stilt.observations.select_observations_spatial#
- stilt.observations.select_observations_spatial(longitudes, latitudes, *, site_longitude, site_latitude, near_field_dlon, near_field_dlat, near_field_cols, near_field_rows, background_cols, background_rows, domain_lon_range, domain_lat_range)[source]#
Select soundings densely near a site and sparsely across the domain.
Lays a dense grid of points around the site and a sparse grid over the whole domain, and keeps the sounding nearest to each grid point. The dense soundings cover the site, where footprints matter most, and the sparse ones give a background. This is X-STILT’s
sel.obs4recpv2.- Parameters:
longitudes (
TypeAliasType) – Sounding positions, in degrees.latitudes (
TypeAliasType) – Sounding positions, in degrees.site_longitude (
float) – Center of the dense grid, in degrees.site_latitude (
float) – Center of the dense grid, in degrees.near_field_dlon (
float) – Half-width and half-height of the dense grid, in degrees.near_field_dlat (
float) – Half-width and half-height of the dense grid, in degrees.near_field_cols (
int) – Number of dense grid points across and up.near_field_rows (
int) – Number of dense grid points across and up.background_cols (
int) – Number of sparse grid points across and up.background_rows (
int) – Number of sparse grid points across and up.domain_lon_range (
tuple[float,float]) –(min, max)extent of the sparse grid, in degrees.domain_lat_range (
tuple[float,float]) –(min, max)extent of the sparse grid, in degrees.
- Returns:
Positions of the selected soundings, each once, in order of latitude. Use them as
df.iloc[selected].- Return type: