Editorial Type:
Article
Article Type:
Research Article

Probabilistic Wind Gust Forecasting Using Nonhomogeneous Gaussian Regression

Thordis L. Thorarinsdottir Institute of Applied Mathematics, Heidelberg University, Heidelberg, Germany

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Matthew S. Johnson Department of Statistics, Oregon State University, Corvallis, Oregon

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Print Publication:
01 Feb 2012
DOI:
https://doi.org/10.1175/MWR-D-11-00075.1
Page(s):
889–897
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Abstract

A joint probabilistic forecasting framework is proposed for maximum wind speed, the probability of gust, and, conditional on gust being observed, the maximum gust speed in a setting where only the maximum wind speed forecast is available. The framework employs the nonhomogeneous Gaussian regression (NGR) statistical postprocessing method with appropriately truncated Gaussian predictive distributions. For wind speed, the distribution is truncated at zero, the location parameter is a linear function of the wind speed ensemble forecast, and the scale parameter is a linear function of the ensemble variance. The gust forecasts are derived from the wind speed forecast using a gust factor, and the predictive distribution for gust speed is truncated according to its definition. The framework is applied to 48-h-ahead forecasts of wind speed over the North American Pacific Northwest obtained from the University of Washington mesoscale ensemble. The resulting density forecasts for wind speed and gust speed are calibrated and sharp, and offer substantial improvement in predictive performance over the raw ensemble or climatological reference forecasts.

Corresponding author address: Thordis L. Thorarinsdottir, Institute of Applied Mathematics, Heidelberg University, Im Neuenheimer Feld 294, D-69120 Heidelberg, Germany. E-mail: thordis@uni-heidelberg.de

Abstract

A joint probabilistic forecasting framework is proposed for maximum wind speed, the probability of gust, and, conditional on gust being observed, the maximum gust speed in a setting where only the maximum wind speed forecast is available. The framework employs the nonhomogeneous Gaussian regression (NGR) statistical postprocessing method with appropriately truncated Gaussian predictive distributions. For wind speed, the distribution is truncated at zero, the location parameter is a linear function of the wind speed ensemble forecast, and the scale parameter is a linear function of the ensemble variance. The gust forecasts are derived from the wind speed forecast using a gust factor, and the predictive distribution for gust speed is truncated according to its definition. The framework is applied to 48-h-ahead forecasts of wind speed over the North American Pacific Northwest obtained from the University of Washington mesoscale ensemble. The resulting density forecasts for wind speed and gust speed are calibrated and sharp, and offer substantial improvement in predictive performance over the raw ensemble or climatological reference forecasts.

Corresponding author address: Thordis L. Thorarinsdottir, Institute of Applied Mathematics, Heidelberg University, Im Neuenheimer Feld 294, D-69120 Heidelberg, Germany. E-mail: thordis@uni-heidelberg.de
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