From e74043c37ea7361ea4f026a3df6c01e44e593c50 Mon Sep 17 00:00:00 2001 From: bjorn-stevens <64255981+bjorn-stevens@users.noreply.github.com> Date: Fri, 19 Aug 2022 12:52:11 +0200 Subject: [PATCH] revised generic fuctions as closures To define generic functions we use the closure construct which introduces a way to make a function with the given parameters so that when it is called it is executed with those parameters without having to pass the parameters. This allows the function calls for different quantities to share a common interface, and more allows for a cleaner separation between a function and its parameters, the latter being used to construct the function. --- examples/examples.ipynb | 214 ++++++++++++++++++------- moist_thermodynamics/constants_icon.py | 31 ++-- moist_thermodynamics/functions.py | 130 ++++++++------- 3 files changed, 244 insertions(+), 131 deletions(-) diff --git a/examples/examples.ipynb b/examples/examples.ipynb index 77a3abc..1a72033 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -490,7 +490,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 288x360 with 1 Axes>" ] @@ -502,6 +502,7 @@ } ], "source": [ + "es = mt.make_es_mxd(es_liq=svp.liq_analytic, es_ice=svp.ice_analytic)\n", "qt = 16.774409538883497e-3\n", "Tice, Py = mt.moist_adiabat(\n", " Tsfc,\n", @@ -511,7 +512,7 @@ " qt,\n", " cc=constants.ci,\n", " l=mt.sublimation_enthalpy,\n", - " es=mt.es_mxd,\n", + " es=es,\n", ")\n", "Tliq, Px = mt.moist_adiabat(\n", " Tsfc,\n", @@ -521,7 +522,7 @@ " qt,\n", " cc=constants.cl,\n", " l=mt.vaporization_enthalpy,\n", - " es=mt.es_mxd,\n", + " es=es,\n", ")\n", "\n", "Tx = np.ones(len(Px)) * constants.T0\n", @@ -558,49 +559,58 @@ "name": "stdout", "output_type": "stream", "text": [ - "Best fit parameters for liquid: a=17.4185, b=33.5714\n", - "Best fit parameters for ice: a=22.0422, b=5.0000\n" + "Best fit parameters for liquid: a=17.4276, b=33.4426\n", + "Best fit parameters for ice: a=22.0423, 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", + "def liq_error(T, a, b):\n", + " es = svp.make_tetens(Tref=constants.TvT, Pref=constants.PvT, a=a, b=b)\n", + " return np.abs(es(T) / svp.liq_wagner_pruss(T) - 1.0)\n", "\n", - "T = np.arange(270.,310.,0.1)\n", + "\n", + "def ice_error(T, a, b):\n", + " es = svp.make_tetens(Tref=constants.TvT, Pref=constants.PvT, a=a, b=b)\n", + " return np.abs(es(T) / svp.ice_wagner_etal(T) - 1.0)\n", + "\n", + "\n", + "T = np.arange(270.0, 310.0, 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", + "ydata = y_noise\n", + "popt, pcov = curve_fit(\n", + " liq_error, T, ydata, bounds=((16.0, 33.0), (19.0, 36.0)), method=\"dogbox\"\n", + ")\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", + "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", + "T = np.arange(230.0, 260.0, 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", + "popt, pcov = curve_fit(\n", + " ice_error, T, ydata, bounds=((20.0, 5.0), (23.0, 8.0)), method=\"dogbox\"\n", + ")\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}')" + "print(f\"Best fit parameters for ice: a={a_ice:.4f}, b={b_ice:.4f}\")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "feac77dc-c04e-4fa2-b173-4dd143febcf7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 864x216 with 3 Axes>" ] @@ -613,47 +623,143 @@ ], "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,cx=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,cx=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,cx=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", + "fig, ax = plt.subplots(1, 3, figsize=(12, 3), constrained_layout=True)\n", + "\n", + "ice_tetens_new = svp.make_tetens(\n", + " Tref=constants.TvT, Pref=constants.PvT, a=21.875, b=7.66\n", + ")\n", + "liq_tetens_new = svp.make_tetens(\n", + " Tref=constants.TvT, Pref=constants.PvT, a=17.269, b=35.86\n", + ")\n", + "ice_romps = svp.make_analytic(lx=constants.lsT, cx=1861.0)\n", + "liq_romps = svp.make_analytic(lx=constants.lvT, cx=4119.0)\n", + "\n", + "T = np.arange(200.0, 273.16)\n", + "es_c1 = (svp.ice_analytic(T) / svp.ice_wagner_etal(T) - 1.0) * 100.0\n", + "es_c2 = (ice_romps(T) / svp.ice_wagner_etal(T) - 1.0) * 100.0\n", + "es_c3 = (ice_tetens_new(T) / svp.ice_wagner_etal(T) - 1.0) * 100.0\n", + "es_c4 = (svp.ice_tetens(T) / svp.ice_wagner_etal(T) - 1.0) * 100.0\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", + "\n", + "T = np.arange(235.0, 273.16)\n", + "es_sc1 = (svp.liq_analytic(T) / svp.liq_murphy_koop(T) - 1.0) * 100.0\n", + "es_sc2 = (liq_romps(T) / svp.liq_murphy_koop(T) - 1.0) * 100.0\n", + "es_sc3 = (liq_tetens_new(T) / svp.liq_murphy_koop(T) - 1.0) * 100.0\n", + "es_sc4 = (svp.liq_tetens(T) / svp.liq_murphy_koop(T) - 1.0) * 100.0\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(273.0, 330.0)\n", + "es_w1 = (svp.liq_analytic(T) / svp.liq_wagner_pruss(T) - 1.0) * 100.0\n", + "es_w2 = (liq_romps(T) / svp.liq_wagner_pruss(T) - 1.0) * 100.0\n", + "es_w3 = (liq_tetens_new(T) / svp.liq_wagner_pruss(T) - 1.0) * 100.0\n", + "es_w4 = ((svp.liq_tetens(T)) / svp.liq_wagner_pruss(T) - 1.0) * 100.0\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", + " a.set_ylabel(\"abs error / %\")\n", + " a.set_xlabel(\"T / K\")\n", "\n", - "sns.despine (offset=10)" + "sns.despine(offset=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0512b036-c707-40dd-b09f-b1d1a9d58129", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<function make_analytic.<locals>.es at 0x1427c1040>\n" + ] + }, + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "T = 300.0\n", + "(lambda x: svp.make_analytic(lx=constants.lsT, cx=1861.0))(T)\n", + "print((lambda x: svp.make_analytic(lx=constants.lsT, cx=1861.0))(T))\n", + "(lambda x: x + 1)(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8a086e9b-ada2-48b0-b2d7-e008213ba960", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "358347.48647272115 311350.20551272115\n" + ] + } + ], + "source": [ + "hl = mt.liquid_water_static_energy\n", + "hm = mt.moist_static_energy\n", + "T, Z, qv = 305.0, 100.0, 15.0e-3\n", + "print(hm(T=T, qv=qv, Z=Z), hl(T=T, qv=qv, Z=Z))" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afa2c791-5cc9-487d-8280-94bd1b05ed85", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "202630e9-9ae3-429f-ad31-e17bf0e5a0b7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3531.9663621154004\n" + ] + } + ], + "source": [ + "print(es(300.0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b05e735-a4f5-46c0-b408-026a5155447d", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/moist_thermodynamics/constants_icon.py b/moist_thermodynamics/constants_icon.py index ca7eddd..b43cf28 100644 --- a/moist_thermodynamics/constants_icon.py +++ b/moist_thermodynamics/constants_icon.py @@ -2,21 +2,18 @@ """ Author: Bjorn Stevens (bjorn.stevens@mpimet.mpg.de) """ -# -cpd = isobaric_dry_air_specific_heat = 1004.64 # J/kg/K -rd = dry_air_gas_constant = 287.04 # J/kg/K -cpv = isobaric_water_vapor_specific_heat = 1869.46 # J/kg/K -rv = water_vapor_gas_constant = 461.51 # J/kg/K -clw = liquid_water_specific_heat = (3.1733 + 1.0) * cpd # J/kg/K -ci = forzen_water_specific_heat = 2108.00 # J/kg/K -g = earths_gravitational_acceleration = 9.80665 # m/s2 -lv = vaporization_enthalpy_at_melting_point = 2.5008e6 # J/kg -ls = sublimation_enthalpy_at_melting_point = 2.8345e6 # J/kg -Tmelt = melting_point_temperature = 273.15 # Kelvin - -cvd = isometric_dry_air_specific_heat = cpd-rd # J/kg/K -cvv = isometric_water_vapor_specific_heat = cpv-rv # J/kg/K -lf = melting_enthalpy = ls-lv # J/kg - - +# +cpd = isobaric_dry_air_specific_heat = 1004.64 # J/kg/K +rd = dry_air_gas_constant = 287.04 # J/kg/K +cpv = isobaric_water_vapor_specific_heat = 1869.46 # J/kg/K +rv = water_vapor_gas_constant = 461.51 # J/kg/K +clw = liquid_water_specific_heat = (3.1733 + 1.0) * cpd # J/kg/K +ci = forzen_water_specific_heat = 2108.00 # J/kg/K +g = earths_gravitational_acceleration = 9.80665 # m/s2 +lv = vaporization_enthalpy_at_melting_point = 2.5008e6 # J/kg +ls = sublimation_enthalpy_at_melting_point = 2.8345e6 # J/kg +Tmelt = melting_point_temperature = 273.15 # Kelvin +cvd = isometric_dry_air_specific_heat = cpd - rd # J/kg/K +cvv = isometric_water_vapor_specific_heat = cpv - rv # J/kg/K +lf = melting_enthalpy = ls - lv # J/kg diff --git a/moist_thermodynamics/functions.py b/moist_thermodynamics/functions.py index 469b28c..2dbc834 100644 --- a/moist_thermodynamics/functions.py +++ b/moist_thermodynamics/functions.py @@ -18,23 +18,80 @@ es_liq_default = saturation_vapor_pressures.liq_wagner_pruss es_ice_default = saturation_vapor_pressures.ice_wagner_etal -def es_mxd(T, es_liq=es_liq_default, es_ice=es_ice_default): - """Returns the minimum of the sublimation and saturation vapor pressure +def make_es_mxd(es_liq, es_ice): + """Closure to construct a mixed form of the saturation vapor pressure - Calculates both the sublimation vapor pressure over ice Ih using es_ice and that over planar - water using es_liq, and returns the minimum of the two quantities. + To provide a single function that provides the saturation vapor pressure + over ice when this is lower, and over water when this is lower, we use a + closure function which accepts different choices of the individual saturation + vapor pressures. Args: - T: temperature in kelvin + es_liq: function call for saturation vapor pressure over liquid + es_ice: function call for saturation vapor pressure over ice + + Returns: + function that selects the minimum of es_ice(T) and es_liq(T) + """ + + def es(T): + return np.minimum(es_liq(T), es_ice(T)) + + return es + + +def make_static_energy(hv0): + """Closure function to construct moist static energies + + When including the effects of composition on the specific heat to calcuate the moist enthalpy, a + constitutent part of the static energy different reference states can be adopted. These reference + states effectively weight the contribution of another invariant of the closed system (total water) + differently, leading to moist static energies which give different weights to the thermal and phase + change energies of the system. The closure funciton allows one to construct one or the other of + these static energies by choosing the approriate reference state vapor enthalpy, which then + determines the reference enthalpies of the other phases given the