geocat.comp.rgrid2rcm

geocat.comp.rgrid2rcm(lat1d, lon1d, fi, lat2d, lon2d, msg=None, meta=False)
Interpolates data on a rectilinear lat/lon grid to a curvilinear grid like

those used by the RCM, WRF and NARR models/datasets.

Parameters

  • lat1d (numpy.ndarray) – A one-dimensional array that specifies the latitude coordinates of the regular grid. Must be monotonically increasing.

  • lon1d (numpy.ndarray) – A one-dimensional array that specifies the longitude coordinates of the regular grid. Must be monotonically increasing.

  • fi (numpy.ndarray) – A multi-dimensional array to be interpolated. The rightmost two dimensions (latitude, longitude) are the dimensions to be interpolated.

  • lat2d (numpy.ndarray) – A two-dimensional array that specifies the latitude locations of fi. Because this array is two-dimensional it is not an associated coordinate variable of fi.

  • lon2d (numpy.ndarray) – A two-dimensional array that specifies the longitude locations of fi. Because this array is two-dimensional it is not an associated coordinate variable of fi.

  • msg (numpy.number) – A numpy scalar value that represent a missing value in fi. This argument allows a user to use a missing value scheme other than NaN or masked arrays, similar to what NCL allows.

  • meta (bool) – If set to True and the input array is an Xarray, the metadata from the input array will be copied to the output array; default is False. Warning: this option is not currently supported.

Returns

The interpolated grid. A multi-dimensional array of the same size as fi except that the rightmost dimension sizes have been replaced by the sizes of lat2d and lon2d respectively. Double if fi is double, otherwise float.

Return type

numpy.ndarray

Description:

Interpolates data on a rectilinear lat/lon grid to a curvilinear grid, such as those used by the RCM (Regional Climate Model), WRF (Weather Research and Forecasting) and NARR (North American Regional Reanalysis) models/datasets. No extrapolation is performed beyond the range of the input coordinates. The method used is simple inverse distance weighting. Missing values are allowed but ignored.

Examples

Example 1: Using rgrid2rcm with xarray.DataArray input

import numpy as np
import xarray as xr
import geocat.comp

# Open a netCDF data file using xarray default engine and load the data stream
# input grid and data
ds_rect = xr.open_dataset("./DATAFILE_RECT.nc")

# [INPUT] Grid & data info on the source rectilinear
ht_rect   =ds_rect.SOME_FIELD[:]
lat1D_rect=ds_rect.gridlat_[:]
lon1D_rect=ds_rect.gridlon_[:]

# Open a netCDF data file using xarray default engine and load the data stream
# for output grid
ds_curv = xr.open_dataset("./DATAFILE_CURV.nc")

# [OUTPUT] Grid on destination curvilinear grid (or read the 2D lat and lon from
#          an other .nc file
newlat2D_rect=ds_curv.gridlat2D_[:]
newlon2D_rect=ds_curv.gridlat2D_[:]

ht_curv = geocat.comp.rgrid2rcm(lat1D_rect, lon1D_rect, ht_rect, newlat2D_curv, newlon2D_curv)