An Optimal Model Output Calibration Algorithm Suitable for Objective Temperature Forecasting

Abstract

Abstract An optimal model output calibration (MOC) algorithm suitable for surface air temperature forecasts is proposed and tested with the National Centers for Environmental Prediction Regional Spectral Model (RSM). Differing from existing methodologies and the traditional model output statistics (MOS) technique, the MOC algorithm uses forecasts and observations of the most recent 2–4 weeks to objectively estimate and adjust the current model forecast errors and make refined predictions. The MOC equation, a multivariate linear regression equation with forecast error being the predictand, objectively screens as many as 30 candidates of predictors and optimally selects no more than 6. The equation varies from day to day and from site to site. Since it does not rely on long-term statistics of stable model runs, the MOC minimizes the influence of changes in model physics and spatial resolution on the forecast refinement process. Forecast experiments were conducted for six major urban centers in the Tennessee...