{ "cells": [ { "cell_type": "markdown", "id": "0437be3c-b72e-452f-afc8-a54b3a8c6408", "metadata": {}, "source": [ "### Calculate geopotential from pressure & temperature data\n", "\n", "Calculates geopotential from pressure and temperature data for a single timestep over the entire AUS2200 domain. Can be used to provide geopotential more frequently that is output by the model. " ] }, { "cell_type": "code", "execution_count": 1, "id": "9a6fdf17-da72-4043-8c69-d2d63473c49d", "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "from scipy.interpolate import interp1d\n", "from scipy.integrate import trapezoid\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from dask.distributed import Client" ] }, { "cell_type": "code", "execution_count": 2, "id": "9bd6f7e2-3326-4ce3-82e6-2525665d5821", "metadata": {}, "outputs": [], "source": [ "Rd = 287.1\n", "g0 = 9.81\n", "\n", "specific_humidity_at_level = 'fld_s00i010'\n", "pressure_at_level = 'fld_s00i408'\n", "temperature_at_level = 'fld_s16i004'\n", "\n", "surface_pressure = 'fld_s00i409'\n", "surface_temperature = 'fld_s00i024'\n", "specific_humidity = 'fld_s03i237'\n", "\n", "plev19=np.array([100000, 92500, 85000, 70000,\n", " 60000, 50000, 40000, 30000,\n", " 25000, 20000, 15000, 10000,\n", " 7000, 5000, 3000, 2000,\n", " 1000, 500, 100],dtype=np.float32)\n", "\n", "def interp_and_integrate_over_pressure_with_surf(pres, var, surf_pres, surf_var, plev):\n", "\n", " pres_with_surf = np.insert(pres,0,surf_pres)\n", " var_with_surf = np.insert(var,0,surf_var)\n", "\n", " vint = interp1d(pres_with_surf,var_with_surf,kind=\"linear\",fill_value=\"extrapolate\")\n", " interped = vint(plev)\n", "\n", " all_pres = np.zeros(len(pres_with_surf)+len(plev))\n", " all_fld = np.zeros(len(pres_with_surf)+len(plev))\n", " plev_idx = np.zeros(len(plev),dtype=int)\n", " surf_idx = -1\n", " ### Now intersperse with original var\n", " i = 0\n", " j = 0\n", "\n", " for _ in range(len(all_pres)):\n", " if j >= len(pres_with_surf):\n", " pres_test = -1\n", " else:\n", " pres_test = pres_with_surf[j]\n", " if i >= len(plev):\n", " plev_test = -1\n", " else:\n", " plev_test = plev[i]\n", " \n", " if plev_test > pres_test:\n", " if j == 0:\n", " all_pres[i+j] = 0\n", " else:\n", " all_pres[i+j] = plev_test\n", " all_fld[i+j] = interped[i]\n", " plev_idx[i] = i+j\n", " i+=1\n", " else:\n", " all_pres[i+j] = pres_test\n", " all_fld[i+j] = var_with_surf[j]\n", " if j == 0:\n", " surf_idx = i+j\n", " j+=1\n", " \n", " out_fld = np.zeros(len(plev_idx))\n", " for out_i,idx in enumerate(plev_idx):\n", " if idx < surf_idx:\n", " continue\n", " out_fld[out_i] = trapezoid(all_fld[surf_idx:idx+1],all_pres[surf_idx:idx+1]) * Rd * np.log(all_pres[idx]/all_pres[surf_idx]) / g0 / ( all_pres[surf_idx] - all_pres[idx] )\n", " return out_fld\n", "\n", "def interp_and_integrate_over_pressure(pres, var, plev):\n", "\n", " vint = interp1d(pres,var,kind=\"linear\",fill_value=\"extrapolate\")\n", " interped = vint(plev)\n", "\n", " all_pres = np.zeros(len(pres)+len(plev))\n", " all_fld = np.zeros(len(pres)+len(plev))\n", " plev_idx = np.zeros(len(plev),dtype=int)\n", " surf_idx = -1\n", " ### Now intersperse with original var\n", " i = 0\n", " j = 0\n", "\n", " for _ in range(len(all_pres)):\n", " if j >= len(pres):\n", " pres_test = -1\n", " else:\n", " pres_test = pres[j]\n", " if i >= len(plev):\n", " plev_test = -1\n", " else:\n", " plev_test = plev[i]\n", " \n", " if plev_test > pres_test:\n", " if j == 0:\n", " all_pres[i+j] = 0\n", " else:\n", " all_pres[i+j] = plev_test\n", " all_fld[i+j] = interped[i]\n", " plev_idx[i] = i+j\n", " i+=1\n", " else:\n", " all_pres[i+j] = pres_test\n", " all_fld[i+j] = var[j]\n", " if j == 0:\n", " surf_idx = i+j\n", " j+=1\n", " \n", " out_fld = np.zeros(len(plev_idx))\n", " for out_i,idx in enumerate(plev_idx):\n", " if idx < surf_idx:\n", " continue\n", " out_fld[out_i] = trapezoid(all_fld[surf_idx:idx+1],all_pres[surf_idx:idx+1]) * Rd * np.log(all_pres[idx]/all_pres[surf_idx]) / g0 / ( all_pres[surf_idx] - all_pres[idx] )\n", " return out_fld\n", "### Tv = (1 + 0.618*specific_humidity)*temperature\n", "\n", "### delta_Z = Rd * Tv / g0 * ln(p1/p2)" ] }, { "cell_type": "code", "execution_count": 3, "id": "9f34332e-48d4-4a81-8b0b-c57b18a3a152", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-08-22 12:18:47,923 - distributed.preloading - INFO - Creating preload: /g/data/hh5/public/apps/dask-optimiser/schedplugin.py\n", "2024-08-22 12:18:47,925 - distributed.utils - INFO - Reload module schedplugin from .py file\n", "2024-08-22 12:18:47,932 - distributed.preloading - INFO - Import preload module: /g/data/hh5/public/apps/dask-optimiser/schedplugin.py\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Modifying workers\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client=Client()\n", "client" ] }, { "cell_type": "code", "execution_count": 4, "id": "da0d1408-2891-4bae-9672-76699ce82118", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/hh5/public/apps/cms_conda/envs/analysis3-24.07/lib/python3.10/site-packages/xarray/core/dataset.py:277: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 212. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", "/g/data/hh5/public/apps/cms_conda/envs/analysis3-24.07/lib/python3.10/site-packages/xarray/core/dataset.py:277: UserWarning: The specified chunks separate the stored chunks along dimension \"lon_0\" starting at index 260. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n" ] } ], "source": [ "#ds = xr.open_dataset(\"/g/data/hh5/tmp/AUS2200/flood22-continuous/atmos/umnsa_mdl_20220221T1400_1hr.nc\").sel(lat=-37.81, lon_0=144.96, method='nearest').isel(time=0)\n", "#ds = xr.open_dataset(\"/g/data/hh5/tmp/AUS2200/flood22-continuous/atmos/umnsa_mdl_20220221T1400_1hr.nc\").sel(lat=-7, lon_0=146, method='nearest').isel(time=0)\n", "ds = xr.open_dataset(\"/g/data/hh5/tmp/AUS2200/flood22-continuous/atmos/umnsa_mdl_20220221T1400_1hr.nc\",chunks={\"model_theta_level_number\":-1,\"lat\":212,\"lon_0\":260}).isel(time=0)" ] }, { "cell_type": "code", "execution_count": 5, "id": "bd49d816-10e5-408a-b863-08e901d9c6ad", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/hh5/public/apps/cms_conda/envs/analysis3-24.07/lib/python3.10/site-packages/xarray/core/dataset.py:277: UserWarning: The specified chunks separate the stored chunks along dimension \"lat\" starting at index 212. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n", "/g/data/hh5/public/apps/cms_conda/envs/analysis3-24.07/lib/python3.10/site-packages/xarray/core/dataset.py:277: UserWarning: The specified chunks separate the stored chunks along dimension \"lon\" starting at index 260. This could degrade performance. Instead, consider rechunking after loading.\n", " warnings.warn(\n" ] } ], "source": [ "#ds3 = xr.open_dataset(\"/g/data/hh5/tmp/AUS2200/flood22-continuous/atmos/umnsa_spec_20220221T1400_10min.nc\").sel(lat=-37.81, lon=144.96, method='nearest').isel(time=5)\n", "#ds3 = xr.open_dataset(\"/g/data/hh5/tmp/AUS2200/flood22-continuous/atmos/umnsa_spec_20220221T1400_10min.nc\").sel(lat=-7, lon=146, method='nearest').isel(time=5)\n", "ds3 = xr.open_dataset(\"/g/data/hh5/tmp/AUS2200/flood22-continuous/atmos/umnsa_spec_20220221T1400_10min.nc\",chunks={\"lat\":212,\"lon\":260}).isel(time=5)" ] }, { "cell_type": "code", "execution_count": 6, "id": "2c165f43-abec-4294-99f5-0199f97faa8b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "  * model_theta_level_number  (model_theta_level_number) int32 280B 1 2 ... 70\n",
       "    theta_level_height        (model_theta_level_number) float64 560B dask.array<chunksize=(70,), meta=np.ndarray>\n",
       "    sigma_theta               (model_theta_level_number) float64 560B dask.array<chunksize=(70,), meta=np.ndarray>
