Run SMRF

After installing SMRF, generating a topo and creating a configuration file, SMRF can be ran. There are two ways to run SMRF, first is through the run_smrf command or through the SMRF API. If SMRF is being used as input to a snow or hydrology model, we recommend to use run_smrf as it will generate all the input required.

run_smrf command

To run a full simulation simply run (barring any errors):

run_smrf <config_file_path>

SMRF API

The smrf package can also be used as an API, typically to focus on a single variable. There are steps that SMRF uses to load the data then distribute and usage of the API should follow the same pattern. For example, below is the function run_smrf.

with SMRF(config) as s:
    # load topo data
    s.loadTopo()

    # initialize the distribution
    s.initializeDistribution()

    # initialize the outputs if desired
    s.initializeOutput()

    # load weather data  and station metadata
    s.loadData()

    # distribute
    s.distributeData()

The next example below builds on above and will distribute air temperature and vapor pressure. They can be used to get the distributed dew point or web bulb temperature.

configFile = 'config.ini'

with smrf.framework.SMRF(configFile) as s:

    # ===================================================================
    # Model setup and initialize
    # ===================================================================

    # These are steps that will load the necessary data and initialize
    # the framework. Once loaded, this shouldn't need to be re-ran except
    # if something major changes

    # load topo data
    s.loadTopo()

    # Create the distribution class
    s.distribute['air_temp'] = smrf.distribute.air_temp.ta(
        s.config['air_temp'])
    s.distribute['vapor_pressure'] = smrf.distribute.vapor_pressure.vp(
        s.config['vapor_pressure'])

    # load weather data  and station metadata
    s.loadData()

    # Initialize the distribution
    for v in s.distribute:
        s.distribute[v].initialize(s.topo, s.data)

    # initialize the outputs if desired
    s.initializeOutput()

    # Distribute the data and output
    for output_count, t in enumerate(s.date_time):

        s.distribute['air_temp'].distribute(s.data.air_temp.ix[t])
        s.distribute['vapor_pressure'].distribute(
            s.data.vapor_pressure.ix[t],
            s.distribute['air_temp'].air_temp)

        # output at the frequency and the last time step
        if (output_count % s.config['output']['frequency'] == 0) or \
                (output_count == len(s.date_time)):
            s.output(t)

Create a config file SMRF and Docker