Statistical downscaling with artificial neural networks

Cawley, GC, Haylock, M, Dorling, SR, Goodess, C and Jones, PD (2003) Statistical downscaling with artificial neural networks. In: Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003), 2003-04-23 - 2003-04-25.

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Abstract

Statistical downscaling methods seek to model the relationship between large scale atmospheric circulation, on say a European scale, and climatic variables, such as temperature and precipitation, on a regional or sub-regional scale. Downscaling is an important area of research as it bridges the gap between predictions of future circulation generated by General Circulation Models (GCMs) and the effects of climate change on smaller scales, which are often of greater interest to end-users. In this paper we describe a neural network based approach to statistical downscaling, with application to the analysis of events associated with extreme precipitation in the United Kingdom.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > Climatic Research Unit
Faculty of Science > School of Computing Sciences
Faculty of Science > School of Environmental Sciences
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Depositing User: Vishal Gautam
Date Deposited: 22 Jul 2011 14:07
Last Modified: 06 May 2019 00:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/21981
DOI:

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