" ], "text/plain": [ " Size: 2GB\n", "dask.array\n", "Coordinates:\n", " time datetime64[ns] 8B 2022-02-21T15:00:00\n", " * lat (lat) float64 17kB -48.79 -48.77 ... -6.852 -6.832\n", " * lon (lon) float64 21kB 107.5 107.5 ... 159.0 159.0\n", " * model_theta_level_number (model_theta_level_number) int32 280B 1 2 ... 70\n", " theta_level_height (model_theta_level_number) float64 560B dask.array\n", " sigma_theta (model_theta_level_number) float64 560B dask.array" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_tv = (1 + 0.618 * ds[specific_humidity_at_level])*ds[temperature_at_level]\n", "ds_tv = ds_tv.rename({'lon_0':'lon'})\n", "ds_tv" ] }, { "cell_type": "code", "execution_count": 7, "id": "9f916e51-c49a-40bb-b29f-c8521696243f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Size: 22MB\n", "dask.array\n", "Coordinates:\n", " time datetime64[ns] 8B 2022-02-21T15:00:00\n", " * lat (lat) float64 17kB -48.79 -48.77 ... -6.852 -6.832\n", " * lon (lon) float64 21kB 107.5 107.5 ... 159.0 159.0\n", " theta_level_height float64 8B 5.0\n", " model_theta_level_number int32 4B 1\n", " sigma_theta float64 8B 0.9994\n", " height float64 8B 10.0\n", " height_0 float64 8B 1.5" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tv_surf = (1 + 0.618 * ds3[specific_humidity]) * ds3[surface_temperature]\n", "tv_surf" ] }, { "cell_type": "code", "execution_count": 8, "id": "f64e1a05-969a-4fd8-ace1-fcd5d3a6a79b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'fld_s00i409' (lat: 2120, lon: 2600)> Size: 22MB\n",
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       "    long_name:        SURFACE PRESSURE AFTER TIMESTEP\n",
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" ], "text/plain": [ " Size: 22MB\n", "dask.array\n", "Coordinates:\n", " time datetime64[ns] 8B 2022-02-21T15:00:00\n", " * lat (lat) float64 17kB -48.79 -48.77 ... -6.852 -6.832\n", " * lon (lon) float64 21kB 107.5 107.5 ... 159.0 159.0\n", " theta_level_height float64 8B ...\n", " model_theta_level_number int32 4B ...\n", " sigma_theta float64 8B ...\n", " height float64 8B ...\n", " height_0 float64 8B ...\n", "Attributes:\n", " standard_name: surface_air_pressure\n", " long_name: SURFACE PRESSURE AFTER TIMESTEP\n", " units: Pa\n", " um_stash_source: m01s00i409\n", " grid_mapping: latitude_longitude" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "surf_pres = ds3[surface_pressure]\n", "surf_pres" ] }, { "cell_type": "raw", "id": "9af18a44-ff23-4236-8fc3-30aa9d4cfda5", "metadata": {}, "source": [ "ds_tv_pl = xr.apply_ufunc(\n", " interp_and_integrate_over_pressure_with_surf,\n", " ds[pressure_at_level].rename({'lon_0':'lon'}),\n", " ds_tv,\n", " surf_pres,\n", " tv_surf,\n", " plev19,\n", " input_core_dims=[[\"model_theta_level_number\"],[\"model_theta_level_number\"],[],[],[\"pressure_level\"]],\n", " output_core_dims=[ [\"pressure_level\"] ],\n", " exclude_dims=set((\"model_theta_level_number\",)),\n", " vectorize=True,\n", " dask=\"parallelized\"\n", ")" ] }, { "cell_type": "code", "execution_count": 9, "id": "20363ba2-6ea7-4882-82dd-d171d70b2930", "metadata": {}, "outputs": [], "source": [ "ds_tv_pl = xr.apply_ufunc(\n", " interp_and_integrate_over_pressure,\n", " ds[pressure_at_level].rename({'lon_0':'lon'}),\n", " ds_tv,\n", " plev19,\n", " input_core_dims=[[\"model_theta_level_number\"],[\"model_theta_level_number\"],[\"pressure_level\"]],\n", " output_core_dims=[ [\"pressure_level\"] ],\n", " exclude_dims=set((\"model_theta_level_number\",)),\n", " vectorize=True,\n", " dask=\"parallelized\"\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "id": "017bc49a-f70d-461e-a354-836e4fbc20e1", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Coordinates:\n",
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       "  * lon      (lon) float64 21kB 107.5 107.5 107.6 107.6 ... 158.9 159.0 159.0\n",
       "Dimensions without coordinates: pressure_level
