| # Dataset: Low-to-high-resolution weather forecasting using topography | |
| The dataset is intended and structured for the problem of transforming/interpolating low-resolution weather forecasts into higher resolution using topography data. | |
|  | |
| The dataset consists of 3 different types of data (as illustrated above): | |
| - **Historical weather observation data ([SMHI](https://opendata.smhi.se/apidocs/metobs/index.html))** | |
| - Historical weather observation data from selected SMHI observation stations (evaluation points) | |
| - **Historical low-resolution weather forecasts ([ECMWF](https://www.ecmwf.int/en/forecasts/datasets/set-i))** | |
| - For a given SMHI station: Historical, (relatively) low-resolution ECMWF weather forecasts from the 4 nearest ECMWF grid points | |
| - **Topography/elevation data ([Copernicus DEM GLO-30](https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model))**: | |
| - Topography/elevation data around a given SMHI station, grid enclosed by the 4 ECMWF points | |
| The dataset is meant to facilitate the following modeling pipeline: | |
| - 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) | |
| - SMHI weather observation data is used as a sparse evaluation point of the produced higher-resolution forecasts | |
| ## Data details & samples | |
| ### SMHI weather observation data | |
| SMHI weather observation data is structured in csv files, separately for each weather parameter and weather observation station. | |
| See the sample below: | |
| ```csv | |
| Stationsnamn;Stationsnummer;Stationsnät;Mäthöjd (meter över marken) | |
| Stockholm-Observatoriekullen;98210;SMHIs stationsnät;2.0 | |
| Parameternamn;Beskrivning;Enhet | |
| Lufttemperatur;momentanvärde, 1 gång/tim;celsius | |
| Tidsperiod (fr.o.m);Tidsperiod (t.o.m);Höjd (meter över havet);Latitud (decimalgrader);Longitud (decimalgrader) | |
| 1859-01-01 00:00:00;2024-02-01 07:20:11;43.133;59.3417;18.0549 | |
| Datum;Tid (UTC);Lufttemperatur;Kvalitet;;Tidsutsnitt: | |
| 2023-10-27;06:00:00;2.4;G | |
| 2023-10-27;18:00:00;3.0;G | |
| 2023-10-28;06:00:00;0.6;G | |
| 2023-10-28;18:00:00;1.9;G | |
| 2023-10-29;06:00:00;0.3;G | |
| 2023-10-29;18:00:00;3.2;G | |
| 2023-10-30;06:00:00;2.4;G | |
| 2023-10-30;18:00:00;4.3;G | |
| 2023-10-31;06:00:00;8.5;G | |
| 2023-10-31;18:00:00;4.6;G | |
| 2023-11-01;06:00:00;1.9;G | |
| ``` | |
| ### ECMWF historical weather forecasts | |
| Historical ECMWF weather forecasts contain a number of forecasted weather variables at 4 nearest grid points around each SMHI observation station: | |
| ``` | |
| <xarray.Dataset> | |
| Dimensions: (reference_time: 2983, valid_time: 54, | |
| corner_index: 4, station_index: 275) | |
| Coordinates: | |
| * reference_time (reference_time) datetime64[ns] 2020-01-01 ... 20... | |
| latitude (corner_index, station_index) float64 55.3 ... 68.7 | |
| longitude (corner_index, station_index) float64 ... | |
| point (corner_index, station_index) int64 ... | |
| * valid_time (valid_time) int32 0 1 2 3 4 5 ... 48 49 50 51 52 53 | |
| * station_index (station_index) int64 0 1 2 3 4 ... 271 272 273 274 | |
| * corner_index (corner_index) <U3 'llc' 'lrc' 'ulc' 'urc' | |
| station_names (station_index) <U29 ... | |
| station_ids (station_index) int64 ... | |
| Data variables: | |
| PressureReducedMSL (reference_time, valid_time, corner_index, station_index) float32 ... | |
| RelativeHumidity (reference_time, valid_time, corner_index, station_index) float32 ... | |
| SolarDownwardRadiation (reference_time, valid_time, corner_index, station_index) float64 ... | |
| Temperature (reference_time, valid_time, corner_index, station_index) float32 ... | |
| WindDirection:10 (reference_time, valid_time, corner_index, station_index) float32 ... | |
| WindSpeed:10 (reference_time, valid_time, corner_index, station_index) float32 ... | |
| ``` | |
| ### Topography data | |
| The topography data is provided in the chunks cut around each of the SMHI stations. | |
| The corners of each chunk correspond to ECMWF forecast grid points. | |
| 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]). | |
|  | |
| ## Loading the data | |
| The dependencies can be installed through `conda` or `mamba` in the following way: | |
| ```bash | |
| mamba create -n ourenv python pandas xarray dask netCDF4 | |
| ``` | |
| --- | |
| Below, for a given SMHI weather observation station, we read the following data: | |
| - weather observations | |
| - historical ECMWF weather forecasts | |
| - topography/elevation | |
| ```python | |
| import pandas as pd | |
| import xarray as xr | |
| smhi_weather_observation_station_index = 153 | |
| smhi_weather_observation_station_id = pd.read_csv( | |
| './smhi_weather_observation_stations.csv', | |
| index_col='station_index' | |
| ).loc[smhi_weather_observation_station_index]['id'] # 102540 | |
| weather_parameter = 1 # temperature | |
| # NOTE: Need to unzip the file first! | |
| smhi_observation_data = pd.read_csv( | |
| './weather_observations/smhi_observations_from_2020/' | |
| f'parameter_{weather_parameter}' | |
| f'/smhi_weather_param_{weather_parameter}_station_{smhi_weather_observation_station_id}.csv', | |
| sep=';', | |
| ) | |
| print(smhi_observation_data) | |
| ecmwf_data = xr.open_dataset( | |
| './ecmwf_historical_weather_forecasts/ECMWF_HRES-reindexed.nc' | |
| ).sel(station_index=smhi_weather_observation_station_index) | |
| print(ecmwf_data) | |
| topography_data = xr.open_dataset( | |
| 'topography/sweden_chunks_copernicus-dem-30m' | |
| f'/topography_chunk_station_index-{smhi_weather_observation_station_index}.nc' | |
| ) | |
| print(topography_data) | |
| ``` | |