. King Abdullah University of Science and Technology (Saudi Arabia)
Spatial analysis of U.S. precipitation extremes: a local likelihood approach for estimating complex tail dependence structures in high dimensions
Sala 2, Facultad de Matemáticas, Edificio Rolando Chuaqui, Campus San Joaquin, Pontificia Universidad Católica de Chile
In order to model the complex non-stationary dependence structure of precipitation extremes over the entire contiguous U.S., we propose a flexible local approach based on factor copula models. Specifically, by using Gaussian location mixture processes, we assume that there is a common, unobserved, random factor affecting the joint dependence of all measurements in small regional neighborhoods. Choosing this common factor to be exponentially distributed, one can show that the joint upper tail of the resulting copula is asymptotically equivalent to the (max-stable) Hüsler-Reiss copula or its Pareto process counterpart; therefore, the so-called exponential factor copula model captures tail dependence, but unlike the latter, has weakening dependence strength as events become more extreme, a feature commonly observed with precipitation data. In order to describe the stochastic behavior of extreme precipitation events over the U.S., we embed the exponential factor model in a more general non-stationary model, but we fit its local stationary counterpart to high threshold exceedances under the assumption of local stationarity. This allows us to gain in flexibility, while making inference for such a large and complex dataset feasible. Adopting a local censored likelihood approach, inference is made on a fine spatial grid, and local model fitting is performed taking advantage of distributed computing resources and of the embarrassingly parallel nature of this estimation method. The local model is efficiently fitted at all grid points, and uncertainty is measured using a block bootstrap procedure. Simulation results show that our approach is able to adequately capture complex dependencies on a local scale, therefore providing valuable input for regional risk assessment. Additionally, our data application shows that the model is able to flexibly represent extreme rainfall characteristics on a continental scale. A comparison between past and current U.S. rainfall data suggests that extremal dependence might be stronger nowadays than during the first half of the twentieth century in some areas, which has important implications on regional flood risk assessment.