Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/8680
Title: Spatiotemporal Stochastic Simulation of Monthly Rainfall Patterns in the United Kingdom (1980–87)
Authors: Ekstrom, Marie 
Kyriakidis, Phaedon 
Chappell, Andrian 
Jones, Philip D. 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Keywords: Rainfall simulators;Rainfall;Metereological instruments;Spatial systems;Trend surface analysis
Issue Date: 15-Aug-2007
Source: Journal of climate, 2007, vol. 20, no. 16, pp. 4194-4210
Volume: 20
Issue: 16
Start page: 4194
End page: 4210
Journal: Journal of climate 
Abstract: With few exceptions, spatial estimation of rainfall typically relies on information in the spatial domain only. In this paper, a method that utilizes information in time and space and provides an assessment of estimate uncertainty is used to create a gridded monthly rainfall dataset for the United Kingdom over the period 1980–87. Observed rainfall profiles within the region were regarded as the sum of a deterministic temporal trend and a stochastic residual component. The parameters of the temporal trend components established at the rain gauges were interpolated in space, accounting for their auto- and cross correlation, and for relationships with ancillary spatial variables. Stochastic Gaussian simulation was then employed to generate alternative realizations of the spatiotemporal residual component, which were added to the estimated trend component to yield realizations of rainfall (after distributional corrections). In total, 40 realizations of rainfall were generated for each month of the 8-yr period. The methodology resulted in reasonably accurate estimates of rainfall but underestimated in northwest and north Scotland and northwest England. The cause for the underestimation was identified as a weak relationship between local rainfall and the spatial area average rainfall, used to estimate the temporal trend model in these regions, and suggestions were made for improvement. The strengths of this method are the utilization of information from the time and space domain, and the assessment of spatial uncertainty in the estimated rainfall values
URI: https://hdl.handle.net/20.500.14279/8680
ISSN: 15200442
DOI: 10.1175/JCLI4233.1
Rights: © American Meteorological Society
Type: Article
Affiliation : University of East Anglia 
University of California 
University of Salford 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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