correct_bias#
- geobricks.correct_bias(data: Dataset, variables: str | List[str] | Dict[str, float], bias_factors: float | Dict[str, float] | None = None, region: str | None = None, progressive: bool = False, progressive_factors: Dict[str, ndarray] | None = None) Dataset[source]#
Apply bias correction to climate variables using either constant or monthly progressive factors.
- The correction is multiplicative:
corrected = original * factor
Parameters#
- dataxr.Dataset
Input dataset containing the variables to be corrected.
- variablesUnion[str, List[str], Dict[str, float]]
Variables to correct. Can be string, list of strings, or dict mapping variable names to factors.
- bias_factorsOptional[Union[float, Dict[str, float]]], default None
Base multiplicative factors. Float for uniform factor, dict for per-variable factors.
- regionOptional[str], default None
Region key for predefined factors from configuration.
- progressivebool, default False
If True, apply monthly progressive factors instead of base factors.
- progressive_factorsOptional[Dict[str, np.ndarray]], default None
Dict mapping variable names to 12-element monthly multiplier arrays.
Returns#
- xr.Dataset
Dataset with bias correction applied to specified variables.
Raises#
- ValueError
If variables parameter has invalid type.
Notes#
Multiplicative correction only. Progressive mode ignores base factors. Variables not found in dataset are skipped with warning.