Similarity Retrieval Method for Regional Rainstorm Processes Based on Multidimensional Features
ZHAO Liang
WEI Tiexin
WANG Lirong
XIE Wenjuan
Abstract：For the assessment of regional rainstorm processes,disaster prevention and mitigation efforts,and decision-making services,it is crucial to retrieve historical cases with similar characteristics and compare their meteorological parameters.With a focus on the Beijing-Tianjin-Hebei region,we used daily precipitation data during 1961-2023 from 175 meteorological observation stations in this area,and identified 517 regional rainstorm processes.To measure the similarity of these processes,we proposed a similarity discrimination method for regional rainstorm processes based on multidimensional parameters.The Euclidean distance was used to measure attribute similarity based on parameters such as maximum process precipitation,maximum daily precipitation,influence range,duration,average intensity,and average range.The learned perceptual image patch similarity was used to measure spatial similarity based on the distribution of process precipitation,maximum daily precipitation,and average daily precipitation.The composite similarity score was calculated using a weighted summation of attribute and spatial similarity.The similarity analysis of the rainstorm process in July 2023 reveals that the three most similar rainstorm processes were in August 1963,August 1996,and July 2016,consistent with experts'empirical analyses.Compared with other methods,this method offers the broadest retrieval coverage and reasonable retrieval results,as validated by retrieval coverage rates and the random retrieval of rainstorms across different intensity levels.
Keywords：multidimensional parametersregional rainstormEuclidean distancelearned perceptual image patch similaritysimilarity retrieval
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 966-973 )