Visualize wind speed, focus on typhoon in Taiwan

Process

1. Load in necessary libraries
2. Load in netcdf file using the netCDF4 library
3. Get the u_wind and v_wind variables: dataset.variables['u'][:], dataset.variables['v'][:]
4. calculate the wind speed: wind_speed = np.sqrt(u_wind2 + v_wind2)
5. Get the lats and lons: dataset.variables['latitude'][:], dataset.variables['longitude'][:]
6. Create color maps: i.e. my_cmap = plt.colormaps['twilight']
7. Use maplotlib to plot contours: plt.contourf(lons, lats, wind_speed, 60, transform=ccrs.PlateCarree())
8. IF don't want a global map, set the extent like this: ax.set_extent([118, 130, 15, 35], crs=ccrs.PlateCarree())
9. Add arrows using on top of map using maplotlib quiver: plt.quiver(lons, lats, u_wind, v_wind, scale=800, color='k')

Data

NOAA

• NOMADS Data at NCEP: https://nomads.ncep.noaa.gov/gribfilter.php?ds=gfs_sflux

1. Parameters: GFS sflux, UGRID, VGRID, 1 hybrid level
2. We will get a GRIB file, will need to convert it to a .nc file

Some other wind sources:

• NCEI GFS: https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast
• Copernicus: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form

Some example outputs (testing different colors)

Global map

Global map with Robinson projection

Map zoomed into Taiwan