diff --git a/notebooks/2020_temp_precip_sta.nc b/notebooks/2020_temp_precip_sta.nc new file mode 100644 index 0000000..4d21c70 Binary files /dev/null and b/notebooks/2020_temp_precip_sta.nc differ diff --git a/notebooks/merge_climate_datasets_exercise.ipynb b/notebooks/merge_climate_datasets_exercise.ipynb index 9be1adf..bed0607 100644 --- a/notebooks/merge_climate_datasets_exercise.ipynb +++ b/notebooks/merge_climate_datasets_exercise.ipynb @@ -1,254 +1,3670 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "aa24b099", - "metadata": {}, - "source": [ - "# Merging Climate Datasets Exercise\n", - "\n", - "Work through this notebook to practice harmonizing and merging two climate datasets that differ in temporal cadence and spatial resolution.\n", - "\n", - "You will: \n", - "- Load two public NOAA datasets directly from the cloud\n", - "- Subset to the continental US (use 230°E–300°E in longitude since the data span 0–360°)\n", - "- Use `xr.resample` to aggregate time and `xr.interp` to match grids\n", - "- Combine the variables with `xr.merge` for joint analysis\n", - "\n", - "Refer back to the answer key after attempting each step.\n" - ] - }, - { - "cell_type": "markdown", - "id": "d6f677f5", - "metadata": {}, - "source": [ - "## 1. Setup\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0a656265", - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import xarray as xr\n", - "\n", - "try:\n", - " import cartopy.crs as ccrs\n", - " import cartopy.feature as cfeature\n", - "except ImportError:\n", - " ccrs = None\n", - " cfeature = None\n", - "\n", - "TEMP_URL = \"https://psl.noaa.gov/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2020.nc\"\n", - "PRECIP_URL = \"https://psl.noaa.gov/thredds/dodsC/Datasets/cpc_global_precip/precip.2020.nc\"\n", - "\n", - "LAT_RANGE = (20, 50) # degrees North\n", - "LON_RANGE_360 = (230, 300) # degrees East (equivalent to -130° to -60°)\n", - "LON_RANGE_180 = (-130, -60) # convenience if a dataset uses -180° to 180°\n", - "\n", - "TIME_RANGE = slice(\"2020-06-01\", \"2020-06-30\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "45f8536b", - "metadata": {}, - "source": [ - "## 2. Load the datasets\n", - "\n", - "Open both remote datasets with `xr.open_dataset`, passing a reasonable chunk size for the time dimension. Assign the resulting objects to `air` and `precip`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3270985f", - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# TODO: load the air temperature and precipitation datasets.\n", - "# Example: air = xr.open_dataset(..., chunks={\"time\": 8})\n", - "raise NotImplementedError(\"Assign datasets to `air` and `precip`.\")\n" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "aa24b099", + "metadata": {}, + "source": [ + "# Merging Climate Datasets Exercise\n", + "\n", + "Work through this notebook to practice harmonizing and merging two climate datasets that differ in temporal cadence and spatial resolution.\n", + "\n", + "You will: \n", + "- Load two public NOAA datasets directly from the cloud\n", + "- Subset to the continental US (use 230°E–300°E in longitude since the data span 0–360°)\n", + "- Use `xr.resample` to aggregate time and `xr.interp` to match grids\n", + "- Combine the variables with `xr.merge` for joint analysis\n", + "\n", + "Refer back to the answer key after attempting each step.\n" + ] + }, + { + "cell_type": "markdown", + "id": "d6f677f5", + "metadata": {}, + "source": [ + "## 1. Setup\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0a656265", + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import xarray as xr\n", + "\n", + "try:\n", + " import cartopy.crs as ccrs\n", + " import cartopy.feature as cfeature\n", + "except ImportError:\n", + " ccrs = None\n", + " cfeature = None\n", + "\n", + "TEMP_URL = \"https://psl.noaa.gov/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995.2020.nc\"\n", + "PRECIP_URL = \"https://psl.noaa.gov/thredds/dodsC/Datasets/cpc_global_precip/precip.2020.nc\"\n", + "\n", + "LAT_RANGE = slice(50, 20) # degrees North\n", + "LON_RANGE_360 = slice(230, 300) # degrees East (equivalent to -130° to -60°)\n", + "LON_RANGE_180 = (-130, -60) # convenience if a dataset uses -180° to 180°\n", + "\n", + "TIME_RANGE = slice(\"2020-06-01\", \"2020-06-30\")" + ] + }, + { + "cell_type": "markdown", + "id": "45f8536b", + "metadata": {}, + "source": [ + "## 2. Load the datasets\n", + "\n", + "Open both remote datasets with `xr.open_dataset`, passing a reasonable chunk size for the time dimension. Assign the resulting objects to `air` and `precip`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3270985f", + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# TODO: load the air temperature and precipitation datasets.