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@benbovy
Created March 9, 2015 09:44
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Extract tropopause levels from NCEP reanalyses
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{"nbformat_minor": 0, "nbformat": 4, "metadata": {"kernelspec": {"language": "python", "display_name": "PyGChem Dev (py27)", "name": "pygchemdev_py27"}, "language_info": {"nbconvert_exporter": "python", "version": "2.7.9", "pygments_lexer": "ipython2", "file_extension": ".py", "codemirror_mode": {"version": 2, "name": "ipython"}, "mimetype": "text/x-python", "name": "python"}}, "cells": [{"cell_type": "markdown", "source": "# Extract tropopause levels from NCEP Global Reanalyses\n\n\nThe goal is to use outputs from NCEP Global Reanalyses to calculate and extract information about the global distribution of the altitude of the tropopause. We use the [xray](https://github.com/xray/xray) Python package for handling the data (including either local or remote loading).\n\nAuthor: B. Bovy (Mar 2015).", "metadata": {}}, {"outputs": [], "cell_type": "code", "execution_count": 1, "metadata": {"collapsed": false, "trusted": false}, "source": "import pandas as pd\nimport xray\n\nimport matplotlib.pyplot as plt\n\nplt.style.use('ggplot')\n%matplotlib inline"}, {"cell_type": "markdown", "source": "## Get the monthly mean values of pressure at the tropopause\n\nWe access the data (i.e., the monthly mean values of the tropopause pressure) via [OPeNDAP](http://www.opendap.org/). We only need to specify the URL of the dataset, and `xray` loads it as a `xray.Dataset` object.", "metadata": {}}, {"outputs": [], "cell_type": "code", "execution_count": 2, "metadata": {"collapsed": false, "trusted": false}, "source": "noaa_baseurl = \"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets\"\nncep_src = \"ncep.reanalysis.derived\"\ntropp_data_src = \"tropopause/pres.tropp.mon.mean.nc\"\n\ntropp_data_url = \"/\".join([noaa_baseurl, ncep_src, tropp_data_src])\n\ntropp_mon_mean = xray.open_dataset(tropp_data_url)"}, {"cell_type": "markdown", "source": "View information about the pressure variable (the only one in the dataset). The array has 3 dimensions: lat, lon and time (each month since 01-1948).", "metadata": {}}, {"outputs": [{"data": {"text/plain": "<xray.DataArray 'pres' (time: 806, lat: 73, lon: 144)>\n[8472672 values with dtype=float64]\nCoordinates:\n * lat (lat) float32 90.0 87.5 85.0 82.5 80.0 77.5 75.0 72.5 70.0 67.5 65.0 62.5 ...\n * lon (lon) float32 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0 ...\n * time (time) datetime64[ns] 1948-01-01 1948-02-01 1948-03-01 1948-04-01 ...\nAttributes:\n long_name: Monthly Mean of Pressure\n units: millibars\n precision: 2\n var_desc: Pressure\n dataset: CDC Derived NCEP Reanalysis Products\n level_desc: Tropopause\n statistic: Mean\n parent_stat: Other\n valid_range: [-102.19999695 102.19999695]\n actual_range: [ 85. 