From 43dc79898ed130c0b2f47d9c22fa40613cf8b6b5 Mon Sep 17 00:00:00 2001
From: bjorn-stevens <64255981+bjorn-stevens@users.noreply.github.com>
Date: Tue, 16 Aug 2022 14:29:14 +0200
Subject: [PATCH] revised analytic saturation vapor pressures

Attempted to clean up the presentation of the Teten's and Romps formulae
for saturation vapor pressuer, incorporating improved fitting constants
for the former.  These were derived by fitting to Wagner and Pruss for
the temperature range 270 K to 310 K and to Wagner et al for ice over
the range 230K to 260 K.  The fitting is not particular sensitive and
fitting with noice leads to slight changes in the fits, but the new
constants are robustly an improvement over the old ones, which had
inconsistent triple points vapor pressures over ice and liquid. I also
double checked the 'Romp's' formulae, and refrained from calling these
Romps as they are just straightforward integrations of the
Clausius-Clapeyron equation under the Rankine-Kirchoff assumptions.
---
 examples/examples.ipynb                       | 125 +++++++++++++++++-
 .../saturation_vapor_pressures.py             |  81 +++++++++++-
 2 files changed, 194 insertions(+), 12 deletions(-)

diff --git a/examples/examples.ipynb b/examples/examples.ipynb
index d36be7e..109f024 100644
--- a/examples/examples.ipynb
+++ b/examples/examples.ipynb
@@ -48,7 +48,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 648x288 with 2 Axes>"
       ]
@@ -61,16 +61,16 @@
    ],
    "source": [
     "Tw = np.arange(273.15, 330.0)\n",
-    "es = svp.liq_tetens_murray(Tw)\n",
+    "es = svp.liq_tetens(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 = svp.liq_tetens(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 = svp.ice_tetens(Tc)\n",
     "es_def = svp.ice_wagner_etal(Tc)\n",
     "err3 = (es / es_def - 1.0) * 100.0\n",
     "\n",
@@ -548,10 +548,125 @@
     "sns.despine(offset=10)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "129d7bbb-7015-484c-ac3a-97093314bc88",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5d88d98d-a59e-41fc-a5b6-124a8f97947b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best fit parameters for liquid: a=17.4143, b=33.6308\n",
+      "Best fit parameters for ice:    a=22.0422, b=5.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "def liq_error(T,a,b):\n",
+    "    return np.abs(svp.tetens(T,a,b)/svp.liq_wagner_pruss(T) -1.)\n",
+    "\n",
+    "def ice_error(T,a,b):\n",
+    "    return np.abs(svp.tetens(T,a,b)/svp.ice_wagner_etal(T) -1.)\n",
+    "\n",
+    "T = np.arange(270.,310.,0.1)\n",
+    "\n",
+    "rng = np.random.default_rng()\n",
+    "y_noise = 0.001 * rng.normal(size=T.size)\n",
+    "ydata =  y_noise\n",
+    "popt, pcov = curve_fit(liq_error, T, ydata, bounds = ((16.,33.), (19.,36.)), method='dogbox')\n",
+    "a_liq = popt[0]\n",
+    "b_liq = popt[1]\n",
+    "print (f'Best fit parameters for liquid: a={a_liq:.4f}, b={b_liq:.4f}')\n",
+    "\n",
+    "T = np.arange(230.,260.,0.01)\n",
+    "rng = np.random.default_rng()\n",
+    "y_noise = 0.001 * rng.normal(size=T.size)\n",
+    "ydata = y_noise\n",
+    "popt, pcov = curve_fit(ice_error, T, ydata, bounds = ((20.,5.), (23.,8.)), method='dogbox')\n",
+    "a_ice = popt[0]\n",
