Abstract
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. A case study of the Ethiopian highlands has been used as an illustration.
Highlights
Much of sub-Saharan Africa relies on rain-fed cropping systems for food security; the timely and accurate reporting of weather observations can have sizable benefits for decision makers
It is likely that Satellite-based rainfall estimates (SRFEs) will gain ever more prominence in operational decision making over Africa
This is one of the underlying reasons why SRFEs are so important over the continent, so an important question is whether such a clustered dataset is adequate for a full uncertainty assessment across the region
Summary
Much of sub-Saharan Africa relies on rain-fed cropping systems for food security; the timely and accurate reporting of weather observations can have sizable benefits for decision makers. The data are temporally limited as only 5 years of data were available; it does not sample the full Ethiopian climate and could not be used to create a climatology This is a common occurrence in Africa, where, some individual countries do have dense rain gauge networks, it is often logistically difficult to access data, especially if one is interested in rainfall amounts across country borders. This is one of the underlying reasons why SRFEs are so important over the continent, so an important question is whether such a clustered dataset is adequate for a full uncertainty assessment across the region. It should be noted that there is a significant amount of additional rain gauge data available across Ethiopia that could greatly aid further research and validation
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