swirlspy.blending package

Blending data / nowcast.

class swirlspy.blending.Raw(data: DataArray, sites: Optional[List[Tuple[float, float, float]]] = None, weight: float = 0.1)

Bases: tuple

Raw data and it’s required information for composite QPE

dataxarray.DataArray: raw data
siteslist of (axis 1, axis 2, radius), Default is None: a list radar information, point of center (axis 1, axis 2) and effectivity area’s radius
weightfloat, Default is 0.1: weighting of data in the output data

property data: Alias for field number 0

property sites: Alias for field number 1

property weight: Alias for field number 2

swirlspy.blending.comp_qpe(grid_steps: Tuple[float, float], area: Tuple[float, float, float, float], datas: List[Raw], zero_value: float = 13, method: str = 'pdf', method_args: Optional[dict] = None, apply_smoothing: bool = False) → DataArray

Calculate composite QPE from multiple data sources. Assume all data is lay on same grid.

grid_steps: (axis 1’s step size, axis 2’s step size): step size of grid on both axis, e.g. (y, x), (northing, easting)
area: (axis 1’s start, axis 1’s end, axis 2’s start, axis 2’s end): boundary of output area
datas: list of (Raw): Data sources with necessary data, see {Raw} for details.

composite_data {xarray.DataArray}: The composite data calculated from given data sources.

swirlspy.blending.nwp_bias_correction(radar: DataArray, nwp: DataArray, proj4_str: Optional[str] = None, quantiles: list = [100, 95, 90, 85, 80, 75, 70, 65, 60, 55, 50, 45, 40, 35, 30, 25, 20, 15, 10, 5, 0]) → DataArray

Bias correction of numerical weather prediction data. The objective of bias correction is to match the nwp percentile to the radar percentile This is also known as frequency matching.

radar: (xarray.DataArray): Radar data
nwp: (xarray.DataArray): Numerical weather prediction
quantiles: list of (int): Probability quantiles to caculate the percentiles of NWP and radar.

nwp_corrected {xarray.DataArray}: Corrected NWP data.

swirlspy.blending.rains(nwp: DataArray, nowcast: DataArray) → DataArray

Calculate blending numerical weather prediction and nowcast result with RaINS. Assume all data is lay on same time grid.

nwp: (xarray.DataArray): Numerical weather prediction
nowcast: (xarray.DataArray): Nowcast result data.

blended_data {xarray.DataArray}: The blended nowcast data.