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Volume 22, issue 7 | Copyright
Hydrol. Earth Syst. Sci., 22, 3983-3992, 2018
https://doi.org/10.5194/hess-22-3983-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Technical note 23 Jul 2018

Technical note | 23 Jul 2018

Technical note: Assessment of observation quality for data assimilation in flood models

Joanne A. Waller1, Javier García-Pintado1,2, David C. Mason3, Sarah L. Dance1, and Nancy K. Nichols1 Joanne A. Waller et al.
  • 1School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, UK
  • 2MARUM – Center for Marine Environmental Sciences and Department of Geosciences, University of Bremen, Bremen, Germany
  • 3School of Archaeology, Geography and Environmental Science, University of Reading, Reading, UK

Abstract. The assimilation of satellite-based water level observations (WLOs) into 2-D 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 average estimated correlation length scale of 7km is longer than the expected value of 250m. Furthermore, the correlations do not decrease monotonically; this unexpected behaviour is shown to be the result of assimilating some anomalous observations. 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. Therefore, the understanding gained in this paper will contribute towards the correct assimilation of denser datasets.

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