Cross-Correlation Modeling of European Windstorms: A Cokriging Approach for Optimizing Surface Wind Estimates

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Maximum sustained and peak gust winds from eighteen European windstorms over the last 25 years were analyzed previously to develop surface-level wind predictions across a large and topographically varied landscape based on an anisotropic kriging interpolation methodology for meteorological station data. Results suggested that coastal and mountainous areas experience the highest wind speeds and highest variability over short distances, resulting in the highest errors across concurrent interpolated surfaces. This study utilizes covariates in conjunction with cokriging to investigate the use of cokriging as a method of improvement through the interpolation of five windstorms that impacted both the Alps region and the topographically-varied coastal regions of Western Europe. Results show that cokriging improves isotach interpolation for windstorms in 8 out of 10 models by reducing root mean square error and the total number of high-error stations, primarily in coastal and mountainous areas. Land cover alone contributed to the greatest model improvement in a majority of the models, while aspect and elevation (singularly and collectively) also improved models when compared to original kriging models. Improved surface interpolation is critical for improved understanding of macro-scale windstorm patterns and resulting damage, thus improving risk and vulnerability estimates.