Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales

Journal of Hydrology

Volumn 501, Issue , 2013, Pages 73-91

  Verkade, J S ^a,b,c   Brown, J D ^d   Reggiani, P ^a,e   Weerts, A H ^a,f  

^a DELTARES   (Netherlands)

^b Ministry of Infrastructure and Environment   (Netherlands)

^c DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^d Hydrologic Solutions Limited   (United Kingdom)

^e RWTH AACHEN UNIVERSITY   (Germany)

^f WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

Bias correction; Ensemble forecasting; Post processing; Rhine; Uncertainty estimation; Verification

Indexed keywords

BIAS CORRECTION; ENSEMBLE FORECASTING; POST PROCESSING; RHINE; UNCERTAINTY ESTIMATION;

DECOMPOSITION; FORECASTING; VERIFICATION;

STREAM FLOW;

CLIMATE FORCING; ENSEMBLE FORECASTING; HYDROMETEOROLOGY; PRECIPITATION (CLIMATOLOGY); SPATIAL ANALYSIS; STREAMFLOW; UNCERTAINTY ANALYSIS; WEATHER FORECASTING;

GELDERLAND; LOBITH; NETHERLANDS; RHINE RIVER;

EID: 84882956988     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2013.07.039     Document Type: Article

References (62)

1. Bergström S., Singh V.P. The HBV model. Computer Models of Watershed Hydrology 1995, 443-476. Water Resources Publications, Highlands Ranch, Colorado, United States. V.P. Singh (Ed.).
2. Bogner K., Pappenberger F. Multiscale error analysis, correction, and predictive uncertainty estimation in a flood forecasting system. Water Resources Research 2011, 47(7):W07524.
3. Bowler N.E., Arribas A., Mylne K.R., Robertson K.B., Beare S.E. The MOGREPS short-range ensemble prediction system. Quarterly Journal of the Royal Meteorological Society 2008, 134(632):703-722.
4. Bradley A.A., Schwartz S.S. Summary verification measures and their interpretation for ensemble forecasts. Monthly Weather Review 2011, 139(9):3075-3089.
5. Bremnes J.B. Probabilistic forecasts of precipitation in terms of quantiles using NWP model output. Monthly Weather Review 2004, 132:338-347.
6. Brier G. Verification of forecasts expressed in terms of probability. Monthly Weather Review 1950, 78:1-3.
7. Brown J.D., Seo D.-J. A nonparametric postprocessor for bias correction of hydrometeorological and hydrologic ensemble forecasts. Journal of Hydrometeorology 2010, 11(3):642-665.
8. Brown J.D., Seo D.-J. Evaluation of a nonparametric post-processor for bias correction and uncertainty estimation of hydrologic predictions. Hydrological Processes 2013, 27(1):83-105.
9. Brown J.D., Demargne J., Seo D.-J., Liu Y. The ensemble verification system (EVS): a software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations. Environmental Modelling & Software 2010, 25(7):854-872.
10. Buizza R., Bidlot J.-R., Wedi N., Fuentes M., Hamrud M., Holt G., Vitart F. The new ECMWF VAREPS (variable resolution ensemble prediction system). Quarterly Journal of the Royal Meteorological Society 2007, 133(624):681-695.
11. Clark M., Gangopadhyay S., Hay L., Rajagopalan B., Wilby R. The schaake shuffle: a method for reconstructing space-time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology 2004, 5(1):243-262.
12. Cloke H., Pappenberger F. Ensemble flood forecasting: a review. Journal of Hydrology 2009, 375(3-4):613-626.
13. Gneiting T., Raftery A., Westveld A., Goldman T. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 2005, 133(5):1098-1118.
14. Green D.M., Swets J.A. Signal Detection Theory and Psychophysics 1966, John Wiley & Sons Inc., New York.
15. Hagedorn R. Using the ECMWF reforecast dataset to calibrate EPS forecasts. ECMWF Newsletter 2008, 117:8-13.
16. Hagedorn R., Hamill T., Whitaker J. Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. Part i: two-meter temperatures. Monthly Weather Review 2008, 136(7):2608-2619.
17. Hamill T.M. Verification of TIGGE multimodel and ECMWF reforecast-calibrated probabilistic precipitation forecasts over the contiguous united states*. Monthly Weather Review 2012, 140(7):2232-2252.
