globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-3983-2018
论文题名:
Technical note: Assessment of observation quality for data assimilation in flood models
作者: Waller J.A.; García-Pintado J.; Mason D.C.; Dance S.L.; Nichols N.K.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:7
起始页码: 3983
结束页码: 3992
语种: 英语
Scopus关键词: Error statistics ; Errors ; Flood control ; Floods ; Quality control ; Water levels ; Correlated errors ; Correlation length scale ; Data assimilation ; Hydrodynamic model ; Observation errors ; Observation qualities ; Spatial correlations ; Statistical average ; Weather forecasting ; assessment method ; correlation ; data assimilation ; data quality ; data set ; flood ; flood forecasting ; hydrodynamics ; hydrological modeling ; quality control ; satellite altimetry ; two-dimensional modeling ; water level
英文摘要: 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 7 km is longer than the expected value of 250 m. 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. © Author(s) 2018.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163245
Appears in Collections:气候变化与战略

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作者单位: Waller, J.A., School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, United Kingdom; García-Pintado, J., School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, United Kingdom, MARUM - Center for Marine Environmental Sciences and Department of Geosciences, University of Bremen, Bremen, Germany; Mason, D.C., School of Archaeology, Geography and Environmental Science, University of Reading, Reading, United Kingdom; Dance, S.L., School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, United Kingdom; Nichols, N.K., School of Mathematical, Physical and Computational Sciences, University of Reading, Reading, United Kingdom

Recommended Citation:
Waller J.A.,García-Pintado J.,Mason D.C.,et al. Technical note: Assessment of observation quality for data assimilation in flood models[J]. Hydrology and Earth System Sciences,2018-01-01,22(7)
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