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Advances in the stochastic modelling of satellite-derived rainfall estimates using a sparse calibration dataset

Greatrex, H., Grimes, D. and Wheeler, T. (2014) Advances in the stochastic modelling of satellite-derived rainfall estimates using a sparse calibration dataset. Journal of Hydrometeorology, 15. pp. 1810-1831. ISSN 1525-7541

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To link to this item DOI: 10.1175/JHM-D-13-0145.1

Abstract/Summary

As satellite technology develops, satellite rainfall estimates are likely to become ever more important in the world of food security. It is therefore vital to be able to identify the uncertainty of such estimates and for end users to be able to use this information in a meaningful way. This paper presents new developments in the methodology of simulating satellite rainfall ensembles from thermal infrared satellite data. Although the basic sequential simulation methodology has been developed in previous studies, it was not suitable for use in regions with more complex terrain and limited calibration data. Developments in this work include the creation of a multithreshold, multizone calibration procedure, plus investigations into the causes of an overestimation of low rainfall amounts and the best way to take into account clustered calibration data. A case study of the Ethiopian highlands has been used as an illustration.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Walker Institute
Faculty of Life Sciences > School of Agriculture, Policy and Development > Biodiversity, Crops and Agroecosystems Division > Crops Research Group
ID Code:39974
Publisher:American Meteorological Society

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