Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions

• Published in: Geomatics, natural hazards and risk Vol. 8; no. 2; pp. 207 - 224
• Main Authors: Friedland, Carol J., Joyner, T. Andrew, Massarra, Carol, Rohli, Robert V., Treviño, Anna M., Ghosh, Shubharoop, Huyck, Charles, Weatherhead, Mark
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 15.12.2017; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Anisotropy; Economics; Geostatistics; Interpolation; Interpolation methods; Kriging interpolation; Landscape; Mathematical models; Mountains; peak gust; Statistical analysis; Statistical methods; sustained winds; Weather station data; Weather stations; Wind; Wind direction; Wind speed; Windstorm
• ISSN: 1947-5705; 1947-5713
• DOI: 10.1080/19475705.2016.1185749

Windstorms result in significant damage and economic loss and are a major recurring threat in many countries. Estimating surface-level wind speeds resulting from windstorms is a complicated problem, but geostatistical spatial interpolation methods present a potential solution. Maximum sustained and peak gust weather station data from two historic windstorms in Europe were analyzed to predict surface-level wind speed surfaces across a large and topographically varied landscape. Disjunctively sampled maximum sustained wind speeds were adjusted to represent equivalent continuously sampled 10-minute wind speeds and missing peak gust station data were estimated by applying a gust factor to the recorded maximum sustained wind speeds. Wind surfaces were estimated based on anisotropic and isotropic kriging interpolation methodologies. The study found that anisotropic kriging is well-suited for interpolating wind speeds in meso- and macro-scale areas because it accounts for wind direction and trends in wind speeds across a large, heterogeneous surface, and resulted in interpolation surface improvement in most models evaluated. Statistical testing of interpolation error for stations stratified by geographic classification revealed that stations in coastal and/or mountainous locations had significantly higher prediction errors when compared with stations in non-coastal/non-mountainous locations. These results may assist in mitigating losses to structures due to excessive wind events.