Stochastic rainfall modeling using spatio-temporal, multivariate and nonstationary trans-Gaussian random fields

Stochastic rainfall models are probabilistic tools able to simulate synthetic rainfall datasets with statistical properties that resemble those from observations, which makes them particularly suitable to assess the uncertainty of rainfall estimates and 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 (typically observations from a rain gauge network).

In this presentation I will explore how a new model of space-time, multivariate and non-stationary GRF 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 achieved by this flexible trans-Gaussian model I will examine how spatial non-stationarity can model orographic effects, and how multivariate modeling can be used to embed rainfall into a stochastic weather generator including five different variables (rainfall, temperature, wind, solar radiation and humidity).