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Network design for heavy rainfall analysis
Author(s) -
Rietsch T.,
Naveau P.,
Gilardi N.,
Guillou A.
Publication year - 2013
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2013jd020867
Subject(s) - precipitation , research object , extreme value theory , artificial neural network , computer science , meteorology , environmental science , data mining , artificial intelligence , statistics , geography , mathematics , regional science
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.