{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Preparing Meteorological Inputs #\n", "\n", "This section shows a complete example of the current NetCDF-based meteorological workflow in `pycequeau`.\n", "\n", "The workflow assumes that the project already contains a `meteo/ERA` folder with prepared NetCDF files for the basin.\n", "\n", "1. create the `Basin` object\n", "2. derive an additional meteorological variable when needed\n", "3. load the NetCDF meteorological files\n", "4. inspect missing values\n", "5. interpolate the meteorological fields\n", "6. construct the CEQUEAU meteorological grid\n", "7. save the resulting NetCDF file\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import os\n", "import matplotlib.pyplot as plt\n", "\n", "from pycequeau.physiographic import Basin\n", "from pycequeau.meteo import MeteoCalculator, NetCDFMeteo\n", "from pycequeau.core.netcdf import intermidiate_interpolation" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Expected content of the ERA folder\n", "\n", "The `ERA` folder is expected to contain NetCDF files prepared for the basin, usually with one meteorological variable per file. The ERA folder should be located in the following path `path/to/your/project/meteo/ERA`\n", "\n", "The file names themselves do not matter. What matters for `pycequeau` is that each file contains a supported variable name and valid units metadata.\n", "\n", "For the current example project, the files in the folder look like this:\n", "\n", "| File name | Variable name | Units in this example | Other accepted units |\n", "| --- | --- | --- | --- |\n", "| `era5_10m-wind-speed_daily.nc` | `wind` | `m s-1` | `km h-1` |\n", "| `era5_2m-dewpoint-temperature_daily.nc` | `d2m` | `K` | `C` |\n", "| `era5_2m-temperature_daily_max.nc` | `tasmax` | `K` | `C` |\n", "| `era5_2m-temperature_daily_min.nc` | `tasmin` | `K` | `C` |\n", "| `era5_surface-solar-radiation-downwards_daily.nc` | `ssrd` | `J m-2` | `MJ m-2 d-1`
`MJ m-2`
`J m-2 d-1`
`W m-2` |\n", "| `era5_surface-thermal-radiation-downwards_daily.nc` | `strd` | `J m-2` | `MJ m-2 d-1`
`MJ m-2`
`J m-2 d-1`
`W m-2` |\n", "| `era5_total-cloud-cover_daily.nc` | `tcc` | `0-1` | `fraction`
`1`
`%`
`percent` |\n", "| `era5_total-precipitation_daily.nc` | `tp` | `m` | `mm d-1`
`mm`
`m d-1`
`kg m-2 s-1` |\n", "| `saturated_vapor_pressure.nc` | `vp` | `mmHg` | `Pa`
`kPa` |\n", "\n", "Most of these files come from prepared ERA5 products aggregated to the daily time step. In this workflow, wind speed and vapor pressure are obtained with the meteorological calculators before the complete meteorological dataset is loaded.\n", "\n", "In the example below, `saturated_vapor_pressure.nc` is created from `d2m` with `MeteoCalculator.create_variable_file(...)`. The wind-speed file follows the same idea, using the meteorological calculators to derive `wind` from the original wind components. For the calculator API reference, see [the meteorological calculators module](../api/pycequeau.meteo.calculators.rst).\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Project configuration\n", "\n", "Set the project folder, the basin name, and the raster files used to initialize the basin." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "project_folder = r\"path/to/your/project/\"\n", "files_list = [\"DEM.tif\", # DEM tif file\n", " \"FAC.tif\", #Flow accumulation tif file\n", " \"LCF.tif\", # Land conver tif file\n", " \"Watershed.tif\", # Watershed tif file\n", " \"CAT.tif\" #Sub basins tif file\n", " ]\n", "basin_name = \"Margarite\"\n", "\n", "bassin_versant_file = os.path.join(\n", " project_folder,\n", " \"results\",\n", " \"bassinVersant.mat\",\n", ")\n", "meteo_folder = os.path.join(project_folder, \"meteo\", \"ERA\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Create the basin object\n", "\n", "The basin object is required to project and export the meteorological data to the CEQUEAU grid." