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.

由于数值模型用于模拟大气的确定性解决方案，数值天气预报 (NWP) 会系统地出现错误。统计后处理技术如今广泛用于 NWP 校准。但是，此类模型通常不适应时变偏差。校准性能对用于训练的时间窗口也很敏感。本文提出了扩展主要统计后处理方法以校准 NWP 模型输出的时空模型。跨高斯随机场被认为可以解释具有不对称行为的气象变量。数据增强用于解释响应变量的删失。