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

Climate Research

Robert G. Crane¹  Bruce C. Hewitson¹ 

关键词: Upscaling;    Regional precipitation;    Regionalization;   

DOI  :  10.3354/cr025095

来源: Inter-Research Science Publishing

【 摘 要 】

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 thelocally 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 andidentifies 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 toproduce 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 aspronounced when there is a bias in the missing data for both high-precipitation and low-precipitation events.

【 授权许可】

Unknown