+    "b_ice = popt[1]\n",
+    "print (f'Best fit parameters for ice:    a={a_ice:.4f}, b={b_ice:.4f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "feac77dc-c04e-4fa2-b173-4dd143febcf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x216 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_context(\"talk\")\n",
+    "fig, ax = plt.subplots(1,3,figsize=(12, 3), constrained_layout=True)\n",
+    "\n",
+    "T = np.arange(200.,273.16)\n",
+    "es_c1 = (svp.ice_analytic(T)/svp.ice_wagner_etal(T) - 1.)*100.\n",
+    "es_c2 = (svp.ice_analytic(T,ci=1861.)/svp.ice_wagner_etal(T) - 1.)*100.\n",
+    "es_c3 = (svp.tetens(T,21.875,7.66)/svp.ice_wagner_etal(T) - 1.)*100.\n",
+    "es_c4 = (svp.tetens(T,a_ice,b_ice)/svp.ice_wagner_etal(T) - 1.)*100.\n",
+    "\n",
+    "ax[0].plot(T,np.abs(es_c1),c='k',label='Romps')\n",
+    "ax[0].plot(T,np.abs(es_c2),c='k',ls='dotted',label='Romps best fit')\n",
+    "ax[0].plot(T,np.abs(es_c3),c='violet',label='Teten-Murray')\n",
+    "ax[0].plot(T,np.abs(es_c4),c='violet',ls='dotted',label='Teten best fit')\n",
+    "T = np.arange(235.,273.16)\n",
+    "es_sc1 = (svp.liq_analytic(T)/svp.liq_murphy_koop(T) - 1.)*100.\n",
+    "es_sc2 = (svp.liq_analytic(T,cl=4119.)/svp.liq_murphy_koop(T) - 1.)*100.\n",
+    "es_sc3 = (svp.tetens(T,17.269,35.86)/svp.liq_murphy_koop(T) - 1.)*100.\n",
+    "es_sc4 = (svp.tetens(T,a_liq,b_liq)/svp.liq_murphy_koop(T) - 1.)*100.\n",
+    "\n",
+    "ax[1].plot(T,np.abs(es_sc1),c='k')\n",
+    "ax[1].plot(T,np.abs(es_sc2),c='k',ls='dotted')\n",
+    "ax[1].plot(T,np.abs(es_sc3),c='violet')\n",
+    "ax[1].plot(T,np.abs(es_sc4),c='violet',ls='dotted')\n",
+    "\n",
+    "T = np.arange(273.,330.)\n",
+    "es_w1 = (svp.liq_analytic(T)/svp.liq_wagner_pruss(T) - 1.)*100.\n",
+    "es_w2 = (svp.liq_analytic(T,cl=4119.)/svp.liq_wagner_pruss(T) - 1.)*100.\n",
+    "es_w3 = (svp.tetens(T,17.269,35.86)/svp.liq_wagner_pruss(T) - 1.)*100.\n",
+    "es_w4 = (svp.tetens(T,a_liq,b_liq)/svp.liq_wagner_pruss(T) - 1.)*100.\n",
+    "\n",
+    "ax[2].plot(T,np.abs(es_w1),c='k',label='Romps')\n",
+    "ax[2].plot(T,np.abs(es_w2),c='k',ls='dotted',label='Romps best fit')\n",
+    "ax[2].plot(T,np.abs(es_w3),c='violet',label='Teten-Murray')\n",
+    "ax[2].plot(T,np.abs(es_w4),c='violet',ls='dotted',label='Teten best fit')\n",
+    "\n",
+    "ax[0].legend()\n",
+    "for a in ax:\n",
+    "    a.set_ylabel('abs error / %')\n",
+    "    a.set_xlabel('T / K')\n",
+    "\n",
+    "sns.despine (offset=10)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0ffbde02-a695-45ca-8aa2-4c401651a913",
+   "id": "fef2f42d-8685-4cd8-9a4c-e0014f721f8c",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/moist_thermodynamics/saturation_vapor_pressures.py b/moist_thermodynamics/saturation_vapor_pressures.py
index fcecb6e..9893ddc 100644
--- a/moist_thermodynamics/saturation_vapor_pressures.py
+++ b/moist_thermodynamics/saturation_vapor_pressures.py
@@ -11,13 +11,14 @@ 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
@@ -85,12 +86,13 @@ def ice_wagner_etal(T):
     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. 
-    
+    Pruß fits for supercooled conditions.
+
     The fit has been verified for 123K <= T < = 332 K
 
