diff --git a/.github/workflows/changelog-check.yml b/.github/workflows/changelog-check.yml index 8967963..1e91381 100644 --- a/.github/workflows/changelog-check.yml +++ b/.github/workflows/changelog-check.yml @@ -22,4 +22,4 @@ jobs: else echo "You must update CHANGELOG.md before merging into main." exit 1 - fi \ No newline at end of file + fi diff --git a/.github/workflows/sphinx.yml b/.github/workflows/sphinx.yml index b34c5c4..5b53afe 100644 --- a/.github/workflows/sphinx.yml +++ b/.github/workflows/sphinx.yml @@ -28,11 +28,26 @@ jobs: with: enable-cache: true + - name: Setup cache + uses: actions/cache@v4 + with: + path: | + $TMPDIR/*.pkl + $TMPDIR/*.grib + key: danra-${{ hashFiles('danra-book/scripts/utils.py') }} + + - name: Write CDS API key (needed for notebooks/paper-figures.ipynb) + run: | + echo "url: https://cds.climate.copernicus.eu/api" > ~/.cdsapirc + echo "key: ${{ secrets.CDS_API_KEY}}" >> ~/.cdsapirc + chmod 600 ~/.cdsapirc + - name: Build Jupyter Book - run: uv run jupyter-book build danra-book/ + run: uv run jupyter-book build -W -n --keep-going danra-book/ - name: Upload Jupyter Book as artifact (for debugging) uses: actions/upload-artifact@v4 + if: always() with: name: jupyter-book path: danra-book/_build/html diff --git a/.zenodo.json b/.zenodo.json index 15c6589..73b5e84 100644 --- a/.zenodo.json +++ b/.zenodo.json @@ -47,4 +47,4 @@ "affiliation": "Danish Meteorological Institute" } ] -} \ No newline at end of file +} diff --git a/danra-book/_config.yml b/danra-book/_config.yml index 7e9fbcb..7a72bff 100644 --- a/danra-book/_config.yml +++ b/danra-book/_config.yml @@ -6,12 +6,6 @@ author: DMI NWP Team logo: logo.png copyright: '2025' -# Force re-execution of notebooks on each build. -# See https://jupyterbook.org/content/execute.html -execute: - exclude_patterns: - - 'notebooks/**.ipynb' # exclude all notebooks in the notebooks folder - # Define the name of the latex output file for PDF builds latex: latex_documents: @@ -21,6 +15,11 @@ latex: bibtex_bibfiles: - references.bib +execute: + execute_notebooks: auto + allow_errors: false + timeout: 7200 + # Information about where the book exists on the web repository: url: https://github.com/dmidk/danradocs # Online location of your book diff --git a/danra-book/_toc.yml b/danra-book/_toc.yml index e52837a..44417a3 100644 --- a/danra-book/_toc.yml +++ b/danra-book/_toc.yml @@ -15,8 +15,6 @@ parts: - file: docs/modelspecs.md - file: docs/data-availability.md - file: docs/data-details.md -# - file: docs/annex.md -# - file: docs/references.md - caption: User Examples chapters: diff --git a/danra-book/about.md b/danra-book/about.md index ab0361b..5277e54 100644 --- a/danra-book/about.md +++ b/danra-book/about.md @@ -1,4 +1,4 @@ # About DANRA DANRA is a high-resolution meteorological reanalysis dataset for Denmark and Northwestern Europe developed by the Danish Meteorological Institute (DMI). The dataset covers the period September 1990 to December 2023. It is produced using state of the art numerical models and data assimilation, three-hourly analyses and hourly forecasts on a 2.5 km spatial grid. -The DANRA-Book is a collection of resources for the DANRA project. \ No newline at end of file +The DANRA-Book is a collection of resources for the DANRA project. diff --git a/danra-book/docs/annex.md b/danra-book/docs/annex.md deleted file mode 100644 index 1a7e48d..0000000 --- a/danra-book/docs/annex.md +++ /dev/null @@ -1,5 +0,0 @@ -# Annex - -## Description of DANRA's reanalysis system - -## Principles of reanalysis diff --git a/danra-book/docs/data-availability.md b/danra-book/docs/data-availability.md index c333995..d27a5fe 100644 --- a/danra-book/docs/data-availability.md +++ b/danra-book/docs/data-availability.md @@ -33,9 +33,9 @@ ds_danra_sl Alternatively, the data can be accessed directly from the S3 object store: -- [s3://dmi-danra-05/height_levels.zarr](s3://dmi-danra-05/height_levels.zarr) -- [s3://dmi-danra-05/single_levels.zarr](s3://dmi-danra-05/single_levels.zarr) -- [s3://dmi-danra-05/pressure_levels.zarr](s3://dmi-danra-05/pressure_levels.zarr) +- [s3://dmi-danra-05/height_levels.zarr](https://registry.opendata.aws/dmi-danra-05/) +- [s3://dmi-danra-05/single_levels.zarr](https://registry.opendata.aws/dmi-danra-05/) +- [s3://dmi-danra-05/pressure_levels.zarr](https://registry.opendata.aws/dmi-danra-05/) Fetching