HRRR Dashboard

The High Resolution Rapid Refresh (HRRR, pronouned “her”) is the highest resolution (2.5km) weather forecast for the entire Continental USA. Here we investigate the gridded HRRR forecast data products from Unidata’s THREDDS server and visualizing the data using the holoviz tools.

[1]:
import xarray as xr
[2]:
url = 'http://thredds.ucar.edu/thredds/dodsC/grib/NCEP/HRRR/CONUS_2p5km/Best'
ds = xr.open_dataset(url)

Find all the data variables that depend on time (and are not time bounds)

[3]:
time_vars = []
for var in ds.data_vars:
    if len(ds[var].dims) > 0:
        if 'time' in ds[var].dims[0] and not 'bounds' in var:
            time_vars.append(var)

Read the coordinate reference system (crs) using metpy

[4]:
import metpy
[5]:
init_var = 'Temperature_height_above_ground'
[6]:
ds  = ds.metpy.parse_cf()
[7]:
crs = ds[init_var].metpy.cartopy_crs

Import the holoviz tools we need

[8]:
from cartopy import crs as ccrs
import hvplot.xarray
import holoviews as hv
from geoviews import tile_sources as gvts
import panel as pn