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()
ormap_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