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A Practical Guide to a Space-Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data

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Satellite Rainfall Applications for Surface Hydrology

Abstract

Abstract For continual refinement of error models and their promotion in prototyping satellite-based hydrologic monitoring systems, a practical user guide that readers can refer to, is useful. In this chapter, we provide our readers with one such practical guide on a space-time stochastic error model called SREM2D (A Two Dimensional Satellite Rainfall Error Model) developed by Hossain and Anagnostou (IEEE Transactions on Remote Sensing and Geosciences, 44(6), pp. 1511–1522, 2006). Our guide first provides an overview of the philosophy behind SREM2D and emphasizes the need to flexibly interpret the error model as a collection of modifiable concepts always under refinement rather than a final tool. Users are encouraged to verify that the complexity and assumptions of error modeling are compatible with the intended application. The current limitations on the use of the error model as well as the various data quality control issues that need to be addressed prior to error modeling are also highlighted. Our motivation behind the compilation of this practical guide is that readers will learn to apply SREM2D by recognizing the strengths and limitations simultaneously and thereby minimize any black-box or unrealistic applications for surface hydrology.

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Acknowledgements

Support for this work was provided by the NASA New Investigator Program Award (NNX08AR32G) to the first author and NASA Precipitation Measurement Mission to authors Anagnostou and Hossain. Authors Nikolopoulos and Tang were supported by NASA Earth System Science Fellowship.

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Correspondence to Faisal Hossain .

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Hossain, F., Tang, L., Anagnostou, E.N., Nikolopoulos, E.I. (2010). A Practical Guide to a Space-Time Stochastic Error Model for Simulation of High Resolution Satellite Rainfall Data. In: Gebremichael, M., Hossain, F. (eds) Satellite Rainfall Applications for Surface Hydrology. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2915-7_9

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