419.70001221]\n _ChunkSize: [ 1 73 144]"}, "output_type": "execute_result", "metadata": {}, "execution_count": 3}], "cell_type": "code", "execution_count": 3, "metadata": {"collapsed": false, "trusted": false}, "source": "tropp_mon_mean.pres"}, {"cell_type": "markdown", "source": "## Get the monthly mean values of sea level pressure\n\nThe cells below perform the same operations as for the pressure at the tropopause.", "metadata": {}}, {"outputs": [], "cell_type": "code", "execution_count": 4, "metadata": {"collapsed": false, "trusted": false}, "source": "slp_data_src = \"surface/slp.mon.mean.nc\"\nslp_data_url = \"/\".join([noaa_baseurl, ncep_src, slp_data_src])\n\nslp_mon_mean = xray.open_dataset(slp_data_url)"}, {"outputs": [{"data": {"text/plain": "<xray.DataArray 'slp' (time: 806, lat: 73, lon: 144)>\n[8472672 values with dtype=float64]\nCoordinates:\n * lat (lat) float32 90.0 87.5 85.0 82.5 80.0 77.5 75.0 72.5 70.0 67.5 65.0 62.5 ...\n * lon (lon) float32 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0 ...\n * time (time) datetime64[ns] 1948-01-01 1948-02-01 1948-03-01 1948-04-01 ...\nAttributes:\n long_name: Sea Level Pressure\n valid_range: [ 870. 1150.]\n units: millibars\n precision: 1\n var_desc: Sea Level Pressure\n dataset: CDC Derived NCEP Reanalysis Products\n level_desc: Sea Level\n statistic: Mean\n parent_stat: Other\n actual_range: [ 958.97631836 1082.55822754]\n _ChunkSize: [ 1 73 144]"}, "output_type": "execute_result", "metadata": {}, "execution_count": 5}], "cell_type": "code", "execution_count": 5, "metadata": {"collapsed": false, "trusted": false}, "source": "slp_mon_mean.slp"}, {"cell_type": "markdown", "source": "## Get the monthly mean values of potential temperature\n\nThe potential temperature is calculated at the sigma level 995.", "metadata": {}}, {"outputs": [], "cell_type": "code", "execution_count": 6, "metadata": {"collapsed": true, "trusted": false}, "source": "pottmp_data_src = \"surface/pottmp.sig995.mon.mean.nc\"\npottmp_data_url = \"/\".join([noaa_baseurl, ncep_src, pottmp_data_src])\n\npottmp_mon_mean = xray.open_dataset(pottmp_data_url)"}, {"outputs": [{"data": {"text/plain": "<xray.DataArray 'pottmp' (time: 806, lat: 73, lon: 144)>\n[8472672 values with dtype=float64]\nCoordinates:\n * lat (lat) float32 90.0 87.5 85.0 82.5 80.0 77.5 75.0 72.5 70.0 67.5 65.0 62.5 ...\n * lon (lon) float32 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0 ...\n * time (time) datetime64[ns] 1948-01-01 1948-02-01 1948-03-01 1948-04-01 ...\nAttributes:\n long_name: Monthly mean potential temperature at sigma level 995\n units: degK\n precision: 1\n GRIB_id: 13\n GRIB_name: POT\n var_desc: Potential Temperature\n dataset: CDC Derived NCEP Reanalysis Products\n level_desc: Surface\n statistic: Mean\n parent_stat: Mean\n valid_range: [ 150. 900.]\n actual_range: [ 214.59985352 334.69995117]\n _ChunkSize: [ 1 73 144]"}, "output_type": "execute_result", "metadata": {}, "execution_count": 7}], "cell_type": "code", "execution_count": 7, "metadata": {"collapsed": false, "trusted": false}, "source": "pottmp_mon_mean.pottmp"}, {"cell_type": "markdown", "source": "## Calculate the altitude of the tropopause given the sea level pressure, the tropopause pressure and the potential temperature\n\nWe use the following simple model of pressure vs. altitude (assuming a column of dry air that behaves like a perfect gas):\n\n$$ p_t \\approx p_0 \\left(1 - \\frac{g \\cdot h_t}{c_p \\cdot T_0}\\right)^{\\frac{c_p \\cdot M}{R}} \\approx p_0 \\left(1 - 0.0065 \\frac{h_t}{T_0}\\right)^{5.2561} $$\n\nwhere $p_t$ is the pressure at the tropopause (Pa), $p_0$ is the pressure at sea level, $h_t$ is the altitude of the tropopause (m), $T_0$ is the potential temperature (K), $c_p$ is the constant pressure specific heat ($\\sim$ 1007 J kg$^{-1}$ K$^{-1}$), $g$ is the Earth-surface gravitational acceleration (9.81 m s$^{-2}$), $M$ is the molar mass of dry air (0.0289644 kg mol$^{-1}$) and $R$ is the universal gas constant (8.31447 J mol$^{-1}$ K$^{-1}$).