Generate a monitor file compatible with the CIFΒΆ

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

We prepare in the following an example of

  1. Generate an arbitrary 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 you just created:

    here is a Python template to quick look the content of the

    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'])
  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; 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.