Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions. Issue 2 (15th December 2017)

• Record Type: Journal Article
• Authors: Friedland, Carol J.; Joyner, T. Andrew; Massarra, Carol; Rohli, Robert V.; Treviño, Anna M.; Ghosh, Shubharoop; Huyck, Charles; Weatherhead, Mark
• Is Part Of: Geomatics, natural hazards & risk. Volume 8:Issue 2(2017)
• Page Start: 207
• Page End: 224
• Publication Date: 2017-12-15
• Subjects: Windstorm -- sustained winds -- peak gust -- anisotropy -- kriging interpolation; Geomatics -- Periodicals; Geomatics; Periodicals; 526.905
• DOI: 10.1080/19475705.2016.1185749
• Languages: English
• ISSNs: 1947-5705

Abstract

ABSTRACT: 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.