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

Helen Greatrex, David Grimes, Tim Wheeler

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.Acase study of the Ethiopian highlands has been used as an illustration.

Original languageEnglish (US)
Pages (from-to)1810-1831
Number of pages22
JournalJournal of Hydrometeorology
Volume15
Issue number5
DOIs
StatePublished - Jan 1 2014

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calibration
rainfall
modeling
methodology
complex terrain
food security
satellite data
simulation

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

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Advances in the stochastic modeling of satellite-derived rainfall estimates using a sparse calibration dataset. / Greatrex, Helen; Grimes, David; Wheeler, Tim.

In: Journal of Hydrometeorology, Vol. 15, No. 5, 01.01.2014, p. 1810-1831.

Research output: Contribution to journalArticle

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