Generate a monitor file compatible with the CIF

The CIF compares observations to simulations to carry out data assimilation. Observations are stored in a so-called monitor.nc file. Please have a look here to know more about monitor.nc files.

We prepare in the following an example of monitor.nc.

1. Generate an arbitrary monitor.nc with random data inside:

   this can be done by using the Yaml example file available here; you need to change paths in this yaml and can change the extent of the domain you use; once modified, run pyCIF with this Yaml: python -m pycif your_monitor_yaml.yml

2. Check the content of the monitor.nc you just created:

   here is a Python template to quick look the content of the monitor.nc:

   import matplotlib.pyplot as plt
   from pycif.utils.datastores import dump
   
   # Reading the monitor file into a Pandas dataframe
   datastore = dump.read_datastore(your_monitor_file)
   
   # Plot a map with the location of your stations
   sct = plt.scatter(datastore['lon'], datastore['lat'], c=datastore['alt'])
   
   plt.colorbar(sct)
   
   plt.show()
   

3. Transform the observations of your test case into a pyCIF compatible file:

   if you do not want to start from scratch and rather use observations from a test case, you can use the random template to create a real-case monitor.nc; at this step, you only need a file gathering information on the raw observations; this includes the following columns:

   • date

   • duration

   • station

   • network

   • parameter

   • lon, lat, alt

   • obs

   • obserror

   To produce such a file, you need to fill manually a pandas Datastore compatible with pyCIF from your existing observations. Details are given here about pyCIF datastores.

You now have a file storing all the raw observations you used for your test case.