Bivariate Gaussian models for wind vectors in a distributional regression framework

Abstract

A new probabilistic post-processing method for wind vectors is presented in a distributional regression framework employing the bivariate Gaussian distribution. In contrast to previous studies all parameters of the distribution are simultaneously modeled, namely the means and variances for both wind components and also the correlation coefficient between them employing flexible regression splines. To capture a possible mismatch between the predicted and observed wind direction, ensemble forecasts of both wind components are included using flexible two-dimensional smooth functions. This encompasses a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction. The performance of the new method is tested for stations located in plains, mountain foreland, and within an alpine valley employing ECMWF ensemble forecasts as explanatory variables for all distribution parameters. The rotation-allowing model shows distinct improvements in terms of predictive skill for all sites compared to a baseline model that post-processes each wind component separately. Moreover, different correlation specifications are tested and small improvements compared to the model setup with no estimated correlation could be found for stations located in alpine valleys.

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Cited by in corpus (4)

• Statistical Postprocessing for Weather Forecasts -- Review, Challenges and Avenues in a Big Data World
• Generative machine learning methods for multivariate ensemble post-processing
• Cholesky-based multivariate Gaussian regression
• Circular Regression Trees and Forests with an Application to Probabilistic Wind Direction Forecasting