one of these datasets with Python and xarray using simple loading is as simple as ``` @@ -49,4 +49,4 @@ ds_danra_sl = xarray.open_zarr( ) ``` -Efforts are currently underway to prepare the DANRA forecasts for public release. Once available, the dataset will provide hourly temporal resolution. Updates and further details will be posted on the website. \ No newline at end of file +Efforts are currently underway to prepare the DANRA forecasts for public release. Once available, the dataset will provide hourly temporal resolution. Updates and further details will be posted on the website. diff --git a/danra-book/docs/data-details.md b/danra-book/docs/data-details.md index af5a785..0b23ab9 100644 --- a/danra-book/docs/data-details.md +++ b/danra-book/docs/data-details.md @@ -1,5 +1,5 @@ # Details about parameters -This section describes special parameters in the DANRA dataset. +This section describes special parameters in the DANRA dataset. ## DANRA Parameters @@ -119,8 +119,8 @@ The icing index, icei0m, is a measure of the risk of icing in the a | 1 | Trace | | 2 | Light | | 3 | Moderate | -| 4 | Severe | +| 4 | Severe |

AROME Hail diagnostic

The hail diagnostic, xhail0m, is a measure of the risk of hail. It is the maximum total column graupel in kg/m^2 since previous post-processing. Values above 16 kg/m^2 are considered to be a risk of hail. - \ No newline at end of file + diff --git a/danra-book/docs/data-formats.md b/danra-book/docs/data-formats.md deleted file mode 100644 index 6ee9e5d..0000000 --- a/danra-book/docs/data-formats.md +++ /dev/null @@ -1,7 +0,0 @@ -# Data formats in DANRA -Data content formats - -## zarr format - - -## grib format diff --git a/danra-book/docs/description.md b/danra-book/docs/description.md index 334b946..5726317 100644 --- a/danra-book/docs/description.md +++ b/danra-book/docs/description.md @@ -4,4 +4,4 @@ DANRA is produced using three-dimensional variational (3D-Var) data assimilation DANRA runs on a horizontal grid mesh of 800 x 600 at 2.5 km grid size, with 65 hybrid sigma/pressure (model) levels in the vertical, with the bottom level at around 12 meters height and the top level at 10 hPa. For data assimilation, quality-controlled local observation data from synoptic stations in Denmark and nearby countries have been added, along with conventional weather observations collected from the Global Telecommunication System (GTS) for synoptic, ship, drift buoy, radiosonde, and aircraft observations, as well as satellite remote sensing data of radiance, atmospheric motion vectors (AMV), scatterometer, and radio occultation. Selected atmospheric data are available on model levels and are also interpolated to 14 pressure levels. "Surface or single level" data are also available, containing 2D parameters such as screen-level properties, accumulated precipitation, top-of-atmosphere radiation, and vertical integrals over the entire depth of the atmosphere. -The DANRA dataset contains high-resolution reanalysis for key meteorological parameters. Analysis data are available at 3-hour frequency, while forecast output will be available at 1-hour frequency (expected November 2025). \ No newline at end of file +The DANRA dataset contains high-resolution reanalysis for key meteorological parameters. Analysis data are available at 3-hour frequency, while forecast output will be available at 1-hour frequency (expected November 2025). diff --git a/danra-book/docs/documentation.md b/danra-book/docs/documentation.md index 2703b0e..4639be8 100644 --- a/danra-book/docs/documentation.md +++ b/danra-book/docs/documentation.md @@ -8,8 +8,4 @@ [Details about parameters](data-details.md) -[Known issues](issues.md) - -[Annex](annex.md) - -[References](references.md) +[Known issues](https://github.com/dmidk/danradocs/issues) diff --git a/danra-book/docs/introduction.md b/danra-book/docs/introduction.md deleted file mode 100644 index 812b43b..0000000 --- a/danra-book/docs/introduction.md +++ /dev/null @@ -1,28 +0,0 @@ -# Introduction to the Danish Reanalysis -The DANRA dataset covers the period from Sept 1990 to Dec 2023. \ -DANRA is produced using 3D-Var data assimilation and model forecasts with the reanalysis system based on the adapted Harmonie-arome CY40h1.1 forecast system ([Bengtsson et al., 2017](https://journals.ametsoc.org/view/journals/mwre/145/5/mwr-d-16-0417.1.xml)). DANRA runs on a horizontal grid-mesh of 800 x 600 at 2.5 km grid size, with 65 hybrid sigma/pressure (model) levels in the vertical and the bottom level at around 12 meter and top level at 10 hPa. For data assimilation, quality controlled local observation data from synoptic stations in Denmark and nearby countries have been added, along with conventional weather observations collected from the Global Telecommunication System (GTS) for synoptic, ship, drift buoy, radiosonde, aircraft observations, as well as satellite remote sensing data on radiance, atmospheric motion vector (AMV), scatterometer, radio occultation, has been assimilated. Selected atmospheric data are available on model levels and they are also interpolated to 14 pressure levels. "Surface or single level" data are also available, containing 2D parameters such as screen level properties, accumulated precipitation, top of atmosphere radiation and vertical integrals over the entire depth of the atmosphere. \ -The DANRA dataset contains high resolution reanalysis for key meteorological parameters. The data are available at a 1h and 3h frequency and consist of analyses and short (18 hour) forecasts, initialised 8 times daily every three hours. Many analysed parameters are also available from the forecasts. However, there are a number of forecast parameters, e.g. fluxes, minima and maxima, and accumulations, that are not available from the analyses.\ -The data are archived in the internal DMI data archive and a pertinent sub-set of the data has been copied to the European Weather Cloud. Where single level and pressure level data are available, analyses are provided rather than forecasts, unless the parameter is only available from the forecasts. - -## Technical details -For a full technical description of the dataset, please refer to the @danratech.\ - -### Domain -![DANRA Domain](danra-book/docs/figures/domain.png)\ -The DANRA domain covers the area bounded in a box spanning from roughly 47.5°N, 3.0°W to 65.5°N, 25.0°E (as seen in the figure above) on a Lambert Conformal grid centered around 56.7°N, 8.2°E.\ -The horizontal resolution is 2.5 km and the vertical resolution is 65 hybrid sigma/pressure levels. The complete domain spans 800x600 horizontal grid points, including an extension zone covering 11 grid points. Thus, output is available in 789x589 horizontal grid points. - -### Reanalysis system -DANRA uses hourly global ERA5 reanalysis as lateral boundary conditions. In contrast to time-critical operational systems that use forecast lateral boundaries, DANRA uses analysis boundary conditions, providing optimal boundary conditions of the regional reanalysis. \ -The operational production has been carried out over 9 streams of each 4 years except for the final, near-real time, each consisting of a full year spin-up. -| Stream | Reanalysis period | -|-----------|-------------| -| 1989 | 1990.09.01 - 1994.08.30 | -| 1993 | 1994.09.01 - 1998.08.30 | -| 1997 | 1998.09.01 - 2002.08.30 | -| 2001 | 2002.09.01 - 2006.08.30 | -| 2005 | 2006.09.01 - 2010.08.30 | -| 2009 | 2010.09.01 - 2014.08.30 | -| 2013 | 2014.09.01 - 2017.08.30 | -| 2016 | 2017.09.01 - 2019.11.30 | -| 2018 | 2019.12.01 - 2023.12.31 | \ No newline at end of file diff --git a/danra-book/docs/modelspecs.md b/danra-book/docs/modelspecs.md index 14ca667..4e863d3 100644 --- a/danra-book/docs/modelspecs.md +++ b/danra-book/docs/modelspecs.md @@ -11,7 +11,7 @@ The boundary conditions for the DANRA reanalysis are derived from the ECMWF Glob The DANRA dataset uses a Lambert Conic Conformal projection with the following specifications: ### CRS WKT -```wkt +``` PROJCRS["DMI HARMONIE DANRA lambert projection", BASEGEOGCRS["DMI HARMONIE DANRA lambert CRS", DATUM["DMI HARMONIE DANRA lambert datum", @@ -74,4 +74,4 @@ DANRA has produced in several production streams. Each stream has an associated | 2009 | 2010.09.01 - 2014.08.30 | | 2013 | 2014.09.01 - 2017.08.30 | | 2016 | 2017.09.01 - 2019.11.30 | -| 2018 | 2019.12.01 - 2023.12.31 | \ No newline at end of file +| 2018 | 2019.12.01 - 2023.12.31 | diff --git a/danra-book/docs/parameters.md b/danra-book/docs/parameters.md deleted file mode 100644 index 1c8244e..0000000 --- a/danra-book/docs/parameters.md +++ /dev/null @@ -1,90 +0,0 @@ -## DANRA Parameters - -DANRA output is ordered into three main categories based on the vertical levels of the data: surface or single level (SL), height levels (HL), and pressure levels (PL). Each category contains various meteorological parameters, which are described in detail below. - -
