MODELLING EXTREMES OF SPATIAL AGGREGATES OF PRECIPITATION USING CONDITIONAL METHODS

Richards, Jordan and Tawn, Jonathan and Brown, Simon (2022) MODELLING EXTREMES OF SPATIAL AGGREGATES OF PRECIPITATION USING CONDITIONAL METHODS. Annals of Applied Statistics. ISSN 1932-6157 (In Press)

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Abstract

Inference on the extremal behaviour of spatial aggregates of precipitation is important for quantifying river flood risk. There are two classes of previous approach, with one failing to ensure self-consistency in inference across different regions of aggregation and the other imposing highly restrictive assumptions. To overcome these issues, we propose a model for high-resolution precipitation data, from which we can simulate realistic fields and explore the behaviour of spatial aggregates. Recent developments have seen spatial extensions of the Heffernan and Tawn (2004) model for conditional multivariate extremes, which can handle a wide range of dependence structures. Our contribution is twofold: extensions and improvements of this approach and its model inference for high-dimensional data; and a novel framework for deriving aggregates addressing edge effects and sub-regions without rain.We apply our modelling approach to gridded East-Anglia, UK precipitation data. Return-level curves for spatial aggregates over different regions of various sizes are estimated and shown to fit very well to the data.

Item Type:
Journal Article
Journal or Publication Title:
Annals of Applied Statistics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
165302
Deposited By:
Deposited On:
31 Jan 2022 12:00
Refereed?:
Yes
Published?:
In Press
Last Modified:
22 May 2022 00:34