{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "AirSeaFluxCode is developed to provide an easy and accessible way to calculate turbulent surface fluxes (TSFs) from a small number of bulk variables and for a viariety of bulk algorithms. \n", "\n", "By running AirSeaFluxCode you can compare different bulk algorithms and to also investigate the effect choices within the implementation of each parameterisation have on the TSFs estimates. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting started\n", "\n", "Let's first import the basic python packages we will need for reading in our input data, to perform basic statistics and plotting" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# first import all packages you might need\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import netCDF4 as nc\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exchange coefficients\n", "We can implement a function to calculate the exchange coefficients and momentum roughness length given the method and the 10 metre neutral wind speed (u10n). Note that these are the same functions included in flux_subs.py (cdn_calc, ctcqn_calc, cdn_from roughness) that are called in AirSeaFluxCode, just adjusted to run with dummy input data generated in the next step." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "kappa = 0.4 # von Karman's constant\n", "def blk_neutral(meth, u10n):\n", " ub = np.maximum(u10n, 0.1)\n", " # first guess\n", " cdn = 8.575e-5*ub + 0.657e-3\n", " ctn, cqn = np.zeros(u10n.shape), np.zeros(u10n.shape)\n", " zo, zc = np.zeros(u10n.shape), np.zeros(u10n.shape)\n", " zs = np.zeros(u10n.shape)\n", " if (meth == \"S80\"):\n", " cdn = (0.61+0.063*u10n)*0.001\n", " elif (meth == \"LP82\" or meth == \"LP82(zol<=0)\" or meth == \"LP82(zol>0)\"):\n", " cdn = np.where(u10n < 4, 1.14*0.001,\n", " np.where((u10n < 11) & (u10n >= 4), 1.2*0.001,\n", " (0.49+0.065*u10n)*0.001))\n", " elif (meth == \"YT96\"):\n", " # convert usr in eq. 21 to cdn to expand for low wind speeds\n", " cdn = np.power((0.10038+u10n*2.17e-3+np.power(u10n, 2)*2.78e-3 -\n", " np.power(u10n, 3)*4.4e-5)/u10n, 2)\n", " elif (meth == \"LY04\" or meth == \"LY04(zol<=0)\" or meth == \"LY04(zol>0)\"):\n", " cdn = np.where(u10n > 0.5, (0.142+2.7/u10n+u10n/13.09 -\n", " 3.14807e-10*np.power(u10n, 6))*1e-3,\n", " (0.142+2.7/0.5+0.5/13.09 -\n", " 3.14807e-10*np.power(0.5, 6))*1e-3)\n", " cdn = np.where(u10n > 33, 2.34e-3, np.copy(cdn))\n", " cdn = np.maximum(np.copy(cdn), 0.1e-3)\n", " else:\n", " for it in range(50):\n", " usr = ub*np.sqrt(cdn)\n", " if (meth == \"S88\"):\n", " # Charnock roughness length (eq. 4 in Smith 88)\n", " zc = 0.011*np.power(usr, 2)/9.8\n", " # smooth surface roughness length (eq. 6 in Smith 88)\n", " zs = 0.11*1.5e-5/usr\n", " zo = zc + zs # eq. 7 & 8 in Smith 88\n", " elif (meth == \"UA\"):\n", " # valid for 0 10, 0.011+(u10n-10)*(0.018-0.011)/(18-10),\n", " np.where(u10n > 18, 0.018, a))\n", " zo = a*np.power(usr, 2)/9.8+0.11*1.5e-5/usr\n", " elif (meth == \"C35\"):\n", " zo = (0.11*1.5e-5/usr +\n", " np.minimum(0.0017*19-0.0050, 0.0017*u10n-0.0050) *\n", " np.power(usr, 2)/9.8)\n", " elif ((meth == \"ecmwf\" or meth == \"Beljaars\")):\n", " # eq. (3.26) p.38 over sea IFS Documentation cy46r1\n", " zo = 0.018*np.power(usr, 2)/9.8+0.11*1.5e-5/usr\n", " else:\n", " print(\"method unknown\")\n", " cdn = np.power(kappa/np.log(10/zo), 2)\n", "\n", " if (meth == \"S80\" or meth == \"S88\" or meth == \"YT96\"):\n", " cqn = np.ones(u10n.shape)*1.20*0.001 # from S88\n", " ctn = np.ones(u10n.shape)*1.00*0.001\n", " elif (meth == \"LP82(zol<=0)\"):\n", " cqn = np.ones(u10n.shape)*1.15*0.001\n", " ctn = np.ones(u10n.shape)*1.13*0.001\n", " elif (meth == \"LP82(zol>0)\"):\n", " cqn = np.ones(u10n.shape)*0.001\n", " ctn = np.ones(u10n.shape)*0.66*0.001\n", " elif (meth == \"LY04(zol>0)\"):\n", " cqn = np.maximum(34.6*0.001*np.sqrt(cdn), 0.1e-3)\n", " ctn = np.maximum(18*0.001*np.sqrt(cdn), 0.1e-3)\n", " elif (meth == \"LY04(zol<=0)\"):\n", " cqn = np.maximum(34.6*0.001*np.sqrt(cdn), 0.1e-3)\n", " ctn = np.maximum(32.7*0.001*np.sqrt(cdn), 0.1e-3)\n", " elif (meth == \"UA\"):\n", " usr = np.sqrt(cdn*np.power(u10n, 2))\n", " # Zeng et al. 1998 (25)\n", " usr = np.sqrt(cdn*np.power(u10n, 2))\n", " rr = usr*zo/1.5e-5\n", " zoq = zo/np.exp(2.67*np.power(rr, 1/4)-2.57)\n", " zot = np.copy(zoq)\n", " cqn = np.power(kappa, 2)/(np.log(10/zo)*np.log(10/zoq))\n", " ctn = np.power(kappa, 2)/(np.log(10/zo)*np.log(10/zoq))\n", " elif (meth == \"C30\"):\n", " usr = np.sqrt(cdn*np.power(u10n, 2))\n", " rr = zo*usr/1.5e-5\n", " zoq = np.minimum(5e-5/np.power(rr, 0.6), 1.15e-4) # moisture roughness\n", " zot = np.copy(zoq) # temperature roughness\n", " cqn = np.power(kappa, 2)/np.log(10/zo)/np.log(10/zoq)\n", " ctn = np.power(kappa, 2)/np.log(10/zo)/np.log(10/zot)\n", " elif (meth == \"C35\"):\n", " usr = np.sqrt(cdn*np.power(u10n, 2))\n", " rr = zo*usr/1.5e-5\n", " zoq = np.minimum(5.8e-5/np.power(rr, 0.72), 1.6e-4) # moisture roughness\n", " zot = np.copy(zoq) # temperature roughness\n", " cqn = np.power(kappa, 2)/np.log(10/zo)/np.log(10/zoq)\n", " ctn = np.power(kappa, 2)/np.log(10/zo)/np.log(10/zot)\n", "\n", " elif (meth == \"ecmwf\" or meth == \"Beljaars\"):\n", " # eq. (3.26) p.38 over sea IFS Documentation cy46r1\n", " usr = np.sqrt(cdn*np.power(u10n, 2))\n", " zot = 0.40*1.5e-5/usr\n", " zoq = 0.62*1.5e-5/usr\n", " cqn = kappa**2/np.log(10/zo)/np.log(10/zoq)\n", " ctn = kappa**2/np.log(10/zo)/np.log(10/zot)\n", " else:\n", " print(\"unknown method ctcqn: \"+meth)\n", " return cdn, ctn, cqn, zo\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, generate \"dummy\" values for u10n to use as input to blk_neutral." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "u10n = np.zeros(1201)\n", "for jw in range(2, 1201):\n", " dw = 0.25*(60/(1201-1))\n", " if (u10n[jw-1] >= 5):\n", " dw = 60/(1201-1)\n", " if (u10n[jw-1] >= 30):\n", " dw = 2*(60/(1201-1))\n", " u10n[jw] = u10n[jw-1]+dw" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, you can run blk_neutral for any method (\"S80\", \"S88\", \"LP82(zol<=0)\", \"LP82(zol>0)\", \"YT96\", \"UA\", \"LY04(zol<=0)\", \"LY04(zol>0)\", \"C30\", \"C35\", \"C40\", \"ecmwf\", \"Beljaars\") and with the \"dummy\" u10n we generated.\n", "Let's try it for UA and plot the result." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cdn, ctn, cqn, zo = blk_neutral(\"UA\", u10n)\n", "fig, ax = plt.subplots(2, 2, sharex=True, sharey=False)\n", "fig.tight_layout()\n", "# fig.subplots_adjust(wspace=0.5)\n", "ax[0, 0].plot(u10n, cdn*1e3, \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[0, 1].plot(u10n, ctn*1e3, \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[1, 0].plot(u10n, cqn*1e3, \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[1, 1].plot(u10n, zo, \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[0, 0].set_ylabel('C$_{dn}$x10$^{-3}$')\n", "ax[0, 0].set_ylim([0.8, 3])\n", "ax[0, 0].set_xlim([0, 36])\n", "ax[0, 1].set_ylabel('C$_{tn}$x10$^{-3}$')\n", "ax[0, 1].set_ylim([0.5, 3])\n", "ax[0, 1].set_xlim([0, 36])\n", "ax[1, 0].set_ylabel('C$_{qn}$x10$^{-3}$')\n", "ax[1, 0].set_ylim([0.8, 3])\n", "ax[1, 0].set_xlim([0, 36])\n", "ax[1, 1].set_ylabel('z$_{o}$ (m)')\n", "ax[1, 1].set_ylim([-0.001, 0.01])\n", "ax[1, 1].ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n", "ax[1, 1].set_xlim([0, 36])\n", "ax[1, 0].set_xlabel('u$_{10n}$ (m/s)')\n", "ax[1, 1].set_xlabel('u$_{10n}$ (m/s)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AirSeaFluxCode examples\n", "\n", "AirSeaFluxCode is set up to run in its default setting with a minimum number of input variables, namely wind speed; air temperature; and sea surface temperature. Let's load the code, import some real data composed for testing it (Research vessel data) and run AirSeaFluxCode with default settings (latitude set to 45°N, relative humidity 80%, atmospheric pressure 1013hPa, sensor height 18m, output height 10m, cool skin/warm layer corrections turned off, bulk algorithm Smith 1988, gustiness on, saturation vapour pressure following Buck, 2012, tolerance limits set for flux estimates and height adjusted variables (['all', 0.01, 0.01, 1e-05, 1e-3, 0.1, 0.1]), number of iterations are ten, non converged points are set to missing and Monin-Obukhov stability length is calculated following the ECMWF implementation." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from AirSeaFluxCode import AirSeaFluxCode" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "inDt = pd.read_csv(\"data_all.csv\")\n", "date = np.asarray(inDt[\"Date\"])\n", "lon = np.asarray(inDt[\"Longitude\"])\n", "lat = np.asarray(inDt[\"Latitude\"])\n", "spd = np.asarray(inDt[\"Wind speed\"])\n", "t = np.asarray(inDt[\"Air temperature\"])\n", "sst = np.asarray(inDt[\"SST\"])\n", "rh = np.asarray(inDt[\"RH\"])\n", "p = np.asarray(inDt[\"P\"])\n", "sw = np.asarray(inDt[\"Rs\"])\n", "hu = np.asarray(inDt[\"zu\"])\n", "ht = np.asarray(inDt[\"zt\"])\n", "hin = np.array([hu, ht, ht])\n", "del hu, ht, inDt\n", "# run AirSeaFluxCode\n", "res = AirSeaFluxCode(spd, t, sst)\n", "flg = res[\"flag\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "res is the output of AirSeaFluxCode which is a dataFrame with keys: \"tau\", \"shf\", \"lhf\", \"L\", \"cd\", \"cdn\", \"ct\", \"ctn\", \"cq\", \"cqn\", \"tsrv\", \"tsr\", \"qsr\", \"usr\", \"psim\", \"psit\",\"psiq\", \"u10n\", \"t10n\", \"tv10n\", \"q10n\", \"zo\", \"zot\", \"zoq\", \"uref\", \"tref\", \"qref\", \"iteration\", \"dter\", \"dqer\", \"dtwl\", \"qair\", \"qsea\", \"Rl\", \"Rs\", \"Rnl\", \"ug\", \"Rib\", \"rh\". Let's plot the flux estimates." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(3, 1, sharex=True, sharey=False)\n", "fig.tight_layout()\n", "ax[0].plot(res[\"tau\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[1].plot(res[\"shf\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[2].plot(res[\"lhf\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[0].set_ylabel('tau (Nm$^{-2}$)')\n", "ax[1].set_ylabel('shf (Wm$^{-2}$)')\n", "ax[2].set_ylabel('lhf (Wm$^{-2}$)')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use the real input we have for latitude, sensor heights, relative humidity, air pressure and shortwave radiation. We will also use the cool skin correction option for bulk algorithm Coare 3.5 (C35) and run 30 iterations." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "res = AirSeaFluxCode(spd, t, sst, lat=lat, hin=hin, P=p, hum=['rh', rh], Rs=sw, cskin=1, skin=\"C35\", meth=\"C35\", n=30)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot the results\n", "fig, ax = plt.subplots(3, 1, sharex=True, sharey=False)\n", "fig.tight_layout()\n", "ax[0].plot(res[\"tau\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[1].plot(res[\"shf\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[2].plot(res[\"lhf\"], \"-\", color=\"grey\", linewidth=1, alpha = 0.8)\n", "ax[0].set_ylabel('tau (Nm$^{-2}$)')\n", "ax[1].set_ylabel('shf (Wm$^{-2}$)')\n", "ax[2].set_ylabel('lhf (Wm$^{-2}$)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 4 }