Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 7
Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-3819-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3819-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 7
Research article
 | 
02 Jul 2021
Research article |  | 02 Jul 2021

Conditional simulation of spatial rainfall fields using random mixing: a study that implements full control over the stochastic process

Jieru Yan, Fei Li, András Bárdossy, and Tao Tao

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Cited articles

Adams, T.: Chapter 10 – Flood Forecasting in the United States NOAA/National Weather Service, in: Flood Forecasting, edited by: Adams, T. E. and Pagano, T. C., Academic Press, Boston, 249–310, https://doi.org/10.1016/B978-0-12-801884-2.00010-4, 2016. a
Bárdossy, A. and Hörning, S.: Random Mixing: An Approach to Inverse Modeling for Groundwater Flow and Transport Problems, Trans. Porous Media, 114, 241–259, https://doi.org/10.1007/s11242-015-0608-4, 2016. a, b
Bell, T. L.: A space-time stochastic model of rainfall for satellite remote-sensing studies, J. Geophys. Res.-Atmos., 92, 9631–9643, https://doi.org/10.1029/JD092iD08p09631, 1987. a
Berenguer, M., Sempere-Torres, D., and Pegram, G.: SBMcast – An ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation, J. Hydrol., 404, 226–240, 2011. a
Berne, A. and Krajewski, W. F.: Radar for hydrology: Unfulfilled promise or unrecognized potential?, Adv. Water Resour., 51, 357–366, 2013. a
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Accurate spatial precipitation estimates are important in various fields. An approach to simulate spatial rainfall fields conditioned on radar and rain gauge data is proposed. Unlike the commonly used Kriging methods, which provide a Kriged mean field, the output of the proposed approach is an ensemble of estimates that represents the estimation uncertainty. The approach is robust to nonlinear error in radar estimates and is shown to have some advantages, especially when estimating the extremes.
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