pyart.map.gridstats_from_radar#

pyart.map.gridstats_from_radar(radars, grid_shape, grid_limits, statistic, weighted=True, copy_field_dtypes=True, **kwargs)[source]#

Map one or more radars to a Cartesian grid returning a Grid object, while computing a possibly weighted statistic from all polar gates that fall within the ROI of a given Cartesian grid cell

Additional arguments are passed to map_gates_to_grid().

Parameters:

• radars (Radar or tuple of Radar objects.) – Radar objects which will be mapped to the Cartesian grid.

• grid_shape (3-tuple of floats) – Number of points in the grid (z, y, x).

• grid_limits (3-tuple of 2-tuples) – Minimum and maximum grid location (inclusive) in meters for the z, y, x coordinates.

• statistic (str) – Statistic to compute over all polar gates that fall within the ROI of every grid cell, supported statistics are “min”, “max”, “mean”, “std”, “QXX” (quantile XX, for example Q25, Q50, Q99) “skewness” and “kurtosis”

• copy_field_dtypes (bool) – Whether or not to maintain the original dtypes found in the radar fields, which will then be used in the grid fields.

Returns:

grid (Grid) – A pyart.io.Grid object containing the gridded radar data.

See also

map_to_grid

Map to grid and return a dictionary of radar fields.

map_gates_to_grid

Map each gate onto a grid returning a dictionary of radar fields.

References

Barnes S., 1964: A Technique for Maximizing Details in Numerical Weather Map Analysis. Journal of Applied Meteorology and Climatology, 3(4), 396-409.

Cressman G., 1959: An operational objective analysis system. Monthly Weather Review, 87(10), 367-374.

Pauley, P. M. and X. Wu, 1990: The theoretical, discrete, and actual response of the Barnes objective analysis scheme for one- and two-dimensional fields. Monthly Weather Review, 118, 1145-1164