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

• Published in: Environmental and ecological statistics Vol. 28; no. 3; pp. 631 - 665
• Main Authors: Gomes, Luiz E. S., Fonseca, Thaís C. O., Gonçalves, Kelly C. M., Ruiz-Cárdenas, Ramiro
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2021; Springer Nature B.V
• Subjects: Atmospheric models; Biomedical and Life Sciences; Brazil; Calibration; Chemistry and Earth Sciences; climate; Climate models; Climate prediction; Computer Science; Ecology; Fields (mathematics); Health Sciences; Life Sciences; Math. Appl. in Environmental Science; Mathematical models; Medicine; Numerical models; Numerical weather forecasting; Physics; space and time; Statistical analysis; statistics; Statistics for Engineering; Statistics for Life Sciences; Theoretical Ecology/Statistics; Training; Weather forecasting; Wind speed; Brazil; Data augmentation; Spatiotemporal dynamic linear models; Eta model; Statistical postprocessing; Wind speed forecasting
• ISSN: 1352-8505; 1573-3009
• DOI: 10.1007/s10651-021-00509-0

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.