Space–time calibration of wind speed forecasts from regional climate models

Numerical weather predictions (NWPs) are systematically subject to errors due to the deterministic solutions used by numerical models to simulate the atmosphere. Statistical postprocessing techniques are widely used nowadays for NWP calibration. However, time-varying bias is usually not accommodated by such models. The calibration performance is also sensitive to the temporal window used for training. This paper proposes space–time models that extend the main statistical postprocessing approaches to calibrate NWP model outputs. Trans-Gaussian random fields are considered to account for meteorological variables with asymmetric behavior. Data augmentation is used to account for the censoring of the response variable. The benefits of the proposed extensions are illustrated through the calibration of hourly 10-m height wind speed forecasts in Southeastern Brazil coming from the Eta model.