Simple Analysis of Forecast-Model-Run-Collections in PyFerret

The development and analysis of a model predicting near-future geophysical conditions based on current conditions typically involves repeated execution of the model with updated current conditions, and comparing past predictions with current conditions.  Each model run produces data, say a NetCDF file, on some grid representing longitude, latitude, level, and time - often an abstract future time.  Examination of this model's results requires appropriate aggregation of these data along a forecast axis so that the time axis represents the actual "measurement" time of the predicted data values.  Furthermore, it is very desirable to compare results from different models, and/or different parameterizations of a model.  This leads to an aggregation of different model data along an ensemble axis in order to work with all these model data as one dataset.  

PyFerret and Ferret easily handle the automatic aggregation and manipulation of data in such a Forecast Model Run Collection spread over a multitude of NetCDF files.  This presentation will explain and demonstrate these simple-to-use functions which show the predictive skill of each model with just a new commands.