\n\nBy rearranging the equation, we express $h_t$ as:\n\n$$ h_t \\approx \\frac{T_0}{0.0065} \\left(1 - \\left[\\frac{p_t}{p_0}\\right]^{\\frac{1}{5.2561}}\\right) $$\n\nSince `xray` supports broadcasting, it is very simple and straightforward to apply the latter expression to the lat/lon/time `xray.DataArray` objects imported above. Note however that no data has been loaded into memory so far (only *lazy loading*). As the calculation here requires the access to the data values, the cell below will load the all the data silently (it may thus take some time).", "metadata": {}}, {"outputs": [], "cell_type": "code", "execution_count": 8, "metadata": {"collapsed": false, "trusted": false}, "source": "tropp_alt_mon_mean = (\n pottmp_mon_mean.pottmp / 0.0065 *\n (1. - (tropp_mon_mean.pres / slp_mon_mean.slp)**(1. / 5.2561))\n)\n"}, {"cell_type": "markdown", "source": "We add some metadata to the `xray.DataArray` object", "metadata": {}}, {"outputs": [], "cell_type": "code", "execution_count": 9, "metadata": {"collapsed": false, "trusted": false}, "source": "tropp_alt_mon_mean.name = \"tropp_alt\"\n\nextra_attrs = {\n 'long_name': \"Monthly mean altitude of the tropopause\",\n 'units': \"m\",\n 'var_desc': \"Altitude\", \n 'dataset': \"Calculated from CDC Derived NCEP Reanalysis Products\",\n 'statistic': \"Mean\",\n 'level_desc': \"Tropopause\"\n}\n \ntropp_alt_mon_mean.attrs.update(extra_attrs) "}, {"cell_type": "markdown", "source": "## Extract and plot tropopause altitudes\n\nThe tropopause altitudes calculated above consist of monthly mean values in a 3-d array with lat, lon and time (each month since 01-1948) coordinates. We can further extract specific values or statistics from this result.\n\nFor example, the cell below calculates the monthly means for the whole period covered by the data. it returns a 3-d array of tropopause altitudes with lat/lon coordinates and a 12-months coordinate.", "metadata": {}}, {"outputs": [{"data": {"text/plain": "<xray.DataArray 'tropp_alt' (lat: 73, lon: 144, month: 12)>\narray([[[ 8116.05819264, 7878.01075448, 7631.67599982, ...,\n 7978.70967424, 7886.30457889, 7984.28838302],\n [ 8116.05819264, 7878.01075448, 7631.67599982, ...,\n 7978.70967424, 7886.30457889, 7984.28838302],\n [ 8116.05819264, 7878.01075448, 7631.67599982, ...,\n 7978.70967424, 7886.30457889, 7984.28838302],\n ..., \n [ 8116.05819264, 7878.01075448, 7631.67599982, ...,\n 7978.70967424, 7886.30457889, 7984.28838302],\n [ 8116.05819264, 7878.01075448, 7631.67599982, ...,\n 7978.70967424, 7886.30457889, 7984.28838302],\n [ 8116.05819264, 7878.01075448, 7631.67599982, ...,\n 7978.70967424, 7886.30457889, 7984.28838302]],\n\n [[ 8167.37238156, 7945.00968961, 7632.69155567, ...,\n 7949.69819633, 7872.08809317, 7996.14542397],\n [ 8175.5445141 , 7955.31681188, 7642.23188988, ...,\n 7957.09451615, 7879.32556884, 8003.55792617],\n [ 8183.02269173, 7965.60889832, 