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "basin = Basin(\n", " project_folder,\n", " basin_name,\n", " files_list,\n", " bassin_versant_file,\n", ")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Load the NetCDF meteorological inputs\n", "\n", "Once the folder is ready, load the NetCDF meteorological files with `NetCDFMeteo.load_from_netcdf`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 11MB\n",
              "Dimensions:      (lat: 41, lon: 21, time: 365)\n",
              "Coordinates:\n",
              "  * lat          (lat) float64 328B 40.0 40.25 40.5 40.75 ... 49.5 49.75 50.0\n",
              "  * lon          (lon) float64 168B -70.0 -69.75 -69.5 ... -65.5 -65.25 -65.0\n",
              "  * time         (time) datetime64[ns] 3kB 2023-01-01 2023-01-02 ... 2023-12-31\n",
              "Data variables:\n",
              "    vitesseVent  (time, lat, lon) float32 1MB 28.55 28.45 28.2 ... 20.39 20.99\n",
              "    d2m          (time, lat, lon) float32 1MB 12.06 12.06 ... -7.709 -7.567\n",
              "    tMax         (time, lat, lon) float32 1MB 16.69 16.64 ... -1.797 -1.594\n",
              "    tMin         (time, lat, lon) float32 1MB 11.38 11.34 11.06 ... -5.42 -5.135\n",
              "    rayonnement  (time, lat, lon) float32 1MB ...\n",
              "    longwaveRad  (time, lat, lon) float32 1MB ...\n",
              "    nebulosite   (time, lat, lon) float32 1MB ...\n",
              "    pTot         (time, lat, lon) float32 1MB ...\n",
              "    pression     (time, lat, lon) float32 1MB ...\n",
              "Attributes:\n",
              "    source_dataset:           era5\n",
              "    source_variables:         10m_u_component_of_wind, 10m_v_component_of_wind\n",
              "    processing_level:         daily\n",
              "    daily_aggregation:        daily_vector_magnitude_from_daily_mean_components\n",
              "    daily_conversion_method:  sfcWind = sqrt(u10^2 + v10^2)
" ], "text/plain": [ " Size: 11MB\n", "Dimensions: (lat: 41, lon: 21, time: 365)\n", "Coordinates:\n", " * lat (lat) float64 328B 40.0 40.25 40.5 40.75 ... 49.5 49.75 50.0\n", " * lon (lon) float64 168B -70.0 -69.75 -69.5 ... -65.5 -65.25 -65.0\n", " * time (time) datetime64[ns] 3kB 2023-01-01 2023-01-02 ... 2023-12-31\n", "Data variables:\n", " vitesseVent (time, lat, lon) float32 1MB 28.55 28.45 28.2 ... 20.39 20.99\n", " d2m (time, lat, lon) float32 1MB 12.06 12.06 ... -7.709 -7.567\n", " tMax (time, lat, lon) float32 1MB 16.69 16.64 ... -1.797 -1.594\n", " tMin (time, lat, lon) float32 1MB 11.38 11.34 11.06 ... -5.42 -5.135\n", " rayonnement (time, lat, lon) float32 1MB ...\n", " longwaveRad (time, lat, lon) float32 1MB ...\n", " nebulosite (time, lat, lon) float32 1MB ...\n", " pTot (time, lat, lon) float32 1MB ...\n", " pression (time, lat, lon) float32 1MB ...\n", "Attributes:\n", " source_dataset: era5\n", " source_variables: 10m_u_component_of_wind, 10m_v_component_of_wind\n", " processing_level: daily\n", " daily_aggregation: daily_vector_magnitude_from_daily_mean_components\n", " daily_conversion_method: sfcWind = sqrt(u10^2 + v10^2)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meteo_stations = NetCDFMeteo.load_from_netcdf(\n", " basin,\n", " meteo_folder,\n", ")\n", "\n", "meteo_stations.ds" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Downscale the meteorological grid when needed\n", "\n", "In some cases, the meteorological grid is so coarse that the basin falls within a single source pixel. In that situation, the interpolation to the CEQUEAU grid cannot be performed in a meaningful way.\n", "\n", "The `intermidiate_interpolation` step can be used to downscale the meteorological input first, so that several source points are available before interpolating to the CEQUEAU grid." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 263MB\n",