\n", + "# Example: air = xr.open_dataset(..., chunks={\"time\": 8})\n", + "air = xr.open_dataset(TEMP_URL, chunks={\"time\": 8}, mask_and_scale=True, decode_cf=True)\n", + "precip = xr.open_dataset(PRECIP_URL, chunks={\"time\": 8}, mask_and_scale=True, decode_cf=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3f7cec98-7710-4370-bb1b-9ea078b08693", + "metadata": {}, + "outputs": [], + "source": [ + "precip = precip.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "761ec85f", + "metadata": {}, + "source": [ + "## 3. Subset to the continental United States and June 2020\n", + "\n", + "Select the bounding box provided above and limit the time range to June 2020 for both datasets. Store the results in `air_us` and `precip_us`.\n", + "Remember that longitude runs from 0° to 360°, so select 230°E–300°E. Check whether each coordinate is ascending or descending before building the slice.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "264d9641", + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [], + "source": [ + "# TODO: subset both datasets using `sel`, handling coordinate ordering as needed.\n", + "air_us = air.sel(lat=LAT_RANGE, lon=LON_RANGE_360, time=TIME_RANGE)\n", + "precip_us = precip.sel(lat=LAT_RANGE, lon=LON_RANGE_360, time=TIME_RANGE)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5f58dea7-4dd9-411a-acc9-4ed5479631b4", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "761ec85f", - "metadata": {}, - "source": [ - "## 3. Subset to the continental United States and June 2020\n", - "\n", - "Select the bounding box provided above and limit the time range to June 2020 for both datasets. Store the results in `air_us` and `precip_us`.\n", - "Remember that longitude runs from 0° to 360°, so select 230°E–300°E. Check whether each coordinate is ascending or descending before building the slice.\n" + "data": { + "text/html": [ + "
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+       "Attributes:\n",
+       "    Conventions:                     COARDS\n",
+       "    title:                           4x daily NMC reanalysis (2014)\n",
+       "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+       "    description:                     Data is from NMC initialized reanalysis\\...\n",
+       "    platform:                        Model\n",
+       "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+       "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
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+       "    version:                         V1.0\n",
+       "    title:                           CPC GLOBAL PRCP V1.0 RT\n",
+       "    dataset_title:                   CPC GLOBAL PRCP V1.0\n",
+       "    Source:                          ftp://ftp.cpc.ncep.noaa.gov/precip/CPC_U...\n",
+       "    References:                      https://www.psl.noaa.gov/data/gridded/da...\n",
+       "    history:                         Updated 2021-01-02 23:31:03\n",
+       "    DODS_EXTRA.Unlimited_Dimension:  time
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+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 12MB dask.array<chunksize=(61, 20, 140), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:                     COARDS\n",
+       "    title:                           4x daily NMC reanalysis (2014)\n",
+       "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+       "    description:                     Data is from NMC initialized reanalysis\\...\n",
+       "    platform:                        Model\n",
+       "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+       "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+       "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+       "    DODS_EXTRA.Unlimited_Dimension:  time
" + ], + "text/plain": [ + " Size: 12MB\n", + "Dimensions: (time: 366, lat: 60, lon: 140)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 3kB 2020-01-01 2020-01-02 ... 2020-12-31\n", + " * lat (lat) float32 240B 49.75 49.25 48.75 48.25 ... 21.25 20.75 20.25\n", + " * lon (lon) float32 560B 230.2 230.8 231.2 231.8 ... 298.8 299.2 299.8\n", + "Data variables:\n", + " air (time, lat, lon) float32 12MB dask.array\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (2014)\n", + " history: created 2017/12 by Hoop (netCDF2.3)\n", + " description: Data is from NMC initialized reanalysis\\...\n", + " platform: Model\n", + " dataset_title: NCEP-NCAR Reanalysis 1\n", + " _NCProperties: version=2,netcdf=4.6.3,hdf5=1.10.5\n", + " References: http://www.psl.noaa.gov/data/gridded/dat...\n", + " DODS_EXTRA.Unlimited_Dimension: time" ] - }, + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: interpolate the resampled temperature field onto the precipitation grid.\n", + "air_interp = air_daily.interp(lat=precip_us.lat, lon=precip_us.lon)\n", + "air_interp" + ] + }, + { + "cell_type": "markdown", + "id": "7e1bcf4b", + "metadata": {}, + "source": [ + "## 6. Merge the datasets\n", + "\n", + "Convert the aligned arrays into datasets with clear variable names and merge them with `xr.merge`. Save the output as `merged`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b766aff5", + "metadata": { + "tags": [ + "exercise" + ] + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "96529bc0", - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# TODO: use xr.resample to create daily means.\n", - "raise NotImplementedError(\"Create `air_daily`.\")\n" + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 2MB\n",