7650.55812647, ...,\n 7963.31728846, 7886.17602303, 8010.26747786],\n ..., \n [ 8141.11869954, 7913.74343184, 7605.21559976, ...,\n 7929.86468522, 7850.19513078, 7972.8356489 ],\n [ 8150.06537252, 7924.44595713, 7614.16322501, ...,\n 7936.78525705, 7857.55707227, 7981.26110662],\n [ 8159.89588394, 7935.02867264, 7622.6156195 , ...,\n 7943.0122403 , 7864.09336197, 7989.2929909 ]],\n\n [[ 8241.4072492 , 8027.48703872, 7649.03312434, ...,\n 7959.29388418, 7886.28816755, 8008.57907985],\n [ 8255.83505602, 8048.46848072, 7666.91153609, ...,\n 7971.51215039, 7901.91010631, 8021.24247355],\n [ 8267.99261294, 8065.55912257, 7685.69883715, ...,\n 7983.79559727, 7917.14454843, 8034.98414087],\n ..., \n [ 8192.50564729, 7965.77602045, 7595.90293667, ...,\n 7921.83469119, 7842.59858212, 7970.1281703 ],\n [ 8208.08817094, 7987.14615253, 7613.45266455, ...,\n 7934.1902622 , 7856.75221977, 7982.89582049],\n [ 8224.06041553, 8006.60633874, 7632.36058335, ...,\n 7946.42727884, 7871.37884137, 7995.66697797]],\n\n ..., \n [[ 8557.59974797, 7734.94241601, 7299.15345283, ...,\n 10711.59535771, 10083.6849549 , 9062.50148572],\n [ 8563.29228973, 7731.12177064, 7283.74571865, ...,\n 10706.11951447, 10089.78692137, 9067.51203366],\n [ 8569.83418417, 7728.1697101 , 7269.84602027, ...,\n 10700.90681604, 10090.48006526, 9071.95636349],\n ..., \n [ 8539.37916638, 7747.29062901, 7356.40081938, ...,\n 10734.63170196, 10067.89613996, 9041.02912792],\n [ 8546.60219753, 7743.95777045, 7338.42639359, ...,\n 10724.14355224, 10074.56235945, 9050.29065573],\n [ 8552.6980106 , 7739.21626925, 7318.8260675 , ...,\n 10715.98735493, 10079.25917003, 9057.22509771]],\n\n [[ 8400.44089775, 7657.56189812, 7242.35444359, ...,\n 10548.27104459, 9898.36834657, 8834.53804493],\n [ 8407.1853191 , 7661.46945985, 7240.57345238, ...,\n 10537.40098717, 9900.29617215, 8841.49102934],\n [ 8415.07199502, 7664.07002801, 7239.11379901, ...,\n 10527.56062428, 9900.53025967, 8847.51345662],\n ..., \n [ 8379.1839044 , 7649.39963545, 7252.30074347, ...,\n 10575.12565726, 9892.1421935 , 8813.27842479],\n [ 8386.47062063, 7652.12952291, 7247.35826685, ...,\n 10566.33268571, 9894.21301793, 8820.39432507],\n [ 8394.07953327, 7654.8651069 , 7244.3792076 , ...,\n 10556.44268319, 9896.66297159, 8826.84503621]],\n\n [[ 8397.10591179, 7754.72755597, 7351.50591822, ...,\n 10595.24422517, 9859.68685651, 8780.36849249],\n [ 8397.10591179, 7754.72755597, 7351.50591822, ...,\n 10595.24422517, 9859.68685651, 8780.36849249],\n [ 8397.10591179, 7754.72755597, 7351.50591822, ...,\n 10595.24422517, 9859.68685651, 8780.36849249],\n ..., \n [ 8397.10614596, 7754.72755597, 7351.50591822, ...,\n 10595.24422517, 9859.68685651, 8780.36849249],\n [ 8397.10614596, 7754.72755597, 7351.50591822, ...,\n 10595.24422517, 9859.68685651, 8780.36849249],\n [ 8397.10614596, 7754.72755597, 7351.50591822, ...,\n 10595.24422517, 9859.68685651, 8780.36849249]]])\nCoordinates:\n * lat (lat) float32 90.0 87.5 85.0 82.5 80.0 77.5 75.0 72.5 70.0 67.5 65.0 62.5 ...\n * lon (lon) float32 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 30.0 ...