Séminaire de Probabilités et Statistique
Monday 16 March 2026 à 13:45 - UM, campus Triolet, bâtiment 9, salle 109 (1er étage)
Lionel Benoît (Inrae Avignon)
A rainy journey through (trans-)Gaussian Random Field modeling
Stochastic rainfall models are probabilistic tools able to simulate synthetic rainfall with statistical properties that resemble those from observations, which makes them particularly suitable to assess the uncertainty of rainfall estimates or to conduct sensitivity analysis of hydro-meteorological modeling chains. When the focus of the modeling is on spatial and temporal patterns, models based on space-time Gaussian random fields (GRFs) are often used because they enable modeling rainfall at any point of the space-time domain from sparse and heterogeneous data.
In this presentation I will explore how space-time, multivariate and non-stationary GRFs can be leveraged to improve stochastic rainfall modeling. A parametric transform function is combined with the GRF to account for rainfall intermittency and skewed marginal distribution, which results in a so-called trans-Gaussian (or meta-Gaussian) model. Among the many applications of trans-GRFs to rainfall modeling I will examine how to account for orographic effects, how to perform radar-rain gauge data fusion, and how to embed rainfall into multivariate stochastic weather generators.
Séminaire en salle 109, également retransmis sur zoom : https://umontpellier-fr.zoom.us/j/7156708132