18. Hamill T., Whitaker J. Probabilistic quantitative precipitation forecasts based on reforecast analogs: theory and application. Monthly Weather Review 2006, 134(11):3209-3229.
19. Hamill T., Whitaker J., Mullen S. Reforecasts: an important dataset for improving weather predictions. Bulletin of the American Meteorological Society 2006, 87:33-46.
20. Hamill T., Hagedorn R., Whitaker J. Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts. part II: precipitation. Monthly Weather Review 2008, 136(7):2620-2632.
21. Hamill, T.M., Bates, G.T., Whitaker, J.S., Murray, D.R., Fiorino, M., Galarneau, T.J., Zhu, Y., Lapenta, W., 2013. NOAA's Second-Generation Global Medium-Range Ensemble Reforecast Data Set. Bulletin of the American Meteorological Society, early online release. <>. http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-12-00014.1.
22. Hashino T., Bradley A., Schwartz S. Evaluation of bias-correction methods for ensemble streamflow volume forecasts. Hydrology and Earth System Sciences 2007, 11:939-950.
23. Haylock M., Hofstra N., Klein Tank A., Klok E., Jones P., New M. A european daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research 2008, 113:D20119.
24. He X., Ng P. COBS: qualitatively constrained smoothing via linear programming. Computational Statistics 1999, 14:315-338.
25. Hersbach H. Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting 2000, 15:559-570.
26. Jarvis, A., Reuter, H., Nelson, A., Guevara, E., 2008. Hole-filled seamless SRTM data v4. <>. http://srtm.csi.cgiar.org.
27. Jolliffe, I.T., Stephenson, D.B. (Eds.), 2012. Forecast Verification: A Practitioner's Guide in Atmospheric Science, second ed.
28. Kang T.-H., Kim Y.-O., Hong I.-P. Comparison of pre- and post-processors for ensemble streamflow prediction. Atmospheric Science Letters 2010, 11(2):153-159.
29. Kelly K., Krzysztofowicz R. Precipitation uncertainty processor for probabilistic river stage forecasting. Water Resources Research 2000, 36(9).
30. Kleiber W., Raftery A.E., Baars J., Gneiting T., Mass C.F., Grimit E. Locally calibrated probabilistic temperature forecasting using geostatistical model averaging and local bayesian model averaging. Monthly Weather Review 2011, 139(8):2630-2649.
31. Krzysztofowicz R. Bayesian forecasting via deterministic model. Risk Analysis 1999, 19(4):739-749.
32. Krzysztofowicz R. The case for probabilistic forecasting in hydrology. Journal of Hydrology 2001, 249(1-4):2-9.
33. Madadgar S., Moradkhani H., Garen D. Towards improved post-processing of hydrologic forecast ensembles. Hydrological Processes 2012.
34. Murphy A. A new vector partition of the probability score. Journal of Applied Meteorology 1973, 12:595-600.
35. Pappenberger F., Thielen J., Del Medico M. The impact of weather forecast improvements on large scale hydrology: analysing a decade of forecasts of the european flood alert system. Hydrological Processes 2011, 25(7):1091-1113.
36. Photiadou C.S., Weerts A.H., van den Hurk B.J.J.M. Evaluation of two precipitation data sets for the rhine river using streamflow simulations. Hydrology and Earth System Sciences 2011, 15(11):3355-3366.
37. Quantum GIS Development Team, 2012. Quantum GIS Geographic Information System.
38. Raiffa H., Schlaifer R. Applied Statistical Decision Theory 1961, Harvard University Press.
39. Ramos M.H., van Andel S.J., Pappenberger F. Do probabilistic forecasts lead to better decisions?. Hydrology and Earth System Sciences 2013, 17:2219-2232.
40. R. Core Team, 2012. R: A Language and Environment for Statistical Computing. Vienna, Austria, ISBN 3-900051-07-0.
41. Reggiani P., Weerts A. A bayesian approach to decision-making under uncertainty: an application to real-time forecasting in the river rhine. Journal of Hydrology 2008, 356(1-2):56-69.