\n * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\nAttributes:\n var_desc: Altitude\n level_desc: Tropopause\n dataset: Calculated from CDC Derived NCEP Reanalysis Products\n long_name: Monthly mean altitude of the tropopause\n statistic: Mean\n units: m"}, "output_type": "execute_result", "metadata": {}, "execution_count": 10}], "cell_type": "code", "execution_count": 10, "metadata": {"collapsed": false, "trusted": false}, "source": "tropp_alt_12m = tropp_alt_mon_mean.groupby(\n tropp_alt_mon_mean['time.month']\n).mean(dim='time')\n\ntropp_alt_12m"}, {"cell_type": "markdown", "source": "We can further extract the latter monthly means at a given lat/lon grid box...", "metadata": {}}, {"outputs": [{"data": {"text/plain": "month\n1 10568.959686\n2 10319.699557\n3 10532.036659\n4 10604.373389\n5 11249.109759\n6 11790.134361\n7 12352.397100\n8 12522.640535\n9 12399.906081\n10 11989.458068\n11 11201.981552\n12 10787.736650\nName: tropp_alt, dtype: float64"}, "output_type": "execute_result", "metadata": {}, "execution_count": 11}], "cell_type": "code", "execution_count": 11, "metadata": {"collapsed": false, "trusted": false}, "source": "gridbox_lat = 45.0\ngridbox_lon = 7.5\n\ntropp_alt_series = tropp_alt_12m.sel(\n lat=gridbox_lat, lon=gridbox_lon\n).to_series()\n\ntropp_alt_series"}, {"cell_type": "markdown", "source": "... or get and plot the complete time series for that grid box", "metadata": {}}, {"outputs": [{"data": {"text/plain": "<matplotlib.figure.Figure at 0x7f6d52f4fd50>", "image/png": 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MDiGxy61AFomkbh1uxx9ZrkllfBT1Dk0HYsTXIeNNK+PHoJ5LqG1J7G37Ecn4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\n4yHtbJIZ1w4gi1lZudISiQNnCIwnZSqazXEnzBAY30VbMw7hAcHd+vX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oBYkBzCbOuASM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\nREQCUTIuIiIiIhKIknERERERkUD+PxXyQSvI6SRFAAAAAElFTkSuQmCC\n"}, "output_type": "display_data", "metadata": {}}], "cell_type": "code", "execution_count": 12, "metadata": {"collapsed": false, "trusted": false}, "source": "tropp_alt_series_all = tropp_alt_mon_mean.sel(\n lat=gridbox_lat, lon=gridbox_lon\n).to_series()\n\nfig, ax = plt.subplots(figsize=(12, 5))\n\ntropp_alt_series_all.plot(ax=ax)\n\n_ = plt.setp(ax, ylabel=\"tropopause height (m)\")"}, {"cell_type": "markdown", "source": "Another example: a contour plot of monthly means averaged over the longitude.", "metadata": {"collapsed": true}}, {"outputs": [{"data": {"text/plain": "<matplotlib.figure.Figure at 0x7f6d52de0cd0>", "image/png": 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BFymbZuXSpFln5i8Bfgm4iSreewGPAq4AHgNgZke7++UhBhla2/GehWbvIiIy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\n"}, "output_type": "display_data", "metadata": {}}], "cell_type": "code", "execution_count": 13, "metadata": {"collapsed": false, "trusted": false}, "source": "tropp_alt_lon_avg = tropp_alt_12m.mean(dim='lon')\n\nfig, ax = plt.subplots(figsize=(8, 8))\n\nc = ax.contourf(tropp_alt_lon_avg.month,\n tropp_alt_lon_avg.lat,\n tropp_alt_lon_avg,\n 20)\n\nplt.colorbar(c)\n\n_ = plt.setp(ax,\n xlabel=\"month\", ylabel=\"latitude (deg N)\",\n yticks=range(-90, 110, 30), xticks=range(1,13),\n xticklabels=['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']\n)"}, {"outputs": [], "cell_type": "code", "execution_count": null, "metadata": {"collapsed": true, "trusted": false}, "source": ""}]}
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