standardize_era5_land#
- class standardize_era5.standardize_era5_land(data: str | Path | Dataset | Dict[str, str | Path], target_frequency: str = 'auto', apply_bias_correction: bool = False, bias_region: str = None, custom_bias_factors: Dict[str, float] | None = None, progressive_bias: bool = False, custom_progressive_factors: Dict[str, ndarray] | None = None, output_path: bool | str | Path | None = None, output_prefix: str = 'ERA5Land', force_reprocess: bool = False, compute_secondary: bool | list | None = None, target_crs: int | str | None = 4326)[source]#
Standardize ERA5-Land data.
Processes files individually without merging.
Parameters#
- datastr, pathlib.Path, xr.Dataset, or dict
Input data: - File path: process single file - Directory path: process all ERA5-Land files individually - Dataset: process in memory - Dict: process each file path individually
- target_frequencystr
Target temporal frequency (‘auto’, ‘hourly’, ‘daily’, ‘monthly’)
- apply_bias_correctionbool
Whether to apply bias correction
- bias_regionstr
Region for bias correction factors
- custom_bias_factorsdict, optional
Custom bias factors {var_name: factor}
- progressive_biasbool
Use progressive (monthly) bias instead of fixed bias
- custom_progressive_factorsdict, optional
Custom monthly factors {var_name: array[12]}
- output_pathbool, str, pathlib.Path, or None
Output directory control: - False: No files saved, return datasets in memory only - None or True: Save to current directory or input file directory - str/Path: Save to specified directory
- output_prefixstr, default ‘ERA5Land’
Prefix for output filenames
- force_reprocessbool
Force reprocessing even if already standardized
- compute_secondaryOptional[Union[bool, list]]
Control secondary variable computation: - None or False: No secondary variables computed - True: Compute all possible secondary variables - List[str]: Compute only specified secondary variables Available: [‘wind_speed’, ‘relative_humidity’, ‘ET0’, ‘EW0’]
Returns#
- Union[xr.Dataset, Dict[str, pathlib.Path]]
Single file/dataset input: standardized dataset or output path
Directory/dict input: dict of output paths