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--- |
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license: other |
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language: |
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- en |
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- sv |
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tags: |
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- climate |
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pretty_name: Low-to-High Resolution Weather Forecasting using Topography |
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--- |
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# Dataset: Low-to-High-Resolution Weather Forecasting using Topography |
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The dataset is intended and structured for the problem of transforming/interpolating low-resolution weather forecasts into higher resolution using topography data. |
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The dataset consists of 3 different types of data (as illustrated above): |
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- **Historical weather observation data ([SMHI](https://opendata.smhi.se/apidocs/metobs/index.html))** |
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- Historical weather observation data from selected SMHI observation stations (evaluation points) |
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- **Historical low-resolution weather forecasts ([ECMWF](https://www.ecmwf.int/en/forecasts/datasets/set-i))** |
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- For a given SMHI station: Historical, (relatively) low-resolution ECMWF weather forecasts from the 4 nearest ECMWF grid points |
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- **Topography/elevation data ([Copernicus DEM GLO-30](https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model))**: |
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- Topography/elevation data around a given SMHI station, grid enclosed by the 4 ECMWF points |
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- Mean elevation around each of the nearest 4 ECMWF grid points for a given SMHI station (mean over +/-0.05 arc degree in each direction from the grid point center) |
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The dataset is meant to facilitate the following modeling pipeline: |
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- Weather forecasts for a set of 4 neighboring ECMWF points are combined with topography/elevation data and turned into higher resolution forecasts grid (corresponding to the resolution of the topography data) |
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- SMHI weather observation data is used as a sparse evaluation point of the produced higher-resolution forecasts |
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## License and terms |
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The terms of usage of the source data and their corresponding licenses: |
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- SMHI weather observation data: (Creative Common Attribution 4.0) |
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- [SMHI terms and conditions](https://www.smhi.se/data/oppna-data/information-om-oppna-data/villkor-for-anvandning-1.30622) |
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- [SMHI: Creative Commons Attribution 4.0](https://www.smhi.se/polopoly_fs/1.93273!/Menu/general/extGroup/attachmentColHold/mainCol1/file/CreativeCommons-Erk%C3%A4nnande-4.0.pdf) |
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- [SMHI: terms](https://www.smhi.se/polopoly_fs/1.164900!/Villkor%20f%C3%B6r%20konsekvensbaserade%20v%C3%A4dervarningar%20och%20meddelanden%20%28G%C3%A4ller%20fr%C3%A5n%20oktober%202021%29.pdf) |
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- ECMWF historical weather forecasts: |
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- [ECMWF License](https://www.ecmwf.int/sites/default/files/ECMWF_Standard_Licence.pdf) |
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- Copernicus DEM GLO-30: |
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- [Copernicus: Data access license](https://spacedata.copernicus.eu/documents/20123/121286/CSCDA_ESA_User_Licence_latest.pdf/) |
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- [Licence for the use of the Copernicus WorldDEM-30](./topography/eula_F.pdf) |
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## Acknowledgment |
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The dataset has been developed as part of the [OWGRE project](https://www.smhi.se/en/research/research-departments/meteorology/owgre-1.197929), funded within the ERA-Net SES Joint Call 2020 for transnational research, development and demonstration projects. |
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## Data details & samples |
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### SMHI weather observation data |
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SMHI weather observation data is structured in csv files, separately for each weather parameter and weather observation station. |
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See the sample below: |
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```csv |
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Datum;Tid (UTC);Lufttemperatur;Kvalitet |
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2020-01-01;06:00:00;-2.2;G |
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2020-01-01;12:00:00;-2.7;G |
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2020-01-01;18:00:00;0.2;G |
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2020-01-02;06:00:00;0.3;G |
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2020-01-02;12:00:00;4.3;G |
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2020-01-02;18:00:00;4.9;G |
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2020-01-03;06:00:00;6.0;G |
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2020-01-03;12:00:00;2.7;G |
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2020-01-03;18:00:00;1.7;G |
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2020-01-04;06:00:00;-4.6;G |
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2020-01-04;12:00:00;0.6;G |
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2020-01-04;18:00:00;-5.9;G |
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2020-01-05;06:00:00;-7.9;G |
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2020-01-05;12:00:00;-3.1;G |
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``` |
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### ECMWF historical weather forecasts |
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Historical ECMWF weather forecasts contain a number of forecasted weather variables at 4 nearest grid points around each SMHI observation station: |
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``` |
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<xarray.Dataset> |
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Dimensions: (reference_time: 2983, valid_time: 54, |
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corner_index: 4, station_index: 275) |
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Coordinates: |
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* reference_time (reference_time) datetime64[ns] 2020-01-01 ... 20... |
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latitude (corner_index, station_index) float64 55.3 ... 68.7 |
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longitude (corner_index, station_index) float64 ... |
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point (corner_index, station_index) int64 ... |
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* valid_time (valid_time) int32 0 1 2 3 4 5 ... 48 49 50 51 52 53 |
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* station_index (station_index) int64 0 1 2 3 4 ... 271 272 273 274 |
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* corner_index (corner_index) <U3 'llc' 'lrc' 'ulc' 'urc' |
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station_names (station_index) <U29 ... |
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station_ids (station_index) int64 ... |
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Data variables: |
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PressureReducedMSL (reference_time, valid_time, corner_index, station_index) float32 ... |
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RelativeHumidity (reference_time, valid_time, corner_index, station_index) float32 ... |
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SolarDownwardRadiation (reference_time, valid_time, corner_index, station_index) float64 ... |
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Temperature (reference_time, valid_time, corner_index, station_index) float32 ... |
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WindDirection:10 (reference_time, valid_time, corner_index, station_index) float32 ... |
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WindSpeed:10 (reference_time, valid_time, corner_index, station_index) float32 ... |
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``` |
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### Topography data |
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The topography data is provided in the chunks cut around each of the SMHI stations. |
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The corners of each chunk correspond to ECMWF forecast grid points. |
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Each chunk consists approximately 361 x 361 points, spanning across 0.1° x 0.1°. (Some of the values across longitudes are NaN since apparently the Earth is not square [citation needed]). |
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## Loading the data |
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The dependencies can be installed through `conda` or `mamba` in the following way: |
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```bash |
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mamba create -n ourenv python pandas xarray dask netCDF4 |
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``` |
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--- |
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Below, for a given SMHI weather observation station, we read the following data: |
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- weather observations |
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- historical ECMWF weather forecasts |
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- topography/elevation |
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```python |
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import pickle |
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import pandas as pd |
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import xarray as xr |
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smhi_weather_observation_station_index = 153 |
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smhi_weather_observation_station_id = pd.read_csv( |
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'./smhi_weather_observation_stations.csv', |
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index_col='station_index' |
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).loc[smhi_weather_observation_station_index]['id'] # 102540 |
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weather_parameter = 1 # temperature |
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# NOTE: Need to unzip the file first! |
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smhi_observation_data = pd.read_csv( |
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'./weather_observations/smhi_observations_from_2020/' |
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f'parameter_{weather_parameter}' |
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f'/smhi_weather_param_{weather_parameter}_station_{smhi_weather_observation_station_id}.csv', |
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sep=';', |
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) |
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print('SMHI observation data:') |
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print(smhi_observation_data) |
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ecmwf_data = xr.open_dataset( |
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'./ecmwf_historical_weather_forecasts/ECMWF_HRES-reindexed.nc' |
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).sel(station_index=smhi_weather_observation_station_index) |
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print('ECMWF data:') |
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print(ecmwf_data) |
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topography_data = xr.open_dataset( |
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'topography/sweden_chunks_copernicus-dem-30m' |
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f'/topography_chunk_station_index-{smhi_weather_observation_station_index}.nc' |
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) |
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print('Topography chunk:') |
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print(topography_data) |
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mean_elevations = pickle.load(open('./topography/ecmwf_grid_mean_elevations.pkl', 'rb')) |
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print('Mean elevation:') |
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print(mean_elevations[0]) |
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``` |
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