How do I plot GFS grib2 data with Python?

Question

I would like to have a chart with the temperatures for the following days on my website, and the Global Forecasting System meets my needs the most. How do I plot the GRIB2 data in matplotlib and create a PNG image from the plot?

I've spend hours of searching on the internet, asking people who do know how to do this (they where not helpfull at all) and I don't know where to start. GFS data can be found here: ftp://ftp.ncep.noaa.gov/pub/data/nccf/com/gfs/prod/ If possible, I'd like it to be lightweight and without loosing too much server space.

Answer 1

When you think lightweight about data usage and storage, you may consider to use other data forms than GRIB. GRIB-files usually contain worldwide data, which is pretty useless when you only want to plot for a specific domain.

I can strongly recommend to use data from the NOAA-NCEP opendap data server. You can gain data from this server using netCDF4. Unfortunately, this server is known to be unstable at some times which may causes delays in refreshing runs and/or malformed datasets. Although, in 95% of the time, I have acces to all the data I need.

Note: This data server may be slow due to high trafficking after a release of a new run. Acces to the data server can be found here: http://nomads.ncdc.noaa.gov/data.php?name=access#hires_weather_datasets

Plotting data is pretty easy with Matplotlib and Basemap toolkits. Some examples, including usage of GFS-datasets, can be found here: http://matplotlib.org/basemap/users/examples.html

Answer 2

Basically, there are 2 steps:

1. use wgrib to extract selected variables from grib2 data, and save into NetCDF file. Although there are some API such as pygrib, yet I found it less buggy to use the command line tool directly. some useful links:

install: http://www.cpc.ncep.noaa.gov/products/wesley/wgrib2/compile_questions.html
tricks: http://www.ftp.cpc.ncep.noaa.gov/wd51we/wgrib2/tricks.wgrib2

For example, extract temperature and humidity:

wgrib2 test.grb2 -s | egrep '(:RH:2 m above ground:|:TMP:2 m above ground:)'|wgrib2 -i test.grb2 -netcdf test.nc

2. use Python libraries to process NetCDF files, example code may look like this:

   import warnings
   warnings.filterwarnings("ignore")
   import matplotlib.pyplot as plt
   import numpy as np
   import pandas as pd
   % matplotlib inline
   from netCDF4 import Dataset
   from mpl_toolkits.basemap import Basemap
   from pyproj import Proj
   import matplotlib.cm as cm
   import datetime

   file = "test.nc"
   rootgrp = Dataset(file, "r")
   x = rootgrp['longitude'][:] # 0-359, step = 1
   y = rootgrp['latitude'][:] # -90~90, step =1
   tmp = rootgrp['TMP_2maboveground'][:][0] # shape(181,360) dt = datetime.datetime(1970,1,1) + datetime.timedelta(seconds = rootgrp['time'][0])

   fig = plt.figure(dpi=150)
   m = Basemap(projection='mill',lat_ts=10,llcrnrlon=x.min(), urcrnrlon=x.max(),llcrnrlat=y.min(),urcrnrlat=y.max(), resolution='c')

   xx, yy = m(*np.meshgrid(x,y))
   m.pcolormesh(xx,yy,tmp-273.15,shading='flat',cmap=plt.cm.jet)
   m.colorbar(location='right')

   m.drawcoastlines()
   m.drawparallels(np.arange(-90.,120.,30.), labels=[1,0,0,0], fontsize=10)
   m.drawmeridians(np.arange(0.,360.,60.), labels=[0,0,0,1], fontsize=10)
   plt.title("{}, GFS, Temperature (C) ".format(dt.strftime('%Y-%m-%d %H:%M UTC')))
   plt.show()