Let's open a WRF model output file:
python
import salem from salem.utils import get_demo_file ds = salem.open_xr_dataset(get_demo_file('wrfout_d01.nc'))
Let's take a time slice of the variable T2 for a start:
python
t2 = ds.T2.isel(Time=2)
@savefig plot_wrf_t2.png width=80% t2.salem.quick_map()
Although we are on a Lambert Conformal projection, it's possible to subset the file using longitudes and latitudes:
python
t2_sub = t2.salem.subset(corners=((77., 20.), (97., 35.)), crs=salem.wgs84)
@savefig plot_wrf_t2_corner_sub.png width=80% t2_sub.salem.quick_map()
It's also possible to use geometries or shapefiles to subset your data:
python
shdf = salem.read_shapefile(get_demo_file('world_borders.shp')) shdf = shdf.loc[shdf['CNTRY_NAME'].isin(['Nepal', 'Bhutan'])] # GeoPandas' GeoDataFrame t2_sub = t2_sub.salem.subset(shape=shdf, margin=2) # add 2 grid points
@savefig plot_wrf_t2_country_sub.png width=80% t2_sub.salem.quick_map()
Based on the same principle, one can mask out the useless grid points:
python
t2_roi = t2_sub.salem.roi(shape=shdf)
@savefig plot_wrf_t2_roi.png width=80% t2_roi.salem.quick_map()
Maps can be pimped with topographical shading, points of interest, and more:
python
smap = t2_roi.salem.get_map(data=t2_roi-273.15, cmap='RdYlBu_r', vmin=-14, vmax=18) _ = smap.set_topography(get_demo_file('himalaya.tif')) smap.set_shapefile(shape=shdf, color='grey', linewidth=3) smap.set_points(91.1, 29.6) smap.set_text(91.2, 29.7, 'Lhasa', fontsize=17)
@savefig plot_wrf_t2_topo.png width=80% smap.visualize()
Maps are persistent, which is useful when you have many plots to do. Plotting further data on them is possible, as long as the geolocalisation information is shipped with the data (in that case, the DataArray's attributes are lost in the conversion from Kelvins to degrees Celsius so we have to set it explicitly):
python
smap.set_data(ds.T2.isel(Time=1)-273.15, crs=ds.salem.grid)
@savefig plot_wrf_t2_transform.png width=80% smap.visualize(title='2m temp - large domain', cbar_title='C')
Salem can also transform data from one grid to another:
python
dse = salem.open_xr_dataset(get_demo_file('era_interim_tibet.nc')) t2_era_reproj = ds.salem.transform(dse.t2m) assert t2_era_reproj.salem.grid == ds.salem.grid @savefig plot_era_repr_nn.png width=80% t2_era_reproj.isel(time=0).salem.quick_map()
python
t2_era_reproj = ds.salem.transform(dse.t2m, interp='spline') @savefig plot_era_repr_spline.png width=80% t2_era_reproj.isel(time=0).salem.quick_map()