xarray.Dataset.regridder.horizontal#
- Dataset.regridder.horizontal(data_var, output_grid, tool='xesmf', **options)#
Apply horizontal regridding to
data_var
of the currentxr.Dataset
tooutput_grid
.When might
Regrid2
be preferred overxESMF
?If performing conservative regridding from a high/medium resolution lat/lon grid to a coarse lat/lon target,
Regrid2
may provide better results as it assumes grid cells with constant latitudes and longitudes whilexESMF
assumes the cells are connected by Great Circles [1].Supported tools, methods and grids:
- xESMF (https://pangeo-xesmf.readthedocs.io/en/latest/)
Methods:
Bilinear
Conservative
Conservative Normed
Patch
Nearest s2d
Nearest d2s
Grids:
Rectilinear
Curvilinear
Find options at
xcdat.regridder.xesmf.XESMFRegridder()
- Regrid2
Methods:
Conservative
Grids:
Rectilinear
Find options at
xcdat.regridder.regrid2.Regrid2Regridder()
- Parameters:
data_var (
str
) – Name of the variable in thexr.Dataset
to regrid.output_grid (
xr.Dataset
) – Dataset containing output grid.tool (
str
) – Name of the regridding tool.**options (
Dict[str
,Any]
) – These options are passed to the tool being used for regridding. See specific regridder documentation for available options.
- Returns:
xr.Dataset
– With thedata_var
variable on the grid defined inoutput_grid
.- Raises:
ValueError – If tool is not supported.
References
Examples
Create destination grid:
>>> output_grid = xcdat.create_uniform_grid(-90, 90, 4.0, -180, 180, 5.0)
Regrid variable using “xesmf”:
>>> ds.regridder.horizontal("ts", output_grid, tool="xesmf", method="bilinear")
Regrid variable using “regrid2”:
>>> ds.regridder.horizontal("ts", output_grid, tool="regrid2")
Use convenience methods:
>>> ds.regridder.horizontal_xesmf("ts", output_grid, method="bilinear")
>>> ds.regridder.horizontal_regrid2("ts", output_grid)