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Advances in the Stochastic Modeling of Satellite-Derived Rainfall Estimates Using a Sparse Calibration Dataset

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Title Advances in the Stochastic Modeling of Satellite-Derived Rainfall Estimates Using a Sparse Calibration Dataset
 
Creator Greatrex, Helen
Grimes D
Wheeler, Tim
 
Subject climate change
agriculture
food security
rain
 
Description 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.
 
Date 2014-10-01
2015-09-16T17:00:33Z
2015-09-16T17:00:33Z
 
Type Journal Article
 
Identifier Greatrex H, Grimes D, Wheeler T. 2014. Advances in the Stochastic Modeling of Satellite-Derived Rainfall Estimates Using a Sparse Calibration Dataset. Journal of Hydrometeorology 15(5):1810-1831.
1525-755X
1525-7541
https://hdl.handle.net/10568/68182
https://doi.org/10.1175/JHM-D-13-0145.1
 
Language en
 
Rights Open Access
 
Publisher American Meteorological Society
 
Source Journal of Hydrometeorology