              "Dimensions:      (time: 365, lat: 200, lon: 100)\n",
              "Coordinates:\n",
              "  * time         (time) datetime64[ns] 3kB 2023-01-01 2023-01-02 ... 2023-12-31\n",
              "  * lat          (lat) float64 2kB 40.0 40.05 40.1 40.15 ... 49.85 49.9 49.95\n",
              "  * lon          (lon) float64 800B -70.0 -69.95 -69.9 ... -65.15 -65.1 -65.05\n",
              "Data variables:\n",
              "    vitesseVent  (time, lat, lon) float32 29MB 28.55 28.55 28.55 ... 20.99 20.99\n",
              "    d2m          (time, lat, lon) float32 29MB 12.06 12.06 ... -7.567 -7.567\n",
              "    tMax         (time, lat, lon) float32 29MB 16.69 16.69 ... -1.594 -1.594\n",
              "    tMin         (time, lat, lon) float32 29MB 11.38 11.38 ... -5.135 -5.135\n",
              "    rayonnement  (time, lat, lon) float32 29MB 7.048 7.048 7.048 ... 4.117 4.117\n",
              "    longwaveRad  (time, lat, lon) float32 29MB 29.97 29.97 29.97 ... 19.93 19.93\n",
              "    nebulosite   (time, lat, lon) float32 29MB 0.6492 0.6492 ... 0.6049 0.6049\n",
              "    pTot         (time, lat, lon) float32 29MB 17.02 17.02 ... 0.3147 0.3147\n",
              "    pression     (time, lat, lon) float32 29MB 10.56 10.56 10.56 ... 2.412 2.412\n",
              "Attributes:\n",
              "    source_dataset:           era5\n",
              "    source_variables:         10m_u_component_of_wind, 10m_v_component_of_wind\n",
              "    processing_level:         daily\n",
              "    daily_aggregation:        daily_vector_magnitude_from_daily_mean_components\n",
              "    daily_conversion_method:  sfcWind = sqrt(u10^2 + v10^2)
" ], "text/plain": [ " Size: 263MB\n", "Dimensions: (time: 365, lat: 200, lon: 100)\n", "Coordinates:\n", " * time (time) datetime64[ns] 3kB 2023-01-01 2023-01-02 ... 2023-12-31\n", " * lat (lat) float64 2kB 40.0 40.05 40.1 40.15 ... 49.85 49.9 49.95\n", " * lon (lon) float64 800B -70.0 -69.95 -69.9 ... -65.15 -65.1 -65.05\n", "Data variables:\n", " vitesseVent (time, lat, lon) float32 29MB 28.55 28.55 28.55 ... 20.99 20.99\n", " d2m (time, lat, lon) float32 29MB 12.06 12.06 ... -7.567 -7.567\n", " tMax (time, lat, lon) float32 29MB 16.69 16.69 ... -1.594 -1.594\n", " tMin (time, lat, lon) float32 29MB 11.38 11.38 ... -5.135 -5.135\n", " rayonnement (time, lat, lon) float32 29MB 7.048 7.048 7.048 ... 4.117 4.117\n", " longwaveRad (time, lat, lon) float32 29MB 29.97 29.97 29.97 ... 19.93 19.93\n", " nebulosite (time, lat, lon) float32 29MB 0.6492 0.6492 ... 0.6049 0.6049\n", " pTot (time, lat, lon) float32 29MB 17.02 17.02 ... 0.3147 0.3147\n", " pression (time, lat, lon) float32 29MB 10.56 10.56 10.56 ... 2.412 2.412\n", "Attributes:\n", " source_dataset: era5\n", " source_variables: 10m_u_component_of_wind, 10m_v_component_of_wind\n", " processing_level: daily\n", " daily_aggregation: daily_vector_magnitude_from_daily_mean_components\n", " daily_conversion_method: sfcWind = sqrt(u10^2 + v10^2)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meteo_stations.ds = intermidiate_interpolation(meteo_stations.ds, 5)\n", "meteo_stations.ds" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Interpolate the meteorological fields\n", "\n", "This example uses the `nearest` method." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 26MB\n",
