Open Access
Issue
E3S Web Conf.
Volume 7, 2016
3rd European Conference on Flood Risk Management (FLOODrisk 2016)
Article Number 18011
Number of page(s) 11
Section Forecasting and warning
DOI https://doi.org/10.1051/e3sconf/20160718011
Published online 20 October 2016
  1. Krzysztofowicz R. (2001). The case for probabilistic forecasting in hydrology. Journal of Hydrology, 249(1–4), 2–9. [Google Scholar]
  2. Ramos M. H., Van Andel S. J. and Pappenberger F. (2013). Do probabilistic forecasts lead to better decisions? Hydrology and Earth System Sciences, 17, 2219–2232. [CrossRef] [Google Scholar]
  3. Roulin E. (2007). Skill and relative economic value of medium-range hydrological ensemble predictions. Hydrology and Earth System Sciences, 11(2), 725–737. [CrossRef] [Google Scholar]
  4. Houdant B. (2004). Contribution à l’amélioration de la prévision hydrométéorologique opérationnelle. Pour l’usage des probabilités dans la communication entre acteurs. PhD thesis, ENGREF (AgroParisTech). [Google Scholar]
  5. Demeritt D., Nobert S., Cloke H. and Pappenberger F. (2010). Challenges in communicating and using ensembles in operational flood forecasting. Meteorological Applications, 17(2), 209–222 [CrossRef] [Google Scholar]
  6. Thielen J., Bartholmes J., Ramos M.-H. and de Roo A. (2009). The European Flood Alert System – Part 1: Concept and development. Hydrology and Earth System Sciences, 13(2), 125–140. [CrossRef] [Google Scholar]
  7. Demargne J., Wu L., Regonda S. K., Brown J. D., Lee H., He M., et al. (2014). The Science of NOAA’s Operational Hydrologic Ensemble Forecast Service. Bulletin of the American Meteorological Society, 95(1), 79–98. [CrossRef] [Google Scholar]
  8. Cloke H. L. and Pappenberger F. (2009). Ensemble flood forecasting: A review. Journal of Hydrology, 375(3–4), 613–626. [Google Scholar]
  9. Lorenz E. N. (1969). Atmospheric Predictability as Revealed by Naturally Occurring Analogues. Journal of the Atmospheric Sciences, 26(4), 636–646. [CrossRef] [Google Scholar]
  10. Buizza R., Milleer M. and Palmer T. N. (1999). Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quarterly Journal of the Royal Meteorological Society, 125(560), 2887–2908. [CrossRef] [Google Scholar]
  11. Bauer P., Thorpe A. and Brunet G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47–55. [CrossRef] [PubMed] [Google Scholar]
  12. Verkade J. S., Brown J. D., Reggiani P. and Weerts A. H. (2013). Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales. Journal of Hydrology, 501, 73–91. [CrossRef] [Google Scholar]
  13. Zalachori I., Ramos M.-H., Garçon R., Mathevet T. and Gailhard J. (2012). Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies. Advances in Science and Research, 8, 135–141. [CrossRef] [Google Scholar]
  14. Obled C., Bontron G. and Garçon R. (2002). Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach. Atmospheric Research, 63(3–4), 303–324. [Google Scholar]
  15. Marty R., Zin I. and Obled C. (2013). Sensitivity of hydrological ensemble forecasts to different sources and temporal resolutions of probabilistic quantitative precipitation forecasts: flash flood case studies in the Cévennes-Vivarais region (Southern France). Hydrological Processes, 27(1), 33–44. [CrossRef] [Google Scholar]
  16. Jolliffe I. T. and Stephenson D. B. (2011). Forecast Verification: A Practitioner’s Guide in Atmospheric Science (2nd Edition), Wiley. [Google Scholar]
  17. Welles E., Sorooshian S., Carter G. and Olsen B. (2007). Hydrologic Verification: A Call for Action and Collaboration. Bulletin of the American Meteorological Society, 88(4), 503–511. [CrossRef] [Google Scholar]
  18. Kang T.-H., Kim Y.-O. and Hong I.-P. (2010). Comparison of pre- and post-processors for ensemble streamflow prediction. Atmospheric Science Letters, 11(2), 153–159. [CrossRef] [Google Scholar]
