{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Climatology calculations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Imports\n", "from earthkit.transforms import aggregate as ek_aggregate\n", "from earthkit import data as ek_data\n", "from earthkit.data.testing import earthkit_remote_test_data_file\n", "ek_data.settings.set(\"cache-policy\", \"user\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Load some test data\n", "\n", "In this example we will use hourly ERA5 2m temperature data on a 0.5x0.5 spatial grid for the year 2015 as\n", "our physical data; and we will use the NUTS geometries which are stored in a geojson file.\n", "\n", "All `earthkit-transforms` methods can be called with `earthkit-data` objects (Readers and Wrappers) or with a pre-loaded `xarray`. To reduce the number of conversions in the example, we will convert to xarray in the first cell and use that data object for all subsequent steps." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "18b52a343a9849a7862e37ad763a862c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "era5_temperature_france_2015_2016_2017_3deg.grib: 0%| | 0.00/109k [00:00\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 111kB\n",
       "Dimensions:     (number: 1, valid_time: 542, surface: 1, latitude: 7,\n",
       "                 longitude: 7)\n",
       "Coordinates:\n",
       "  * number      (number) int64 8B 0\n",
       "  * valid_time  (valid_time) datetime64[ns] 4kB 2015-01-01 ... 2017-03-31T12:...\n",
       "  * surface     (surface) float64 8B 0.0\n",
       "  * latitude    (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n",
       "  * longitude   (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n",
       "Data variables:\n",
       "    t2m         (number, valid_time, surface, latitude, longitude) float32 106kB ...\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2024-07-12T08:59 GRIB to CDM+CF via cfgrib-0.9.1...
" ], "text/plain": [ " Size: 111kB\n", "Dimensions: (number: 1, valid_time: 542, surface: 1, latitude: 7,\n", " longitude: 7)\n", "Coordinates:\n", " * number (number) int64 8B 0\n", " * valid_time (valid_time) datetime64[ns] 4kB 2015-01-01 ... 2017-03-31T12:...\n", " * surface (surface) float64 8B 0.0\n", " * latitude (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n", " * longitude (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n", "Data variables:\n", " t2m (number, valid_time, surface, latitude, longitude) float32 106kB ...\n", "Attributes:\n", " GRIB_edition: 1\n", " GRIB_centre: ecmf\n", " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n", " GRIB_subCentre: 0\n", " Conventions: CF-1.7\n", " institution: European Centre for Medium-Range Weather Forecasts\n", " history: 2024-07-12T08:59 GRIB to CDM+CF via cfgrib-0.9.1..." ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get some demonstration ERA5 data, this could be any url or path to an ERA5 grib or netCDF file.\n", "remote_era5_file = earthkit_remote_test_data_file(\"test-data\", \"era5_temperature_france_2015_2016_2017_3deg.grib\")\n", "era5_data = ek_data.from_source(\"url\", remote_era5_file)\n", "\n", "# convert to xarray to save repeated conversion in further steps\n", "era5_xr = era5_data.to_xarray(xarray_open_dataset_kwargs=dict(time_dims=[\"valid_time\"]))\n", "era5_xr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate the climatologies of the ERA5 data\n", "\n", "Monthly mean" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 740B\n",
       "Dimensions:    (month: 3, number: 1, surface: 1, latitude: 7, longitude: 7)\n",
       "Coordinates:\n",
       "  * number     (number) int64 8B 0\n",
       "  * surface    (surface) float64 8B 0.0\n",
       "  * latitude   (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n",
       "  * longitude  (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n",
       "  * month      (month) int64 24B 1 2 3\n",
       "Data variables:\n",
       "    t2m        (month, number, surface, latitude, longitude) float32 588B 278...\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2024-07-12T08:59 GRIB to CDM+CF via cfgrib-0.9.1...
