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

TL;DR

This paper introduces space-time statistical models to improve wind speed forecast calibration from regional climate models, addressing biases and temporal variability for more accurate predictions.

Contribution

It develops novel space-time calibration models using trans-Gaussian fields and data augmentation, enhancing the accuracy of wind speed forecasts from NWP models.

Findings

Improved calibration of hourly wind speed forecasts in Southeastern Brazil.

Effective handling of asymmetric data with trans-Gaussian models.

Enhanced forecast accuracy through space-time modeling techniques.

Abstract

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.