Spatial verification using wavelet transforms: a review

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• 作者：Michael Weniger ; Florian Kapp and Petra Friederichs
• 刊名：Quarterly Journal of the Royal Meteorological Society
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：143
• 期：702
• 页码：120-136
• 全文大小：6785K
• ISSN：1477-870X

文摘

Due to the emergence of new high-resolution numerical weather prediction (NWP) models and the availability of new or more reliable remote sensing data, the importance of efficient spatial verification techniques is growing. Wavelet transforms offer an effective framework to decompose spatial data into separate (and possibly orthogonal) scales and directions. Most wavelet-based spatial verification techniques have been developed or refined in the last decade and concentrate on assessing forecast performance (i.e. forecast skill or forecast error) on distinct physical scales. Particularly during the last 5 years, a significant growth in meteorological applications could be observed. However, a comparison with other scientific fields, such as feature detection, image fusion, texture analysis or facial and biometric recognition, shows that there is still a considerable, currently unused potential to derive useful diagnostic information. In order to tap the full potential of wavelet analysis, we review the state-of-the-art in one- and two-dimensional wavelet analysis and its application, with emphasis on spatial verification. We further use a technique developed for texture analysis in the context of high-resolution quantitative precipitation forecasts, which is able to assess the structural characteristics of the precipitation fields and allows efficient clustering of ensemble data.