Network design for heavy rainfall analysis

Théo Rietsch (1, 2) , P. Naveau (3, 1) , N. Gilardi (4) , Armelle Guillou (2)

1 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]
2 IRMA - Institut de Recherche Mathématique Avancée
3 ESTIMR - Extrèmes : Statistiques, Impacts et Régionalisation
4 CEA - Commissariat à l'énergie atomique et aux énergies alternatives

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.

Domaines: Océan, Atmosphère; Météorologie; Climatologie

Théo Rietsch, P. Naveau, N. Gilardi, Armelle Guillou. Network design for heavy rainfall analysis. Journal of Geophysical Research: Atmospheres, 2013, 118 (23), pp.13,075-13,086. ⟨10.1002/2013JD020867⟩. ⟨hal-03209744⟩