              "Dimensions:      (time: 365, j: 40, i: 49)\n",
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              "  * time         (time) datetime64[ns] 3kB 2023-01-01 2023-01-02 ... 2023-12-31\n",
              "  * j            (j) int16 80B 49 48 47 46 45 44 43 42 ... 16 15 14 13 12 11 10\n",
              "  * i            (i) int16 98B 10 11 12 13 14 15 16 17 ... 52 53 54 55 56 57 58\n",
              "Data variables:\n",
              "    vitesseVent  (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    d2m          (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    tMax         (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    tMin         (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    rayonnement  (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    longwaveRad  (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    nebulosite   (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    pTot         (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    pression     (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n",
              "    CE           (j, i) float16 4kB nan nan nan nan nan ... 33.0 nan nan nan nan\n",
              "Attributes:\n",
              "    source_dataset:           era5\n",
              "    source_variables:         10m_u_component_of_wind, 10m_v_component_of_wind\n",
              "    processing_level:         daily\n",
              "    daily_aggregation:        daily_vector_magnitude_from_daily_mean_components\n",
              "    daily_conversion_method:  sfcWind = sqrt(u10^2 + v10^2)\n",
              "    interpolated:             Interpolated using xarray.Dataset.interp with m...
" ], "text/plain": [ " Size: 26MB\n", "Dimensions: (time: 365, j: 40, i: 49)\n", "Coordinates:\n", " * time (time) datetime64[ns] 3kB 2023-01-01 2023-01-02 ... 2023-12-31\n", " * j (j) int16 80B 49 48 47 46 45 44 43 42 ... 16 15 14 13 12 11 10\n", " * i (i) int16 98B 10 11 12 13 14 15 16 17 ... 52 53 54 55 56 57 58\n", "Data variables:\n", " vitesseVent (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " d2m (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " tMax (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " tMin (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " rayonnement (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " longwaveRad (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " nebulosite (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " pTot (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " pression (time, j, i) float32 3MB nan nan nan nan ... nan nan nan nan\n", " CE (j, i) float16 4kB nan nan nan nan nan ... 33.0 nan nan nan nan\n", "Attributes:\n", " source_dataset: era5\n", " source_variables: 10m_u_component_of_wind, 10m_v_component_of_wind\n", " processing_level: daily\n", " daily_aggregation: daily_vector_magnitude_from_daily_mean_components\n", " daily_conversion_method: sfcWind = sqrt(u10^2 + v10^2)\n", " interpolated: Interpolated using xarray.Dataset.interp with m..." ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interpolated = meteo_stations.interpolation(\"nearest\")\n", "interpolated" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Build the CEQUEAU grid\n", "\n", "After interpolation, the fields can be reorganized into the CEQUEAU meteorological grid." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset> Size: 14MB\n",
              "Dimensions:      (CEid: 1052, pasTemp: 365)\n",