  19. Bartholomes J. C., Thielen J., Ramos M. H. and Gentilini S. (2009). The European Flood Alert System EFAS – Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts. Hydrology and Earth System Sciences, 13(2), 141–53 [CrossRef] [Google Scholar]
  20. Brown J. D., Demargne J., Seo D.-J. and Liu Y. (2010). The Ensemble Verification System (EVS): A software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations. Environmental Modelling & Software, 25(7), 854–872. [CrossRef] [Google Scholar]
  21. Zappa M., Fundel F. and Jaun S. (2013). A ‘Peak- Box’ approach for supporting interpretation and verification of operational ensemble peak-flow forecasts. Hydrological Processes, 27(1), 117–131. [CrossRef] [Google Scholar]
  22. Box G. E. P., Jenkins G. M., Reinsel G. C. and Ljung G. M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Sons. [Google Scholar]
  23. Bompart P., Bontron G., Celie S. and Haond M. (2009). Une chaîne opérationnelle de prévision hydrométéorologique pour les besoins de la production hydroélectrique de la CNR. La Houille Blanche, 5, 54–60. [CrossRef] [EDP Sciences] [Google Scholar]
  24. Leutbecher M., & Palmer T. N. (2008). Ensemble forecasting. Journal of Computational Physics, 227(7), 3515–3539. [CrossRef] [Google Scholar]
  25. Park Y.-Y., Buizza R. and Leutbecher M. (2008). TIGGE: Preliminary results on comparing and combining ensembles. Quarterly Journal of the Royal Meteorological Society, 134(637), 2029–2050. [CrossRef] [Google Scholar]
  26. Bontron G. and Obled C. (2005). A probabilistic adaptation of meteorological model outputs to hydrological forecasting. La Houille Blanche, 1, 23–28. [Google Scholar]
  27. Ben Daoud A., Sauquet E., Lang M., Bontron G. and Obled C. (2011). Precipitation forecasting through an analog sorting technique: a comparative study. Advances in Geosciences, 29, 103–107. [CrossRef] [Google Scholar]
  28. Marty R., Zin I., Obled C., Bontron G. and Djerboua A. (2012). Toward Real-Time Daily PQPF by an analog sorting approach: application to flash-flood catchments. Journal of Applied Meteorology and Climatology, 51, 505–520. [Google Scholar]
  29. Chardon J., Hingray B., Favre A. C., Autin P., Gailhard J., Zin I., Obled C. (2014). Spatial Similarity and Transferability of Analog Dates for Precipitation Downscaling over France. Journal of Climate, 27(13), 5056–5074. [CrossRef] [Google Scholar]
  30. Ben Daoud A., Sauquet E., Bontron G., Obled C. and Lang M. (2016). Daily quantitative precipitation forecasts based on the analogue method: Improvements and application to a French large river basin. Atmospheric Research, 169, 147–59. [Google Scholar]
  31. Dee D. P., Uppala S. M., Simmons A. J., Berrisford P., Poli P., Kobayashi S., et al. (2011). The ERAInterim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. [Google Scholar]
  32. Saha S., Moorthi S., Pan H.-L., Wu X., Wang J., Nadiga S. et al. (2010). The NCEP Climate Forecast System Reanalysis. Bulletin of the American Meteorological Society, 91(8), 1015–1057. [Google Scholar]
  33. Clark M., Gangopadhyay S., Hay L., Rajagopalan B. and Wilby R. (2004). The Schaake Shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 5(1), 243–262 [CrossRef] [Google Scholar]
  34. Bontron G. (2004). Prévision quantitative des précipitations : adaptation probabiliste par recherche d’analogues. Utilisation des Réanalyses NCEP/NCAR et application aux précipitations du Sud-Est de la France. PhD thesis, Institut National Polytechnique de Grenoble. [Google Scholar]
  35. Trihn B. N., Thielen J., Thirel G. (2013). The reduction continuous rank probability score for evaluating discharge forecasts from hydrological ensemble prediction systems. Atmospheric Science Letters, 14(2), 61–65. [CrossRef] [Google Scholar]
  36. Efron B. and Tibshirani R. J. (1994). An Introduction to the Bootstrap, CRC Press. [Google Scholar]

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