Probing NWP model deficiencies by statistical postprocessing

Abstract

This article describes a Model Output Statistics (MOS) framework for wind speed forecast accuracy improvement. The approach is used to identify non-intuitive explanatory value from a diagnostic variable in an operational numerical weather prediction (NWP) model generating global weather forecasts four times daily, with numerous users worldwide. The analysis is based on two years of hourly wind speed time series measured at three locations: offshore, in coastal flat terrain, and inland in complex topography. Together with wind speed and wind direction predictors derived from the prognostic wind field, the lifted index NWP model diagnostic is found to perform well as statistical wind speed forecast model predictor.