Open Access
| Issue |
E3S Web Conf.
Volume 7, 2016
3rd European Conference on Flood Risk Management (FLOODrisk 2016)
|
|
|---|---|---|
| Article Number | 18022 | |
| Number of page(s) | 9 | |
| Section | Forecasting and warning | |
| DOI | https://doi.org/10.1051/e3sconf/20160718022 | |
| Published online | 20 October 2016 | |
- Ricci S., Piacentini A., Thual O., Le Pape E. and Jonville G., (2011). Correction of upstream flow and hydraulics state with a data assimilation in the context of flood forecasting. Hydrol. Earth Syst. Sci 15, 1–21. [Google Scholar]
- Habert J., Ricci S., Thual O., Le Pape E., Piacentini A., Goutal N., Jonville G., Rochoux M., (2016). Reduction of the uncertainties in the water level-discharge relation of a 1D hydraulic model in the context of operational flood forecasting. Journal of Hydrology, 532, 52–64. [CrossRef] [Google Scholar]
- Barthélémy S., Ph D (2015) Assimilation de données ensembliste et couplage de modèles hydrauliques 1D-2D pour la prévision des crues en temps réel. Application au réseau hydraulique Adour maritime, Université de Toulouse, France. [Google Scholar]
- Goutal N. and Maurel F., (2002). A finite volume solver for 1D shallow water equations applied to an actual river. Int. J. Numer. Meth. Fluids, 38 (2), 1–19. [Google Scholar]
- Beven K., Freer J., 2001. A dynamic topmodel. Hydrol. Process., 15, 1993–2011. [Google Scholar]
- Boyaval S. (2012). A fast Monte-Carlo method with a Reduced Basis of Control Variates applied to Uncertainty Propagation and Bayesian Estimation. CMAME, 241–244. [Google Scholar]
- Wiener P. (1938). The Homogeneous Chaos. American Journal of Mathematics. Vol. 60(4), 897–936. [Google Scholar]
- Ghanem R. and Spanos P. (1991). Stochastic finite elements, A spectral approach, Dover. [Google Scholar]
- Evensen G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162. [Google Scholar]
- Burgers G., Jan Van Leeuwen P. and Evensen G. (1998). Analysis Scheme in the Ensemble Kalman Filter. Monthly Weather Review, 126(6), 1719–1724. [CrossRef] [Google Scholar]
- Anderson J. L. and Anderson S. L. (1999). A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts, Monthly Weather Review, 127, 2741–2758. [Google Scholar]
- Anderson J. L. (2007). An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, Series A, Dynamic Meteorology and Oceanography, 59, 210–224. [CrossRef] [Google Scholar]
- Desroziers G., Berre L., Chaonik B. and Poli P. (2005). Diagnosis of observation, background and analysiserror statistics in observation space. Quarterly Journal of the Royal Meteorological Society, 131(2005), 3385–3396. [CrossRef] [Google Scholar]
- Buis S., Piacentini A. and Déclat D. (2006). PALM: a computational framework for assembling highperformance computing applications. Concurrency and Computation: Practice and experience, 18(2), 231–245. [CrossRef] [Google Scholar]
- Li H., Kalnay E. and Miyoshi T. (2009). Simultaneous estimation of covariance inflation and observation errors within an Ensemble Kalman Filter. Quarterly Journal of the Royal Meteorological Society, 135(February), 523–533. [Google Scholar]
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