+       "Dimensions:          (time: 30, lat: 60, lon: 140)\n",
+       "Coordinates:\n",
+       "  * time             (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n",
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+       "Data variables:\n",
+       "    air_temperature  (time, lat, lon) float32 1MB dask.array<chunksize=(30, 20, 140), meta=np.ndarray>\n",
+       "    daily_precip     (time, lat, lon) float32 1MB nan nan nan ... nan nan nan\n",
+       "Attributes:\n",
+       "    Conventions:                     COARDS\n",
+       "    title:                           4x daily NMC reanalysis (2014)\n",
+       "    history:                         created 2017/12 by Hoop (netCDF2.3)\n",
+       "    description:                     Data is from NMC initialized reanalysis\\...\n",
+       "    platform:                        Model\n",
+       "    dataset_title:                   NCEP-NCAR Reanalysis 1\n",
+       "    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5\n",
+       "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
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" + ], + "text/plain": [ + " Size: 2MB\n", + "Dimensions: (time: 30, lat: 60, lon: 140)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n", + " * lat (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n", + " * lon (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n", + "Data variables:\n", + " air_temperature (time, lat, lon) float32 1MB dask.array\n", + " daily_precip (time, lat, lon) float32 1MB nan nan nan ... nan nan nan\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (2014)\n", + " history: created 2017/12 by Hoop (netCDF2.3)\n", + " description: Data is from NMC initialized reanalysis\\...\n", + " platform: Model\n", + " dataset_title: NCEP-NCAR Reanalysis 1\n", + " _NCProperties: version=2,netcdf=4.6.3,hdf5=1.10.5\n", + " References: http://www.psl.noaa.gov/data/gridded/dat...\n", + " DODS_EXTRA.Unlimited_Dimension: time" ] - }, + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged = xr.merge((\n", + " air_interp.rename_vars({\"air\": \"air_temperature\"}), \n", + " precip_us.rename_vars({\"precip\": \"daily_precip\"})),\n", + " join=\"inner\",\n", + " compat=\"override\"\n", + ")\n", + "merged" + ] + }, + { + "cell_type": "markdown", + "id": "fa374697", + "metadata": {}, + "source": [ + "## 7. Inspect your result\n", + "\n", + "Once your pipeline runs without `NotImplementedError`, evaluate the following cell to sanity-check the merged dataset.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "70410d84", + "metadata": { + "tags": [ + "validation" + ] + }, + "outputs": [ { - "cell_type": "markdown", - "id": "b4bc3f03", - "metadata": {}, - "source": [ - "## 5. Interpolate to the precipitation grid\n", - "\n", - "Use `xr.interp` to interpolate the daily air temperatures onto the precipitation grid (`precip_us.lat` and `precip_us.lon`). Store the interpolated result in `air_interp`.\n" + "data": { + "text/html": [ + "
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+       "    Conventions:                     COARDS\n",
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+       "    References:                      http://www.psl.noaa.gov/data/gridded/dat...\n",
+       "    DODS_EXTRA.Unlimited_Dimension:  time
" + ], + "text/plain": [ + " Size: 2MB\n", + "Dimensions: (time: 30, lat: 60, lon: 140)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 240B 2020-06-01 ... 2020-06-30\n", + " * lat (lat) float32 240B 49.75 49.25 48.75 ... 21.25 20.75 20.25\n", + " * lon (lon) float32 560B 230.2 230.8 231.2 ... 298.8 299.2 299.8\n", + "Data variables:\n", + " air_temperature (time, lat, lon) float32 1MB dask.array\n", + " daily_precip (time, lat, lon) float32 1MB nan nan nan ... nan nan nan\n", + "Attributes:\n", + " Conventions: COARDS\n", + " title: 4x daily NMC reanalysis (2014)\n", + " history: created 2017/12 by Hoop (netCDF2.3)\n", + " description: Data is from NMC initialized reanalysis\\...\n", + " platform: Model\n", + " dataset_title: NCEP-NCAR Reanalysis 1\n", + " _NCProperties: version=2,netcdf=4.6.3,hdf5=1.10.5\n", + " References: http://www.psl.noaa.gov/data/gridded/dat...\n", + " DODS_EXTRA.Unlimited_Dimension: time" ] - }, + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The assertions below should pass once you have completed the exercise.