Assessment of storm wind speed prediction using gridded Bayesian regression applied to historical events with NCAR's real-time ensemble forecast system

GBLR products are produced by interpolating regression coefficients deduced from modeled-observed pairs of historical storms at meteorological stations to grid points, thus producing a gridded product.

The GBLR model is developed for the 10 members of the National Center for Atmospheric Research (NCAR) real-time dynamic ensemble prediction system for a database composed of 92 storms, using leave-one-storm-out cross validation. GBLR almost eliminates the bias of the raw deterministic prediction and achieves average coefficient of determination (R2) improvement of 36% and root-mean-square error reduction of 29% with respect to the ensemble mean for individual storm forecasts.