<jupyter_start><jupyter_text>HRRR Time Series Demo The highest resolution forecast for the CONUS is the 2.5km High Resolution Rapid Refresh (HRRR) model.  The HRRR temperature forecast at a specified lon,lat location can be extracted as CSV from the Unidata THREDDS server using the NetCDF Subset Service (NCSS). The RESTful URL in the next cell was constructed using the form at:  https://thredds.ucar.edu/thredds/ncss/grib/NCEP/HRRR/CONUS_2p5km/Best/pointDataset.htmlbut it can be generated (or modified) manually or programatically as well.<jupyter_code>url = 'https://thredds.ucar.edu/thredds/ncss/grib/NCEP/HRRR/CONUS_2p5km/Best?var=Temperature_height_above_ground&latitude=41.5265&longitude=-70.6731&time_start=2019-11-10T08%3A00%3A00Z&time_end=2019-11-14T02%3A00%3A00Z&vertCoord=2.0&accept=csv'
import pandas as pd
df = pd.read_csv(url, parse_dates=True, skiprows=1, names=['lat','lon','z','temp'], 
                 index_col=0)
df
df.temp = (df.temp - 273.15) * (9/5) + 32
df
import matplotlib.pyplot as plt
%matplotlib inline
import hvplot.pandas
df.temp.plot()
df['temp'].hvplot(grid=True)<jupyter_output><empty_output><jupyter_text>Extract CSV time series data from the USGS CMG ERDDAP Server.  The RESTful URL in the next cell was constructed using the form at: https://geoport.usgs.esipfed.org/erddap/tabledap/NMB14M1T01awWvs-p.html but it can be generated (or modified) manually or programatically as well.<jupyter_code>url = 'https://geoport.usgs.esipfed.org/erddap/tabledap/10634dws-a_z1.csvp?time%2Cwh_4061'
df = pd.read_csv(url, parse_dates=True, index_col='time (UTC)')
df.hvplot(grid=True)<jupyter_output><empty_output>
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