-Single Levels - -### Single level parameters - -Single level refers to parameters that are defined at a single vertical level in the atmosphere or at the surface. -| Variable Name | CF Standard Name | Description | Unit | -|---------------|------------------|-------------|------| -| cape_column | atmosphere_convective_available_potential_energy_wrt_surface | CAPE out of the model | J kg-1 | -| cb_column | cloud_base_altitude | Cloud base | m | -| ct_column | cloud_top_altitude | Cloud top | m | -| danra_projection | | | | -| grpl_column | atmosphere_mass_content_of_graupel | Graupel | kg m-2 | -| hcc0m | high_type_cloud_area_fraction | High cloud cover | 1 | -| icei0m | | Icing index | - | -| lcc0m | low_type_cloud_area_fraction | Low cloud cover | 1 | -| lsm | land_binary_mask | Land cover (1=land, 0=sea) | 1 | -| lwavr0m | | Long-wave radiation flux | W m-2 | -| mcc0m | medium_type_cloud_area_fraction | Medium cloud cover | 1 | -| mld0m | atmosphere_boundary_layer_thickness | Mixed layer depth | m | -| orography | | Geopotential | m2 s-2 | -| pres0m | air_pressure | Pressure | Pa | -| pres_seasurface | air_pressure_at_mean_sea_level | Pressure | Pa | -| prtp0m | | Precipitation Type | - | -| psct0m | brightness_temperature_at_cloud_top | Pseudo satellite image: cloud top temperature (infrared) | K | -| pscw0m | | Pseudo satellite image: cloud water reflectivity (visible) | - | -| pstb0m | | Pseudo satellite image: water vapour Tb | - | -| pstbc0m | | Pseudo satellite image: water vapour Tb + correction for clouds | - | -| pwat_column | atmosphere_mass_content_of_water_vapor | Precipitable water | kg m-2 | -| r2m | relative_humidity | Relative humidity | % | -| sf0m | snowfall_amount | Water equivalent of accumulated snow depth | kg m-2 | -| swavr0m | | Short-wave radiation flux | W m-2 | -| t0m | air_temperature | Temperature | K | -| t2m | air_temperature | Temperature | K | -| tcc0m | cloud_area_fraction | Total cloud cover | 1 | -| u10m | x_wind | u-component of wind | m s-1 | -| v10m | y_wind | v-component of wind | m s-1 | -| vis0m | visibility_in_air | Visibility | m | -| xhail0m | | AROME hail diagnostic | kg m-2 | -
- -
-Height Levels - -### Height level parameters - -Height level refers to parameters that are defined at specific vertical levels in the atmosphere. -#### Available height levels (meters) - -30, 50, 75, 100, 150, 200, 250, 300, 500 - -#### Available parameters - -| Variable Name | CF Standard Name | Description | Unit | -|---------------|------------------|-------------|------| -| danra_projection | | | | -| r | relative_humidity | Relative humidity | % | -| t | air_temperature | Temperature | K | -| u | x_wind | u-component of wind | m s-1 | -| v | y_wind | v-component of wind | m s-1 | -
- -
-Pressure Levels - -### Pressure level parameters - -Pressure level refers to parameters that are defined at specific pressure levels in the atmosphere. -#### Available pressure levels (hPa) - -1000, 950, 925, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 100 - -#### Available parameters - -| Variable Name | CF Standard Name | Description | Unit | -|---------------|------------------|-------------|------| -| ciwc | atmosphere_mass_content_of_cloud_ice | Cloud ice | kg m-2 | -| cwat | atmosphere_mass_content_of_cloud_liquid_water | Cloud water | kg m-2 | -| danra_projection | | | | -| r | relative_humidity | Relative humidity | % | -| t | air_temperature | Temperature | K | -| tw | upward_air_velocity | Vertical velocity | m s-1 | -| u | x_wind | u-component of wind | m s-1 | -| v | y_wind | v-component of wind | m s-1 | -| z | geopotential | Geopotential | m2 s-2 | -
