English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Although most models for rainfall extremes focus on point-wise values, it isaggregated precipitation over areas up to river catchment scale that is of themost interest. To capture the joint behaviour of precipitation aggregatesevaluated at different spatial scales, parsimonious and effective models mustbe built with knowledge of the underlying spatial process. Precipitation isdriven by a mixture of processes acting at different scales and intensities,e.g., convective and frontal, with extremes of aggregates for typical catchmentsizes arising from extremes of only one of these processes, rather than acombination of them. High-intensity convective events cause extreme spatialaggregates at small scales but the contribution of lower-intensity large-scalefronts is likely to increase as the area aggregated increases. Thus, to capturesmall to large scale spatial aggregates within a single approach requires amodel that can accurately capture the extremal properties of both convectiveand frontal events. Previous extreme value methods have ignored this mixturestructure; we propose a spatial extreme value model which is a mixture of twocomponents with different marginal and dependence models that are able tocapture the extremal behaviour of convective and frontal rainfall and morefaithfully reproduces spatial aggregates for a wide range of scales. Modellingextremes of the frontal component raises new challenges due to it exhibitingstrong long-range extremal spatial dependence. Our modelling approach isapplied to fine-scale, high-dimensional, gridded precipitation data. We showthat accounting for the mixture structure improves the joint inference onextremes of spatial aggregates over regions of different sizes.

Joint Estimation of Extreme Spatially Aggregated Precipitation at Different Scales through Mixture Modelling