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              "  * CEid         (CEid) int32 4kB 1 2 3 4 5 6 ... 1047 1048 1049 1050 1051 1052\n",
              "  * pasTemp      (pasTemp) float32 1kB 7.389e+05 7.389e+05 ... 7.393e+05\n",
              "Data variables:\n",
              "    vitesseVent  (pasTemp, CEid) float32 2MB 8.566 8.566 8.566 ... 13.79 13.79\n",
              "    d2m          (pasTemp, CEid) float32 2MB -0.9576 -0.9576 ... -10.33 -10.33\n",
              "    tMax         (pasTemp, CEid) float32 2MB 1.234 1.234 1.234 ... -6.68 -6.68\n",
              "    tMin         (pasTemp, CEid) float32 2MB -0.9955 -0.9955 ... -11.58 -11.58\n",
              "    rayonnement  (pasTemp, CEid) float32 2MB 2.401 2.401 2.401 ... 4.81 4.81\n",
              "    longwaveRad  (pasTemp, CEid) float32 2MB 25.93 25.93 25.93 ... 18.86 18.86\n",
              "    nebulosite   (pasTemp, CEid) float32 2MB 0.9681 0.9681 ... 0.8501 0.8501\n",
              "    pTot         (pasTemp, CEid) float32 2MB 4.324 4.324 4.324 ... 0.1087 0.1087\n",
              "    pression     (pasTemp, CEid) float32 2MB 4.233 4.233 4.233 ... 1.891 1.891
" ], "text/plain": [ " Size: 14MB\n", "Dimensions: (CEid: 1052, pasTemp: 365)\n", "Coordinates:\n", " * CEid (CEid) int32 4kB 1 2 3 4 5 6 ... 1047 1048 1049 1050 1051 1052\n", " * pasTemp (pasTemp) float32 1kB 7.389e+05 7.389e+05 ... 7.393e+05\n", "Data variables:\n", " vitesseVent (pasTemp, CEid) float32 2MB 8.566 8.566 8.566 ... 13.79 13.79\n", " d2m (pasTemp, CEid) float32 2MB -0.9576 -0.9576 ... -10.33 -10.33\n", " tMax (pasTemp, CEid) float32 2MB 1.234 1.234 1.234 ... -6.68 -6.68\n", " tMin (pasTemp, CEid) float32 2MB -0.9955 -0.9955 ... -11.58 -11.58\n", " rayonnement (pasTemp, CEid) float32 2MB 2.401 2.401 2.401 ... 4.81 4.81\n", " longwaveRad (pasTemp, CEid) float32 2MB 25.93 25.93 25.93 ... 18.86 18.86\n", " nebulosite (pasTemp, CEid) float32 2MB 0.9681 0.9681 ... 0.8501 0.8501\n", " pTot (pasTemp, CEid) float32 2MB 4.324 4.324 4.324 ... 0.1087 0.1087\n", " pression (pasTemp, CEid) float32 2MB 4.233 4.233 4.233 ... 1.891 1.891" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_cequeau = meteo_stations.cequeau_grid(interpolated, basin)\n", "grid_cequeau" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Plot a sample day\n", "\n", "A quick plot can be useful to visually inspect a few interpolated meteorological fields before exporting the CEQUEAU file." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_ds = interpolated.isel(time=0)\n", "\n", "fig, axes = plt.subplots(\n", " ncols=2,\n", " nrows=2,\n", " figsize=(12, 10),\n", ")\n", "\n", "plot_ds[\"tMax\"].plot(ax=axes[0, 0], cmap=\"Spectral\")\n", "plot_ds[\"tMin\"].plot(ax=axes[0, 1], cmap=\"Spectral\")\n", "plot_ds[\"rayonnement\"].plot(ax=axes[1, 0], cmap=\"Spectral\")\n", "plot_ds[\"pTot\"].plot(ax=axes[1, 1], cmap=\"Spectral\")\n", "\n", "axes[0, 0].set_title(\"tMax\")\n", "axes[0, 1].set_title(\"tMin\")\n", "axes[1, 0].set_title(\"rayonnement\")\n", "axes[1, 1].set_title(\"pTot\")\n", "\n", "plt.tight_layout()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Save the final NetCDF file\n", "\n", "The resulting CEQUEAU-ready meteorological dataset can then be written to disk." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "output_file = os.path.join(project_folder, \"meteo\", \"meteo_cequeau.nc\")\n", "grid_cequeau.to_netcdf(output_file)\n", "# output_file" ] } ], "metadata": { "kernelspec": { "display_name": "pycequeau", "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": "3.10.19" }, "nbsphinx": { "execute": "never" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }