Impact of Spatial Bias Correction and Conditional Training on Bayesian Model Averaging Over the Northeast United States
The ensemble for this study selectively includes the 21-member Short Range Ensemble Forecast (SREF, 32 to 45-km grid spacing) run at the National Centers for Environmental Prediction (NCEP) as well as the 13-member Stony Brook University (SBU) system (12-km grid spacing) from the Weather Research and Forecasting (WRF-ARW) and Penn-State-NCAR Mesoscale Model (MM5). These models include different initial conditions and physical parameterizations (convective parameterization, boundary layer, and microphysics). A spatially dependent cumulative distribution function (CDF) bias correction is compared to a linear regression bias correction used in most previous BMA studies. The CDF bias correction adjusts the CDF of the model to the observation separately for each unique land surface type.
As will be discussed, the CDF bias correction is shown to more effectively remove model bias at all thresholds and locations compared to a linear regression. When verifying on fire threat days, results improve when bias correction and BMA are trained conditionally on fire threat days compared to traditional training. This is a result of differing model biases under unique synoptic conditions. Anomalies in the synoptic pattern during fire weather events are further explored.