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CR 25:95-107 (2003)  -  doi:10.3354/cr025095

Clustering and upscaling of station precipitation records to regional patterns using self-organizing maps (SOMs)

Robert G. Crane1,*, Bruce C. Hewitson2

1Department of Geography, The Pennsylvania State University, 103 Deike Building, University Park, Pennsylvania 16802, USA
2Department of Environmental and Geographical Sciences, University of Cape Town, Private Bag, Rondebosch 7701, South Africa

ABSTRACT: Self-organizing maps (SOMs), a particular application of artificial neural networks, are used to proportionately combine precipitation records of individual stations into a regional data set by extracting the common regional variability from the locally forced variability at each station. The methodology is applied to a 100 yr record of precipitation data for 104 stations in the Mid-Atlantic/Northeast United States region. The SOM combines stations with common precipitation characteristics and identifies precipitation regions that are consistent across a range of spatial scales. A variation of the SOM application identifies the temporal modes of the regional precipitation record and uses them to fill missing data in the station observations to produce a regional precipitation record. A test of the methodology with a complete data set shows that the Œmissing data¹ routine improves the regional signal when up to 80% of the data are missing from 80% of the stations. The improvement is almost as pronounced when there is a bias in the missing data for both high-precipitation and low-precipitation events.

KEY WORDS: Upscaling · Regional precipitation · Regionalization

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