42. Reggiani P., Weerts A.H. Probabilistic quantitative precipitation forecast for flood prediction: an application. Journal of Hydrometeorology 2008, 9(1):76-95.
43. Schellekens J., Weerts A.H., Moore R.J., Pierce C.E., Hildon S. The use of MOGREPS ensemble rainfall forecasts in operational flood forecasting systems across england and wales. Advances in Geosciences 2011, 29:77-84.
44. Schmeits M.J., Kok K.J. A comparison between raw ensemble output, (modified) bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts. Monthly Weather Review 2010, 138(11):4199-4211.
45. Seo D., Herr H., Schaake J. A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction. Hydrology and Earth System Sciences Discussions 2006, 3(4):1987-2035.
46. Sloughter J., Raftery A., Gneiting T., Fraley C. Probabilistic quantitative precipitation forecasting using bayesian model averaging. Monthly Weather Review 2007, 135(9):3209-3220.
47. Stanski H., Wilson L., Burrows W. Survey of common verification methods in meteorology 1989, World Meteorological Organization Geneva.
48. Todini E. Role and treatment of uncertainty in real-time flood forecasting. Hydrological Processes 2004, 18(14):2743-2746.
49. Todini E. A model conditional processor to assess predictive uncertainty in flood forecasting. International Journal of River Basin Management 2008, 6(2):123-138.
50. Verkade J.S., Werner M.G.F. Estimating the benefits of single value and probability forecasting for flood warning. Hydrology and Earth System Sciences 2011, 15(12):3751-3765.
51. Verkade, J.S., Brown, J.D., Reggiani, P., Weerts, A., 2013. Dataset for "Post-Processing ECMWF Precipitation and Temperature Ensemble Reforecasts for Operational Hydrologic Forecasting at Various Spatial Scales" by Verkade et al., 2013. <>. http://dx.doi.org/10.4121/uuid:56637037-8197-472b-b143-2f87adf49abc.
52. Weerts A., Winsemius H., Verkade J. Estimation of predictive hydrological uncertainty using quantile regression: examples from the national flood forecasting system (England and wales). Hydrology and Earth System Sciences 2011, 15(1):255-265.
53. Werner M., Schellekens J., Gijsbers P., van Dijk M., van den Akker O., Heynert K. The Delft-FEWS Flow Forecasting System. Environmental Modelling & Software 2012.
54. Wilks D.S. A skill score based on economic value for probability forecasts. Meteorological Applications 2001, 8(2):209-219.
55. Wilks D. Statistical Methods in the Atmospheric Sciences 2006, Academic Press.
56. Wilks D.S. Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteorological Applications 2009, 16(3):361-368.
57. Wilks D.S., Hamill T.M. Comparison of ensemble-MOS methods using GFS reforecasts. Monthly Weather Review 2007, 135(6):2379-2390.
58. Wood A.W., Maurer E.P., Kumar A., Lettenmaier D.P. Long-range experimental hydrologic forecasting for the eastern united states. Journal of Geophysical Research 2002, 107(D20):4429.
59. Wu L., Seo D.-J., Demargne J., Brown J.D., Cong S., Schaake J. Generation of ensemble precipitation forecast from single-valued quantitative precipitation forecast for hydrologic ensemble prediction. Journal of Hydrology 2011, 399(3-4):281-298.
60. Yuan X., Wood E.F. Downscaling precipitation or bias-correcting streamflow? Some implications for coupled general circulation model (CGCM)-based ensemble seasonal hydrologic forecast. Water Resources Research 2012, 48(12).
61. Zalachori I., Ramos M.-H., Garon R., Mathevet T., Gailhard J. Statistical processing of forecasts for hydrological ensemble prediction: a comparative study of different bias correction strategies. Advances in Science and Research 2012, 8:135-141.
62. Zhao L., Duan Q., Schaake J., Ye A., Xia J. A hydrologic post-processor for ensemble streamflow predictions. Advances in Geosciences 2011, 29:51-59.