diff --git a/danra-book/docs/references.md b/danra-book/docs/references.md deleted file mode 100644 index 0174803..0000000 --- a/danra-book/docs/references.md +++ /dev/null @@ -1,3 +0,0 @@ -# References - -References and related articles (to be updated as new papers appear) diff --git a/danra-book/docs/summary.md b/danra-book/docs/summary.md deleted file mode 100644 index a28319a..0000000 --- a/danra-book/docs/summary.md +++ /dev/null @@ -1,2 +0,0 @@ -# Summary -DANRA is a high-resolution meteorological reanalysis dataset for Denmark and Northwestern Europe, covering the period September 1990 to December 2023. It is produced using state of the art numerical models and data assimilation, three-hourly analyses and hourly forecasts on a 2.5 km spatial grid. \ No newline at end of file diff --git a/danra-book/notebooks/distributions.ipynb b/danra-book/notebooks/distributions.ipynb index c4900b5..1d840dd 100644 --- a/danra-book/notebooks/distributions.ipynb +++ b/danra-book/notebooks/distributions.ipynb @@ -17,9 +17,14 @@ "metadata": {}, "outputs": [], "source": [ + "import sys\n", + "\n", "import xarray as xr\n", "import matplotlib.pyplot as plt\n", - "import numpy as np" + "import numpy as np\n", + "\n", + "sys.path.append(\"../scripts\")\n", + "from utils import sel_nearest_to_latlon_pt # noqa: E402" ] }, { @@ -34,9 +39,4152 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 10TB\n",
+       "Dimensions:           (time: 96768, y: 589, x: 789)\n",
+       "Coordinates:\n",
+       "  * time              (time) datetime64[ns] 774kB 1990-09-01 ... 2023-10-13T2...\n",
+       "  * y                 (y) float64 5kB -6.095e+05 -6.07e+05 ... 8.605e+05\n",
+       "  * x                 (x) float64 6kB -1.999e+06 -1.997e+06 ... -2.925e+04\n",
+       "    lat               (y, x) float64 4MB dask.array<chunksize=(295, 263), meta=np.ndarray>\n",
+       "    lon               (y, x) float64 4MB dask.array<chunksize=(295, 263), meta=np.ndarray>\n",
+       "Data variables: (12/31)\n",
+       "    cape_column       (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    cb_column         (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    ct_column         (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    danra_projection  float64 8B ...\n",
+       "    grpl_column       (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    hcc0m             (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    ...                ...\n",
+       "    t2m               (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    tcc0m             (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    u10m              (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    v10m              (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    vis0m             (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    xhail0m           (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.8\n",
+       "    contact:      Leif Denby <lcd@dmi.dk>, Danish Meteorological Institute\n",
+       "    description:  All prognostic variables for 1990-09-01T00:00Z to 2024-01-0...\n",
+       "    license:      CC-BY-4.0: https://creativecommons.org/licenses/by/4.0/\n",
+       "    suite_name:   danra
" + ], + "text/plain": [ + " Size: 10TB\n", + "Dimensions: (time: 96768, y: 589, x: 789)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 774kB 1990-09-01 ... 2023-10-13T2...\n", + " * y (y) float64 5kB -6.095e+05 -6.07e+05 ... 8.605e+05\n", + " * x (x) float64 6kB -1.999e+06 -1.997e+06 ... -2.925e+04\n", + " lat (y, x) float64 4MB dask.array\n", + " lon (y, x) float64 4MB dask.array\n", + "Data variables: (12/31)\n", + " cape_column (time, y, x) float64 360GB dask.array\n", + " cb_column (time, y, x) float64 360GB dask.array\n", + " ct_column (time, y, x) float64 360GB dask.array\n", + " danra_projection float64 8B ...\n", + " grpl_column (time, y, x) float64 360GB dask.array\n", + " hcc0m (time, y, x) float64 360GB dask.array\n", + " ... ...