reference state phase change + enthalpies. How the choices affec the construct of the static energy are outlined as follows: + - hv0 = cpv*T0 -> frozen, liquid moist static energy + - hv0 = ls0 + ci*T0 -> frozen moist static energy + - hv0 = cpv*T0 -> liquid water static energy if qi= 0 (default if qv /= 0) + - hv0 = lv0 + cl*T0 -> moist static energy if qi= 0. + - qv=ql=q0=0 -> dry static energy (default) + + Args: + hv0: reference vapor enthalpy Returns: - value of es_ice(T) for T < 273.15 and es_liq(T) otherwise + h: a function for the moist static energy - >>> es_mxd(np.asarray([305.,260.])) - array([4719.32683147, 195.80103377]) """ - return np.minimum(es_liq(T), es_ice(T)) + def h(T, Z, qv=0, ql=0, qi=0): + """Returns moist static energies given the closure + + This function returns the static energy subject to the vapor reference state enthalpy as + given through the closure I + + Args: + T: temperature in kelvin + Z: altitude (above mean sea-level) in meters + qv: specific vapor mass + ql: specific liquid mass + qi: specific ice mass + """ + + qt = qv + ql + qi + x = ( + T * (1.0 - qt) * constants.cpd + + hv0 * qt + + (T - constants.T0) + * (qv * constants.cpv + ql * constants.cl + qi * constants.ci) + - ql * constants.lv0 + - qi * constants.ls0 + + constants.gravity_earth * Z + ) + return x + + return h def planck(T, nu): """Planck source function (J/m2 per steradian per Hz) @@ -149,57 +206,10 @@ def saturation_partition(P, ps, qt): return np.minimum(qt, qs) -def static_energy(T, Z, qv=0, ql=0, qi=0, hv0=constants.cpv * constants.T0): - """Returns the static energy - - The static energy is calculated so that it includes the effects of composition on the - specific heat if specific humidities are included. Different common forms of the static - energy arise from different choices of the reference state and condensate loading: - - hv0 = cpv*T0 -> frozen, liquid moist static energy - - hv0 = ls0 + ci*T0 -> frozen moist static energy - - hv0 = cpv*T0 -> liquid water static energy if qi= 0 (default if qv /= 0) - - hv0 = lv0 + cl*T0 -> moist static energy if qi= 0. - - qv=ql=q0=0 -> dry static energy (default) - - Because the composition weights the reference enthalpies, different choices do not differ by - a constant, but rather by a constant weighted by the specific masses of the different water - phases. - - Args: - T: temperature in kelvin - Z: altitude (above mean sea-level) in meters - qv: specific vapor mass - ql: specific liquid mass - qi: specific ice mass - hv0: reference vapor enthalpy - - >>> static_energy(300.,600.,15.e-3,hv0=constants.lv0 + constants.cl * constants.T0) - 358162.78621841426 - - """ - cpd = constants.isobaric_dry_air_specific_heat - cpv = constants.isobaric_water_vapor_specific_heat - cl = constants.liquid_water_specific_heat - ci = constants.frozen_water_specific_heat - lv0 = constants.lv0 - ls0 = constants.ls0 - T0 = constants.T0 - g = constants.gravity_earth - - qd = 1.0 - qv - ql - qi - cp = qd * cpd + qv * cpv + ql * cl + qi * ci - - h = ( - qd * cpd * T - + qv * cpv * T - + ql * cl * T - + qi * ci * T - + qv * (hv0 - cpv * T0) - + ql * (hv0 - lv0 - cl * T0) - + qi * (hv0 - ls0 - ci * T0) - + g * Z - ) - return h +moist_static_energy = make_static_energy( + hv0=constants.lv0 + constants.cl * constants.T0 +) +liquid_water_static_energy = make_static_energy(hv0=constants.cpv * constants.T0) def theta(T, P, qv=0.0, ql=0.0, qi=0.0): -- GitLab