" ], "text/plain": [ " Size: 740B\n", "Dimensions: (month: 3, number: 1, surface: 1, latitude: 7, longitude: 7)\n", "Coordinates:\n", " * number (number) int64 8B 0\n", " * surface (surface) float64 8B 0.0\n", " * latitude (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n", " * longitude (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n", " * month (month) int64 24B 1 2 3\n", "Data variables:\n", " t2m (month, number, surface, latitude, longitude) float32 588B 278...\n", "Attributes:\n", " GRIB_edition: 1\n", " GRIB_centre: ecmf\n", " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n", " GRIB_subCentre: 0\n", " Conventions: CF-1.7\n", " institution: European Centre for Medium-Range Weather Forecasts\n", " history: 2024-07-12T08:59 GRIB to CDM+CF via cfgrib-0.9.1..." ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "climatology_monthly_mean = ek_aggregate.climatology.monthly_mean(era5_xr)\n", "# # The following line would also work, but we have already converted the data to xarray,\n", "# # so do not need to do it again.\n", "# climatology = ek_aggregate.climatology.monthly_mean(era5_data)\n", "climatology_monthly_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Climatology of the daily mean:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 19kB\n",
       "Dimensions:    (dayofyear: 91, number: 1, surface: 1, latitude: 7, longitude: 7)\n",
       "Coordinates:\n",
       "  * number     (number) int64 8B 0\n",
       "  * surface    (surface) float64 8B 0.0\n",
       "  * latitude   (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n",
       "  * longitude  (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n",
       "  * dayofyear  (dayofyear) int64 728B 1 2 3 4 5 6 7 8 ... 85 86 87 88 89 90 91\n",
       "Data variables:\n",
       "    t2m        (dayofyear, number, surface, latitude, longitude) float32 18kB ...\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2024-07-12T08:59 GRIB to CDM+CF via cfgrib-0.9.1...
" ], "text/plain": [ " Size: 19kB\n", "Dimensions: (dayofyear: 91, number: 1, surface: 1, latitude: 7, longitude: 7)\n", "Coordinates:\n", " * number (number) int64 8B 0\n", " * surface (surface) float64 8B 0.0\n", " * latitude (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n", " * longitude (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n", " * dayofyear (dayofyear) int64 728B 1 2 3 4 5 6 7 8 ... 85 86 87 88 89 90 91\n", "Data variables:\n", " t2m (dayofyear, number, surface, latitude, longitude) float32 18kB ...\n", "Attributes:\n", " GRIB_edition: 1\n", " GRIB_centre: ecmf\n", " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n", " GRIB_subCentre: 0\n", " Conventions: CF-1.7\n", " institution: European Centre for Medium-Range Weather Forecasts\n", " history: 2024-07-12T08:59 GRIB to CDM+CF via cfgrib-0.9.1..." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "climatology_daily_mean = ek_aggregate.climatology.daily_mean(era5_xr)\n", "climatology_daily_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Repeat for the monthly maximum, minimum and standard deviation" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 748B\n",
       "Dimensions:    (month: 3, number: 1, step: 1, surface: 1, latitude: 7,\n",
       "                longitude: 7)\n",
       "Coordinates:\n",
       "  * number     (number) int64 8B 0\n",
       "  * step       (step) timedelta64[ns] 8B 00:00:00\n",
       "  * surface    (surface) float64 8B 0.0\n",
       "  * latitude   (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n",
       "  * longitude  (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n",
       "  * month      (month) int64 24B 1 2 3\n",
       "Data variables:\n",
       "    t2m        (month, number, step, surface, latitude, longitude) float32 588B ...\n",
       "Attributes:\n",
       "    GRIB_edition:            1\n",
       "    GRIB_centre:             ecmf\n",
       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
       "    GRIB_subCentre:          0\n",
       "    Conventions:             CF-1.7\n",
       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
       "    history:                 2024-07-12T08:59 GRIB to CDM+CF via cfgrib-0.9.1...
" ], "text/plain": [ " Size: 748B\n", "Dimensions: (month: 3, number: 1, step: 1, surface: 1, latitude: 7,\n", " longitude: 7)\n", "Coordinates:\n", " * number (number) int64 8B 0\n", " * step (step) timedelta64[ns] 8B 00:00:00\n", " * surface (surface) float64 8B 0.0\n", " * latitude (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n", " * longitude (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n", " * month (month) int64 24B 1 2 3\n", "Data variables:\n", " t2m (month, number, step, surface, latitude, longitude) float32 588B ...\n", "Attributes:\n", " GRIB_edition: 1\n", " GRIB_centre: ecmf\n", " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n", " GRIB_subCentre: 0\n", " Conventions: CF-1.7\n", " institution: European Centre for Medium-Range Weather Forecasts\n", " history: 2024-07-12T08:59 GRIB to CDM+CF via cfgrib-0.9.1..." ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clim_max = ek_aggregate.climatology.monthly_max(era5_data)\n", "clim_min = ek_aggregate.climatology.monthly_min(era5_data)\n", "clim_std = ek_aggregate.climatology.monthly_std(era5_data)\n", "clim_std" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quantiles\n", "\n", "Please note the api for quantiles is slightly different, it requires an additional argument `q` which is a list of the quantiles to return.\n", "\n", "Additionally, the returned object has `quantiles` dimension which is for each of the quantiles returned." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 4kB\n",
       "Dimensions:    (quantile: 3, month: 3, number: 1, surface: 1, latitude: 7,\n",
       "                longitude: 7)\n",
       "Coordinates:\n",
       "  * number     (number) int64 8B 0\n",
       "  * surface    (surface) float64 8B 0.0\n",
       "  * latitude   (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n",
       "  * longitude  (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n",
       "  * quantile   (quantile) float64 24B 0.1 0.5 0.9\n",
       "  * month      (month) int64 24B 1 2 3\n",
       "Data variables:\n",
       "    t2m        (quantile, month, number, surface, latitude, longitude) float64 4kB dask.array<chunksize=(1, 1, 1, 1, 7, 7), meta=np.ndarray>
" ], "text/plain": [ " Size: 4kB\n", "Dimensions: (quantile: 3, month: 3, number: 1, surface: 1, latitude: 7,\n", " longitude: 7)\n", "Coordinates:\n", " * number (number) int64 8B 0\n", " * surface (surface) float64 8B 0.0\n", " * latitude (latitude) float64 56B 48.0 45.0 42.0 39.0 36.0 33.0 30.0\n", " * longitude (longitude) float64 56B 0.0 3.0 6.0 9.0 12.0 15.0 18.0\n", " * quantile (quantile) float64 24B 0.1 0.5 0.9\n", " * month (month) int64 24B 1 2 3\n", "Data variables:\n", " t2m (quantile, month, number, surface, latitude, longitude) float64 4kB dask.array" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quantiles = ek_aggregate.climatology.quantiles(era5_xr, [0.1, 0.5, 0.9], frequency='month')\n", "quantiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot the output for a random location" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from datetime import datetime, timedelta\n", "import xarray as xr\n", "isel_kwargs = {\"latitude\":2, \"longitude\":4}\n", "\n", "fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10,5))\n", "\n", "for data in [climatology_monthly_mean, clim_min, clim_max]:\n", " var_name = list(data.data_vars.keys())[0]\n", " c_point = data[var_name].isel(**isel_kwargs)\n", " c_time = [datetime(2000,i,15) for i in c_point.month.values]\n", " ax.plot(c_time, c_point.values.flat, label=f'Monthly {var_name}')\n", "\n", "\n", "data = climatology_daily_mean\n", "var_name = list(data.data_vars.keys())[0]\n", "c_point = data[var_name].isel(**isel_kwargs)\n", "c_time = [datetime(2000,1,1)+timedelta(days=int(i-1)) for i in c_point.dayofyear.values]\n", "ax.plot(c_time, c_point.values.flat, label=f'Daily {var_name}')\n", "\n", "ax.legend()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.8" }, "vscode": { "interpreter": { "hash": "58c973d167e6390f895c0b1f5bfc390aa59fea9f5b6b442df4c096a67487aa21" } } }, "nbformat": 4, "nbformat_minor": 4 }