  Numerical weather predictions (NWP) 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. Its 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
censuring in the response variable. The benefits of the proposed extensions are
illustrated through the calibration of hourly 10 m wind speed forecasts in
Southeastern Brazil coming from the Eta model.
