DOI: 10.1002/2013JD020867
论文题名: Network design for heavy rainfall analysis
作者: Rietsch T. ; Naveau P. ; Gilardi N. ; Guillou A.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 23 起始页码: 13075
结束页码: 13086
语种: 英语
英文关键词: modeling
; optimal design
; precipitation
Scopus关键词: Learning algorithms
; Models
; Optimal systems
; Precipitation (chemical)
; Weather information services
; Complex objects
; Daily precipitations
; Distributional property
; Extreme value theory
; Optimal design
; Query by committees
; Statistical properties
; Weather stations
; Complex networks
; algorithm
; artificial neural network
; climatology
; design
; network analysis
; numerical model
; precipitation assessment
; rainfall
; temporal analysis
; weather station
; France
英文摘要: The analysis of heavy rainfall distributional properties is a complex object of study in hydrology and climatology, and it is essential for impact studies. In this paper, we investigate the question of how to optimize the spatial design of a network of existing weather stations. Our main criterion for such an inquiry is the capability of the network to capture the statistical properties of heavy rainfall described by the Extreme Value Theory. We combine this theory with a machine learning algorithm based on neural networks and a Query By Committee approach. Our resulting algorithm is tested on simulated data and applied to high-quality extreme daily precipitation measurements recorded in France at 331 weather stations during the time period 1980-2010. Key Points The QBC algorithm is useful to do optimal design in an extreme value context Stations located in the northern part of France are the least informative ©2013. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63117
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: Laboratoire des Sciences du Climat et de l'Environnement, CNRS, Gif-sur-Yvette, France; Institut Recherche Mathématique Avancée, UMR 7501, Université de Strasbourg et CNRS, 7 rue René Descartes, 67084 Strasbourg CEDEX, France; Commissariat À Énergie Atomique, CEA, Saint-Aubin, France
Recommended Citation:
Rietsch T.,Naveau P.,Gilardi N.,et al. Network design for heavy rainfall analysis[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(23)