Inter-Research
Climate Research

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 'missing data' 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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Published in CR Vol. 25, No. 2 (2003) on December 5
Print ISSN: 0936-577X; Online ISSN: 1616-1572. Copyright © Inter-Research, Oldendorf/Luhe, 2003