Synoptic airflow and UK daily precipitation extremes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2010-01-23

AUTHORS

Douglas Maraun, Henning W. Rust, Timothy J. Osborn

ABSTRACT

We develop a vector generalised linear model to describe the influence of the atmospheric circulation on extreme daily precipitation across the UK. The atmospheric circulation is represented by three covariates, namely synoptic scale airflow strength, direction and vorticity; the extremes are represented by the monthly maxima of daily precipitation, modelled by the generalised extreme value distribution (GEV). The model parameters for data from 689 rain gauges across the UK are estimated using a maximum likelihood estimator. Within the framework of vector generalised linear models, various plausible models exist to describe the influence of the individual covariates, possible nonlinearities in the covariates and seasonality. We selected the final model based on the Akaike information criterion (AIC), and evaluated the predictive power of individual covariates by means of quantile verification scores and leave-one-out cross validation. The final model conditions the location and scale parameter of the GEV on all three covariates; the shape parameter is modelled as a constant. The relationships between strength and vorticity on the one hand, and the GEV location and scale parameters on the other hand are modelled as natural cubic splines with two degrees of freedom. The influence of direction is parameterised as a sine with amplitude and phase. The final model has a common parameterisation for the whole year. Seasonality is partly captured by the covariates themselves, but mostly by an additional annual cycle that is parameterised as a phase-shifted sine and accounts for physical influences that we have not attempted to explicitly model, such as humidity. More... »

PAGES

133-153

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10687-010-0102-x

DOI

http://dx.doi.org/10.1007/s10687-010-0102-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028663432


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