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Error code: FeaturesError Exception: ParserError Message: Error tokenizing data. C error: Expected 16 fields in line 6, saw 17 Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 322, in compute compute_first_rows_from_parquet_response( File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response rows_index = indexer.get_rows_index( File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 640, in get_rows_index return RowsIndex( File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 521, in __init__ self.parquet_index = self._init_parquet_index( File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 538, in _init_parquet_index response = get_previous_step_or_raise( File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 591, in get_previous_step_or_raise raise CachedArtifactError( libcommon.simple_cache.CachedArtifactError: The previous step failed. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 240, in compute_first_rows_from_streaming_response iterable_dataset = iterable_dataset._resolve_features() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features features = _infer_features_from_batch(self.with_format(None)._head()) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head return _examples_to_batch(list(self.take(n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__ yield from islice(self.ex_iterable, self.n) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__ for key, pa_table in self.generate_tables_fn(**self.kwargs): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 195, in _generate_tables for batch_idx, df in enumerate(csv_file_reader): File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__ return self.get_chunk() File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk return self.read(nrows=size) File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1923, in read ) = self._engine.read( # type: ignore[attr-defined] File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read chunks = self._reader.read_low_memory(nrows) File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 16 fields in line 6, saw 17
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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)
- Historical weather observation data from selected SMHI observation stations (evaluation points)
- Historical low-resolution weather forecasts (ECMWF)
- 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):
- Topography/elevation data around a given SMHI station, grid enclosed by the 4 ECMWF points
- 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)
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
License and terms
The terms of usage of the source data and their corresponding licenses:
- SMHI weather observation data: (Creative Common Attribution 4.0)
- ECMWF historical weather forecasts:
- Copernicus DEM GLO-30:
Acknowledgment
The dataset has been developed as part of the OWGRE project, funded within the ERA-Net SES Joint Call 2020 for transnational research, development and demonstration projects.
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:
Datum;Tid (UTC);Lufttemperatur;Kvalitet
2020-01-01;06:00:00;-2.2;G
2020-01-01;12:00:00;-2.7;G
2020-01-01;18:00:00;0.2;G
2020-01-02;06:00:00;0.3;G
2020-01-02;12:00:00;4.3;G
2020-01-02;18:00:00;4.9;G
2020-01-03;06:00:00;6.0;G
2020-01-03;12:00:00;2.7;G
2020-01-03;18:00:00;1.7;G
2020-01-04;06:00:00;-4.6;G
2020-01-04;12:00:00;0.6;G
2020-01-04;18:00:00;-5.9;G
2020-01-05;06:00:00;-7.9;G
2020-01-05;12:00:00;-3.1;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:
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
import pickle
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:')
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:')
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 chunk:')
print(topography_data)
mean_elevations = pickle.load(open('./topography/ecmwf_grid_mean_elevations.pkl', 'rb'))
print('Mean elevation:')
print(mean_elevations[0])
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