     Args:
@@ -140,7 +142,7 @@ def liq_hardy(T):
     return np.exp(X)
 
 
-def liq_analytic(T, delta_cl=constants.delta_cl):
+def liq_analytic(T, cl=constants.cl):
     """Analytic approximation for saturation vapor pressure over iquid
 
     Uses the rankine (constant specific heat, negligible condensate volume) approximations to
@@ -170,13 +172,13 @@ def liq_analytic(T, delta_cl=constants.delta_cl):
     lvT = constants.vaporization_enthalpy_triple_point
     Rv = constants.water_vapor_gas_constant
 
-    c1 = delta_cl / Rv
+    c1 = (constants.cpv - 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):
+def ice_analytic(T, ci=constants.ci):
     """Analytic approximation for saturation vapor pressure over ice
 
     Uses the rankine (constant specific heat, negligible condensate volume) approximations to
@@ -207,7 +209,72 @@ def ice_analytic(T, delta_ci=constants.delta_ci):
     lsT = constants.sublimation_enthalpy_triple_point
     Rv = constants.water_vapor_gas_constant
 
-    c1 = delta_ci / Rv
+    c1 = (constants.cpv-ci) / Rv
     c2 = lsT / (Rv * TvT) - c1
     es = PvT * np.exp(c2 * (1.0 - TvT / T)) * (T / TvT) ** c1
     return es
+
+def tetens(T,a,b):
+    """Returns saturation vapor pressure over liquid using the Magnus-Teten's formula
+
+    This equation is written in a general form, with the constants a and b determining the fit.  As 
+    such it can be specified for either ice or water, or adapted as originally impelemented in ICON, 
+    in which case PvT and TvT need to be substituted by Pv0 and T0.
+    
+    Args:
+        T: temperature in kelvin
+
+    >>> tetens(285.,17.269,35.86)
+    1389.7114123472836
+    """
+
+    es = constants.PvT * np.exp(a * (T - constants.TvT) / (T - b))
+    return es
+
+
+def liq_tetens(T):
+    """Returns saturation vapor pressure over liquid using the Magnus-Teten's formula
+
+    This equation is what is used in the ICON code, hence its inclusion in this library.  The original
+    ICON implementation followed Murray's choice of constants (T0=273.15, Pv0=610.78, a=17.269, b=35.86).
+    This implementation is referenced to the triple point values of temperature and vapor and with 
+    revised constants (a,b) chosen to better agree with the fits of Wagner and Pruss
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Murray, F. W. On the Computation of Saturation Vapor Pressure. Journal of Applied Meteorology
+        and Climatology 6, 203–204 (1967).
+
+    >>> liq_tetens(np.asarray([273.16,305.]))
+    array([ 611.655     , 4719.73680592])
+    """
+    a = 17.41463775 
+    b = 33.6393413
+
+    return tetens(T,a,b)
+
+
+def ice_tetens(T):
+    """Returns saturation vapor pressure over liquid using the Magnus-Teten's formula
+
+    This equation is what is used in the ICON code, hence its inclusion in this library.  The original
+    ICON implementation followed Murray's choice of constants (T0=273.15, Pv0=610.78, a=21.875, b=7.66).
+    This implementation is referenced to the triple point values of temperature and vapor and with 
+    revised constants (a,b) chosen to better agree with the fits of Wagner and Pruss
+
+    Args:
+        T: temperature in kelvin
+
+    Reference:
+        Murray, F. W. On the Computation of Saturation Vapor Pressure. Journal of Applied Meteorology
+        and Climatology 6, 203–204 (1967).
+
+    >>> ice_tetens(np.asarray([273.16,260.]))
+    array([611.655     , 196.10072658])
+    """
+    a = 22.0419977
+    b = 5.
+
+    return tetens(T,a,b)
-- 
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