Modeling Jointly Low, Moderate, and Heavy Rainfall Intensities Without a Threshold Selection

• Activity Number: 496
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Transportation Statistics Interest Group
• Author(s): Raphael Huser* and Philippe Naveau
• Companies: KAUST and Laboratoire des Sciences du Climat et l'Environnement
• Keywords: Extreme event ; Precipitation data ; Probability weighted moments ; Threshold selection

Abstract

In the univariate framework, extreme events are classically defined as block maxima or excesses above a high threshold. These approaches yield, however, two main drawbacks. First, working with maxima or excesses implies that a lot of observations are completely disregarded: the dataset is artificially shopped down into two pieces, namely large observations and the rest. This necessarily imposes different statistical models for each piece. Second, it raises a non-trivial and very practical difficultly: how to choose the threshold, or equivalently the block size, which correctly discriminates between extreme and non-extreme data. In this work, we propose a statistical model for rainfall data, which is in compliance with Extreme-Value Theory on its upper and lower tails, while avoiding a threshold selection. Inference is performed using the fast method of probability weighed moments (PWMs). The flexibility of this new model and the efficiency of PWMs estimators are illustrated on simulated and real precipitation data.