\n", + " t2m (time, y, x) float64 360GB dask.array\n", + " tcc0m (time, y, x) float64 360GB dask.array\n", + " u10m (time, y, x) float64 360GB dask.array\n", + " v10m (time, y, x) float64 360GB dask.array\n", + " vis0m (time, y, x) float64 360GB dask.array\n", + " xhail0m (time, y, x) float64 360GB dask.array\n", + "Attributes:\n", + " Conventions: CF-1.8\n", + " contact: Leif Denby , Danish Meteorological Institute\n", + " description: All prognostic variables for 1990-09-01T00:00Z to 2024-01-0...\n", + " license: CC-BY-4.0: https://creativecommons.org/licenses/by/4.0/\n", + " suite_name: danra" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ds_danra_sl = xr.open_zarr(\n", " \"s3://dmi-danra-05/single_levels.zarr\",\n", @@ -46,9 +4194,8 @@ " },\n", ")\n", "\n", - "ds_danra_sl.attrs['suite_name'] = \"danra\"\n", - "ds_danra_sl\n", - "\n" + "ds_danra_sl.attrs[\"suite_name\"] = \"danra\"\n", + "ds_danra_sl" ] }, { @@ -66,7 +4213,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -87,38 +4234,86 @@ "\n", "\n", "\n", - "
<xarray.Dataset> Size: 9TB\n",
-       "Dimensions:          (time: 87360, y: 589, x: 789)\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "
<xarray.Dataset> Size: 10TB\n",
+       "Dimensions:           (time: 96768, y: 589, x: 789)\n",
        "Coordinates:\n",
-       "    lat              (y, x) float64 4MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
-       "    lon              (y, x) float64 4MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
-       "  * time             (time) datetime64[ns] 699kB 1990-09-01 ... 2020-07-24T21...\n",
-       "  * x                (x) float64 6kB -1.999e+06 -1.997e+06 ... -2.925e+04\n",
-       "  * y                (y) float64 5kB -6.095e+05 -6.07e+05 ... 8.58e+05 8.605e+05\n",
-       "Data variables: (12/28)\n",
-       "    cape_column      (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    cb_column        (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    ct_column        (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    grpl_column      (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    hcc0m            (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    icei0m           (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    ...               ...\n",
-       "    t2m              (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    u10m             (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    v10m             (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    vis0m            (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    xhail0m          (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
-       "    wind_speed       (time, y, x) float64 325GB dask.array<chunksize=(256, 256, 256), meta=np.ndarray>\n",
+       "  * time              (time) datetime64[ns] 774kB 1990-09-01 ... 2023-10-13T2...\n",
+       "  * y                 (y) float64 5kB -6.095e+05 -6.07e+05 ... 8.605e+05\n",
+       "  * x                 (x) float64 6kB -1.999e+06 -1.997e+06 ... -2.925e+04\n",
+       "    lat               (y, x) float64 4MB dask.array<chunksize=(295, 263), meta=np.ndarray>\n",
+       "    lon               (y, x) float64 4MB dask.array<chunksize=(295, 263), meta=np.ndarray>\n",
+       "Data variables: (12/32)\n",
+       "    cape_column       (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    cb_column         (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    ct_column         (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    danra_projection  float64 8B ...\n",
+       "    grpl_column       (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    hcc0m             (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    ...                ...\n",
+       "    tcc0m             (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    u10m              (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    v10m              (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    vis0m             (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    xhail0m           (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
+       "    wind_speed        (time, y, x) float64 360GB dask.array<chunksize=(256, 295, 263), meta=np.ndarray>\n",
        "Attributes:\n",
-       "    description:  All prognostic variables for 30-year period on reduced levels\n",
-       "    suite_name:   danra