Coverage for mlair/helpers/data_sources/era5.py: 9%
53 statements
« prev ^ index » next coverage.py v6.4.2, created at 2023-12-18 17:51 +0000
« prev ^ index » next coverage.py v6.4.2, created at 2023-12-18 17:51 +0000
1"""Methods to load era5 data."""
2__author__ = "Lukas Leufen"
3__date__ = "2022-06-09"
5import logging
6import os
7from functools import partial
9import pandas as pd
10import xarray as xr
12from mlair import helpers
13from mlair.configuration.era5_settings import era5_settings
14from mlair.configuration.toar_data_v2_settings import toar_data_v2_settings
15from mlair.helpers.data_sources.toar_data_v2 import load_station_information, combine_meta_data, correct_timezone
16from mlair.helpers.data_sources.data_loader import EmptyQueryResult
17from mlair.helpers.meteo import relative_humidity_from_dewpoint
20def load_era5(station_name, stat_var, sampling, data_origin, time_dim, window_dim, target_dim, era5_data_path=None,
21 era5_file_names=None):
23 # make sure station_name parameter is a list
24 station_name = helpers.to_list(station_name)
26 # get data path
27 data_path, file_names = era5_settings(sampling, era5_data_path=era5_data_path, era5_file_names=era5_file_names)
29 # correct stat_var values if data is not aggregated (hourly)
30 if sampling == "hourly":
31 stat_var = {key: "values" for key in stat_var.keys()}
32 else:
33 raise ValueError(f"Given sampling {sampling} is not supported, only hourly sampling can be used.")
35 # load station meta using toar-data v2 API
36 meta_url_base, headers = toar_data_v2_settings("meta")
37 station_meta = load_station_information(station_name, meta_url_base, headers)
39 # sel data for station using sel method nearest
40 logging.info(f"load data for {station_meta['codes'][0]} from ERA5")
41 try:
42 lon, lat = station_meta["coordinates"]["lng"], station_meta["coordinates"]["lat"]
43 file_names = os.path.join(data_path, file_names)
44 with xr.open_mfdataset(file_names, preprocess=partial(preprocess_era5_single_file, lon, lat)) as data:
45 station_data = data.to_array().T.compute()
46 except OSError as e:
47 logging.info(f"Cannot load era5 data from path {data_path} and filenames {file_names} due to: {e}")
48 return None, None
50 if "relhum" in stat_var:
51 relhum = relative_humidity_from_dewpoint(station_data.sel(variable="D2M"), station_data.sel(variable="T2M"))
52 station_data = xr.concat([station_data, relhum.expand_dims({"variable": ["RHw"]})], dim="variable")
53 station_data.coords["variable"] = _rename_era5_variables(station_data.coords["variable"].values)
55 # check if all requested variables are available
56 if set(stat_var).issubset(station_data.coords["variable"].values) is False:
57 missing_variables = set(stat_var).difference(station_data.coords["variable"].values)
58 origin = helpers.select_from_dict(data_origin, missing_variables)
59 options = f"station={station_name}, origin={origin}"
60 raise EmptyQueryResult(f"No data found for variables {missing_variables} and options {options} in JOIN.")
61 else:
62 station_data = station_data.sel(variable=list(stat_var.keys()))
64 # convert to local timezone
65 station_data.coords["time"] = correct_timezone(station_data.to_pandas(), station_meta, sampling).index
66 station_data = station_data.rename({"time": time_dim, "variable": target_dim})
68 # expand window_dim
69 station_data = station_data.expand_dims({window_dim: [0]})
71 # create meta data
72 variable_meta = _emulate_meta_data(station_data.coords[target_dim].values)
73 meta = combine_meta_data(station_meta, variable_meta)
74 meta = pd.DataFrame.from_dict(meta, orient='index')
75 meta.columns = station_name
76 return station_data, meta
79def preprocess_era5_single_file(lon, lat, ds):
80 """Select lon and lat from data file and transform valid time into lead time."""
81 ds = ds.sel(lon=lon, lat=lat, method="nearest", drop=True)
82 return ds
85def _emulate_meta_data(variables):
86 general_meta = {"sampling_frequency": "hourly", "data_origin": "model", "data_origin_type": "model"}
87 roles_meta = {"roles": [{"contact": {"organisation": {"name": "ERA5", "longname": "ECMWF"}}}]}
88 variable_meta = {var: {"variable": {"name": var}, **roles_meta, ** general_meta} for var in variables}
89 return variable_meta
92def _rename_era5_variables(era5_names):
93 mapper = {"SP": "press", "U10M": "u", "V10M": "v", "T2M": "temp", "D2M": "dew", "BLH": "pblheight",
94 "TCC": "cloudcover", "RHw": "relhum"}
95 era5_names = list(era5_names)
96 try:
97 join_names = list(map(lambda x: mapper[x], era5_names))
98 return join_names
99 except KeyError as e:
100 raise KeyError(f"Cannot map names from era5 to join naming convention: {e}")