" ], "text/plain": [ " Size: 838MB\n", "dask.array\n", "Coordinates:\n", " time datetime64[ns] 8B 2022-02-21T15:00:00\n", " * lat (lat) float64 17kB -48.79 -48.77 -48.75 ... -6.871 -6.852 -6.832\n", " * lon (lon) float64 21kB 107.5 107.5 107.6 107.6 ... 158.9 159.0 159.0\n", "Dimensions without coordinates: pressure_level" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_tv_pl" ] }, { "cell_type": "code", "execution_count": 11, "id": "3671df36-cc7a-431c-b6f8-0e6c7af4654a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/jobfs/123409378.gadi-pbs/ipykernel_2374207/3741949889.py:108: RuntimeWarning: invalid value encountered in scalar divide\n", "/jobfs/123409378.gadi-pbs/ipykernel_2374207/3741949889.py:108: RuntimeWarning: invalid value encountered in scalar divide\n" ] } ], "source": [ "ds_tv_pl = ds_tv_pl.compute()" ] }, { "cell_type": "code", "execution_count": 12, "id": "c332eefc-4cd2-4815-909b-6a6f57e4e0a6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'fld_s16i202' (pressure: 16)> Size: 64B\n",
       "array([  151.,   583.,   805.,  1499.,  1999.,  2533.,  3098.,  3698.,\n",
       "        4336.,  5750.,  7406.,  9418., 10630., 12064., 13882., 16402.],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "    time      datetime64[ns] 8B 2022-02-21T15:00:00\n",
       "  * pressure  (pressure) float64 128B 1e+05 9.5e+04 9.25e+04 ... 1.5e+04 1e+04\n",
       "    lat       float64 8B -37.82\n",
       "    lon       float64 8B 145.0\n",
       "Attributes:\n",
       "    standard_name:    geopotential_height\n",
       "    long_name:        GEOPOTENTIAL HEIGHT ON P LEV/P GRID\n",
       "    units:            m\n",
       "    um_stash_source:  m01s16i202\n",
       "    grid_mapping:     latitude_longitude
" ], "text/plain": [ " Size: 64B\n", "array([ 151., 583., 805., 1499., 1999., 2533., 3098., 3698.,\n", " 4336., 5750., 7406., 9418., 10630., 12064., 13882., 16402.],\n", " dtype=float32)\n", "Coordinates:\n", " time datetime64[ns] 8B 2022-02-21T15:00:00\n", " * pressure (pressure) float64 128B 1e+05 9.5e+04 9.25e+04 ... 1.5e+04 1e+04\n", " lat float64 8B -37.82\n", " lon float64 8B 145.0\n", "Attributes:\n", " standard_name: geopotential_height\n", " long_name: GEOPOTENTIAL HEIGHT ON P LEV/P GRID\n", " units: m\n", " um_stash_source: m01s16i202\n", " grid_mapping: latitude_longitude" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds2 = xr.open_dataset(\"/g/data/hh5/tmp/AUS2200/flood22-continuous/atmos/umnsaa_pverd000_20220221T1200_6hr.nc\")\n", "gph = ds2['fld_s16i202'].sel(lat=-37.81, lon=144.96, method='nearest').isel(time=1).load()\n", "gph" ] }, { "cell_type": "code", "execution_count": 13, "id": "e21979ed-7636-49b5-a485-109a4ed9668b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(gph.data,gph.pressure.data)" ] }, { "cell_type": "code", "execution_count": 14, "id": "2ac114a8-9d7c-4236-a93d-0e865c9fc8ce", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure()\n", "ax1 = fig.add_subplot(111)\n", "\n", "\n", "ax1.scatter(gph.data, gph.pressure.data, c='b', marker=\"s\", label='From model')\n", "ax1.scatter(ds_tv_pl.sel(lat=-37.81, lon=144.96, method='nearest')[:12],plev19[:12], c='r', marker=\"o\", label='calculated')\n", "plt.legend(loc='upper left')\n", "plt.gca().invert_yaxis()\n", "plt.xlabel(\"Geopotential Height (m)\")\n", "plt.ylabel(\"Pressure (Pa)\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "id": "e2d9b49f-86c4-4733-baca-d8ba062c34d5", "metadata": {}, "outputs": [], "source": [ "model_indices=[0,2,3,6,8,9,10,11,12,13,14,15]" ] }, { "cell_type": "code", "execution_count": 16, "id": "d273c68c-7c91-49b4-a0ec-a30e94e3ad01", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(( gph.data[model_indices] - ds_tv_pl.sel(lat=-37.81, lon=144.96, method='nearest')[:12])/gph.data[model_indices]).plot()\n", "plt.xlabel(\"Pressure Level\")\n", "plt.ylabel(\"Relative error\")\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:analysis3-24.07]", "language": "python", "name": "conda-env-analysis3-24.07-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 5 }