Articles | Volume 28, issue 20
https://doi.org/10.5194/hess-28-4539-2024
https://doi.org/10.5194/hess-28-4539-2024
Research article
 | 
23 Oct 2024
Research article |  | 23 Oct 2024

Assessing rainfall radar errors with an inverse stochastic modelling framework

Amy C. Green, Chris Kilsby, and András Bárdossy

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Cited articles

AghaKouchak, A., Bárdossy, A., and Habib, E.: Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula, Adv. Water Resour., 33, 624–634, https://doi.org/10.1016/j.advwatres.2010.02.010, 2010a. a
AghaKouchak, A., Habib, E., and Bárdossy, A.: A comparison of three remotely sensed rainfall ensemble generators, Atmos. Res., 98, 387–399, https://doi.org/10.1016/j.atmosres.2010.07.016, 2010b.  a
Atlas, D. and Banks, H. C.: The Interpretation of Microwave Reflections From Rainfall, J. Meteorol., 8, 271–282, https://doi.org/10.1175/1520-0469(1951)008<0271:tiomrf>2.0.co;2, 1951. a
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Berne, A. and Uijlenhoet, R.: Quantitative analysis of X-band weather radar attenuation correction accuracy, Nat. Hazards Earth Syst. Sci., 6, 419–425, https://doi.org/10.5194/nhess-6-419-2006, 2006. a
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Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well in generating realistic weather radar images visually for a large range of event types.
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