\n", + "assert set(merged.data_vars) == {\"air_temperature\", \"daily_precip\"}\n", + "assert merged.air_temperature.dims == merged.daily_precip.dims\n", + "merged" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "62816954-ba0c-437b-a064-43f66bb94dbc", + "metadata": {}, + "outputs": [], + "source": [ + "merged_c = merged.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "9b789f53", + "metadata": {}, + "source": [ + "## 8. Check In\n", + "\n", + "- Render both variables at the first timestep on a `cartopy` map to verify alignment visually (PlateCarree works well).\n", + "- Build a scatter plot comparing colocated temperature and precipitation values across the merged domain.\n", + "- Save the merged output with `to_netcdf` for future analysis.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "acebf62d-68ec-4588-95a0-0ea79b7d7bc5", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the data at the first timestamp\n", + "first_t = merged_c.time.min()\n", + "data_t1 = merged_c.sel(time=first_t)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "1130acb8-de46-4c9a-9eec-8bcd7fa25ba9", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "52eb7321", - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# TODO: interpolate the resampled temperature field onto the precipitation grid.\n", - "raise NotImplementedError(\"Create `air_interp`.\")\n" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "id": "7e1bcf4b", - "metadata": {}, - "source": [ - "## 6. Merge the datasets\n", - "\n", - "Convert the aligned arrays into datasets with clear variable names and merge them with `xr.merge`. Save the output as `merged`.\n" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the first timestamp air temperature data.\n", + "fig, axis = plt.subplots(1, 1,\n", + " subplot_kw=dict(projection=ccrs.Orthographic(-90, 30))\n", + " )\n", + "\n", + "data_t1.air_temperature.plot(\n", + " ax=axis,\n", + " transform=ccrs.PlateCarree(),\n", + " robust=True\n", + ")\n", + "axis.coastlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "573b378f-f9dc-405a-ae1c-6a5eca5e3912", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "b766aff5", - "metadata": { - "tags": [ - "exercise" - ] - }, - "outputs": [], - "source": [ - "# TODO: build datasets and merge them into one object named `merged`.\n", - "raise NotImplementedError(\"Create `merged`.\")\n" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "id": "fa374697", - "metadata": {}, - "source": [ - "## 7. Inspect your result\n", - "\n", - "Once your pipeline runs without `NotImplementedError`, evaluate the following cell to sanity-check the merged dataset.\n" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] - }, + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the precipitation data at the first timestamp\n", + "fig, axis = plt.subplots(1, 1,\n", + " subplot_kw=dict(projection=ccrs.Orthographic(-90, 30))\n", + " )\n", + "data_t1.daily_precip.plot(\n", + " ax=axis,\n", + " transform=ccrs.PlateCarree(),\n", + " robust=True\n", + ")\n", + "axis.coastlines()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "73bed813-c963-4a5f-8bd4-645a3726a4b0", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "70410d84", - "metadata": { - "tags": [ - "validation" - ] - }, - "outputs": [], - "source": [ - "# The assertions below should pass once you have completed the exercise.\n", - "assert set(merged.data_vars) == {\"air_temperature\", \"daily_precip\"}\n", - "assert merged.air_temperature.dims == merged.daily_precip.dims\n", - "print(merged)\n" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "id": "9b789f53", - "metadata": {}, - "source": [ - "## 8. Check In\n", - "\n", - "- Render both variables at the first timestep on a `cartopy` map to verify alignment visually (PlateCarree works well).\n", - "- Build a scatter plot comparing colocated temperature and precipitation values across the merged domain.\n", - "- Save the merged output with `to_netcdf` for future analysis.\n" + "data": { + "image/png": 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", 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" ] - }, - { - "cell_type": "markdown", - "id": "d1c320a1", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "xarray-climate", - "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": "3.13.7" + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# plot a scatter plot of the variables.\n", + "plt.ylabel(\"Daily Total Precipitation (mm)\")\n", + "plt.xlabel(\"Daily Air Temperature (°C)\")\n", + "plt.scatter(merged_c.air_temperature.values.flatten()-273, \n", + " merged_c.daily_precip.values.flatten()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "81b2425e-dd32-418f-b156-0e98adade368", + "metadata": {}, + "outputs": [], + "source": [ + "merged_c.attrs = {}\n", + "merged_c.to_netcdf(\"2020_temp_precip_sta.nc\", mode=\"w\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 }