pyart.map.grid_from_radars#

pyart.map.grid_from_radars(radars, grid_shape, grid_limits, gridding_algo='map_gates_to_grid', copy_field_dtypes=True, **kwargs)[source]#

Map one or more radars to a Cartesian grid returning a Grid object.

Additional arguments are passed to map_to_grid() or 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.

  • gridding_algo (‘map_to_grid’ or ‘map_gates_to_grid’) – Algorithm to use for gridding. ‘map_to_grid’ finds all gates within a radius of influence for each grid point, ‘map_gates_to_grid’ maps each radar gate onto the grid using a radius of influence and is typically significantly faster.

  • 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