np.log(T) + 0.000367 * T + X)
+
+
+def liq_hardy(T):
+    """Returns satruation vapor pressure (Pa) over liquid water
+
+    Encodes the empirical fit (Eq. 10) of Hardy (1998) which is often used in the postprocessing
+    of radiosondes
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Hardy, B., 1998, ITS-90 Formulations for Vapor Pressure, Frostpoint Temperature, Dewpoint
+        Temperature, and Enhancement Factors in the Range –100 to +100 °C, The Proceedings of the
+        Third International Symposium on Humidity & Moisture, London, England
+
+    >>> liq_hardy(np.asarray([273.16,260.]))
+    array([611.65715494, 222.65143353])
+    """
+    X = (
+        -2.8365744e3 / (T * T)
+        - 6.028076559e3 / T
+        + 19.54263612
+        - 2.737830188e-2 * T
+        + 1.6261698e-5 * T**2
+        + 7.0229056e-10 * T**3
+        - 1.8680009e-13 * T**4
+        + 2.7150305 * np.log(T)
+    )
+    return np.exp(X)
+
+
+def liq_analytic(T, delta_cl=constants.delta_cl):
+    """Analytic approximation for saturation vapor pressure over iquid
+
+    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
+    calculate the saturation vapor pressure over liquid.  The procedure is described in Eq(4) of
+    Romps (2017) and best approximates the actual value for specific heats that differ slightly
+    from the best estimates of these quantities which are provided as default quantities.
+    Romps recommends cl = 4119 J/kg/K, and cpv = 1861 J/kg/K.
+
+    Args:
+        T: temperature in kelvin
+        delta_cl: differnce between isobaric specific heat capacity of vapor and that of liquid.
+
+    Returns:
+        value of saturation vapor pressure over liquid water in Pa
+
+    Reference:
+        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
+        Sciences 74, 3891–3900 (2017).
+        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
+        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
+
+    >>> liq_analytic(np.asarray([273.16,305.]))
+    array([ 611.655     , 4711.13161169])
+    """
+    TvT = constants.temperature_water_vapor_triple_point
+    PvT = constants.pressure_water_vapor_triple_point
+    lvT = constants.vaporization_enthalpy_triple_point
+    Rv = constants.water_vapor_gas_constant
+
+    c1 = delta_cl / Rv
+    c2 = lvT / (Rv * TvT) - c1
+    es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1
+    return es
+
+
+def ice_analytic(T, delta_ci=constants.delta_ci):
+    """Analytic approximation for saturation vapor pressure over ice
+
+    Uses the rankine (constant specific heat, negligible condensate volume) approximations to
+    calculate the saturation vapor pressure over ice.  The procedure is described in Eq(4) of
+    Romps (2017) and best approximates the actual value for specific heats that differ slightly
+    from the best estimates of these quantities which are provided as default quantities.
+    Romps recommends ci = 1861 J/kg/K, and cpv = 1879 J/kg/K.
+
+    Args:
+        T: temperature in kelvin
+        delta_cl: differnce between isobaric specific heat capacity of vapor and that of liquid.
+
+    Returns:
+        value of saturation vapor pressure over liquid water in Pa
+
+    Reference:
+        Romps, D. M. Exact Expression for the Lifting Condensation Level. Journal of the Atmospheric
+        Sciences 74, 3891–3900 (2017).
+        Romps, D. M. Accurate expressions for the dew point and frost point derived from the Rankine-
+        Kirchhoff approximations. Journal of the Atmospheric Sciences (2021) doi:10.1175/JAS-D-20-0301.1.
+
+
+    >>> ice_analytic(np.asarray([273.16,260.]))
+    array([611.655     , 195.99959431])
+    """
+    TvT = constants.temperature_water_vapor_triple_point
+    PvT = constants.pressure_water_vapor_triple_point
+    lsT = constants.sublimation_enthalpy_triple_point
+    Rv = constants.water_vapor_gas_constant
+
+    c1 = delta_ci / Rv
+    c2 = lsT / (Rv * TvT) - c1
+    es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1
+    return es
-- 
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