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Cawley, GC 
ORCID: https://orcid.org/0000-0002-4118-9095, Haylock, M, Dorling, SR, Goodess, C ORCID: https://orcid.org/0000-0002-7462-4479 and Jones, PD ORCID: https://orcid.org/0000-0001-5032-5493
(2003)
Statistical downscaling with artificial neural networks.
In: European Symposium on Artificial Neural Networks, 2003-04-23 - 2003-04-25.
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) |
|---|---|
| Uncontrolled Keywords: | sdg 13 - climate action ,/dk/atira/pure/sustainabledevelopmentgoals/climate_action |
| Faculty \ School: | Faculty of Science > School of Computing Sciences Faculty of Science > School of Environmental Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Climatic Research Unit Faculty of Science > Research Groups > Computational Biology Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 22 Jul 2011 13:07 |
| Last Modified: | 20 Jun 2023 14:34 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/21981 |
| DOI: |
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