Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France

One of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial–temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are based on the concept of intra- and intercluster variances (like thek-means alg...

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Bibliographic Details
Published in:Journal of climate 2013-10, Vol.26 (20), p.7929-7937
Main Authors: Bernard, Elsa, Naveau, Philippe, Vrac, Mathieu, Mestre, Olivier
Format: Article
Language:English
Subjects:
Algorithms
Behavior
Bgi / Prodig
Climate
Climatology
Clustering
Context
Continental interfaces, environment
Datasets
Dependence
Earth, ocean, space
Estimators
Exact sciences and technology
External geophysics
Extreme value theory
Extreme values
Extreme weather
Geophysics. Techniques, methods, instrumentation and models
Heavy rainfall
Hydrologists
Hydrology
Information processing
Mathematical maxima
Meteorologists
Meteorology
Methods. Forecasting
Ocean, Atmosphere
Physical geography
Precipitation
Probability theory
Rain
Rainfall
Random variables
Sciences of the Universe
Silhouettes
Spatial dependencies
Statistical analysis
Statistical discrepancies
Statistical methods
Studies
Water in the atmosphere (humidity, clouds, evaporation, precipitation)
Weather stations
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Summary:One of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial–temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are based on the concept of intra- and intercluster variances (like thek-means algorithm or EOFs). As analyzing quantitative extremes like heavy rainfall has become more and more prevalent for climatologists and hydrologists during these last decades, finding spatial patterns with methods based on deviations from the mean (i.e., variances) may not be the most appropriate strategy in this context of studying such extremes. For practitioners, simple and fast clustering tools tailored for extremes have been lacking. A possible avenue to bridging this methodological gap resides in taking advantage of multivariate extreme value theory, a well-developed research field in probability, and to adapt it to the context of spatial clustering. In this paper, a novel algorithm based on this plan is proposed and studied. The approach is compared and discussed with respect to the classicalk-means algorithm throughout the analysis of weekly maxima of hourly precipitation recorded in France (fall season, 92 stations, 1993–2011).
ISSN:0894-8755
1520-0442
DOI:10.1175/JCLI-D-12-00836.1