The CF (Climate and Forecast) conventions for netCDF define standards for Discrete Sampling Geometries (DSG). These collections of sampled data include sets of profiles, sets of time series, sets of trajectories, and sets of points. Ferret/PyFerret has been extended to recognize DSG dataset types and provides commands which allows one to easily: select subsets in space and time; select subsets of stations, ships or other platforms; generate plots appropriate to the data type; analyze data per station; and regrid for comparison to gridded datasets.
Ferret/PyFerret can perform on-the-fly aggregation of data files representing a time series, a collection of forecasts, or collections of forecasts from different models. The time axis, and the forecast axis, if applicable, of the generated virtual dataset is either derived from the time axes in the aggregated data files or can be directly specified by the user. Relatively simple commands provide views of forecasts that cleanly show biases and other trends in the data, or allow comparisons of forecasts from different models.
This presentation will highlight this functionality in Ferret/PyFerret, and demonstrate how PyFerret allows you to explore and better understand your data.
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