Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Technical note
01 Feb 2018
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Hydrology and Earth System Sciences (HESS).
Technical note: Analysis of observation uncertainty for flood assimilation and forecasting
Joanne A. Waller1, Javier García-Pintado1,2, David C. Mason3, Sarah L. Dance1, and Nancy K. Nichols1 1School of Mathematical, Physical and Computational Sciences, University of Reading, UK
2MARUM – Center for Marine Environmental Sciences and Department of Geosciences, University of Bremen, Germany
3School of Archaeology, Geography and Environmental Science, University of Reading, UK
Abstract. The assimilation of satellite-based water level observations (WLOs) into 2D hydrodynamic models can keep flood forecasts on track or be used for reanalysis to obtain improved assessments of previous flood footprints. In either case, satellites provide spatially dense observation fields, but with spatially correlated errors. To date, assimilation methods in flood forecasting either incorrectly neglect the spatial correlation in the observation errors or, in the best of cases, deal with it by thinning methods. These thinning methods result in a sparse set of observations whose error correlations are assumed to be negligible. Here, with a case study, we show that the assimilation diagnostics that make use of statistical averages of observation-minus-background and observation-minus-analysis residuals are useful to estimate error correlations in WLOs. The estimated correlations do not behave as expected; however, analysis shows that the diagnostic can also be used to highlight anomalous observation datasets. Accurate estimates of the observation error statistics can be used to support quality control protocols and provide insight into which observations it is most beneficial to assimilate. Furthermore, the understanding gained in this paper will contribute towards the correct assimilation of denser datasets.
Citation: Waller, J. A., García-Pintado, J., Mason, D. C., Dance, S. L., and Nichols, N. K.: Technical note: Analysis of observation uncertainty for flood assimilation and forecasting, Hydrol. Earth Syst. Sci. Discuss.,, in review, 2018.
Joanne A. Waller et al.
Joanne A. Waller et al.
Joanne A. Waller et al.


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