Calibration and downscaling methods for quantitative ensemble precipitation forecasts

Weather and Forecasting

Volumn 25, Issue 6, 2010, Pages 1603-1627

  Voisin, Nathalie ^a   Schaake, John C ^b   Lettenmaier, Dennis P ^a  

^a University of Washington   (United States)

^b NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

Ensembles; Forecasting techniques; Precipitation

Indexed keywords

BIAS CORRECTION; DATA SETS; DOWN-SCALING; DOWNSCALING METHODS; ENSEMBLE PREDICTION SYSTEMS; ENSEMBLES; ERROR ESTIMATES; EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS; FLOOD FORECAST; FORECAST ERRORS; FORECAST RELIABILITY; FORECASTING TECHNIQUES; OHIO RIVER; PRECIPITATION; PRECIPITATION FORECAST; PRECIPITATION PATTERNS; SPATIAL DISAGGREGATION; SPATIAL INTERPOLATION; SPATIAL RESOLUTION; TROPICAL RAINFALL; UNGAUGED RIVERS; WEATHER FORECASTS;

ERRORS; FLOOD CONTROL; RAIN; RAIN GAGES; RIVERS; WATERSHEDS;

WEATHER FORECASTING;

CALIBRATION; DOWNSCALING; ENSEMBLE FORECASTING; FLOOD FORECASTING; PRECIPITATION (CLIMATOLOGY); QUANTITATIVE ANALYSIS; RIVER BASIN; SPATIAL RESOLUTION; WEATHER FORECASTING;

OHIO RIVER; UNITED STATES;

EID: 78650983778     PISSN: 08828156     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010WAF2222367.1     Document Type: Article

References (35)

1. Asante,K.O., R.M.Dezanove,G.Artan,R.Lietzow, and J. Verdin, 2007: Developing a flood monitoring system from remotely sensed data for the Limpopo Basin. IEEE Trans. Geosci. Remote Sens., 45, 1709-1714.
2. Berrocal, V. J., A. E. Raftery, and T. Gneiting, 2008: Probabilistic quantitative precipitation field forecasting using a two-stage spatial model. Ann. Appl. Stat., 2, 1170-1193.
3. Clark, M. P., and L. E. Hay, 2004: Use of medium-range numerical weather prediction model output to produce forecasts of streamflow. J. Hydrometeor., 5, 15-32.
4. Clark, M. P., S. Gangopadhyay, L. Hay, B. Rajagopalan, and R. Wilby, 2004: The Schaake shuffle: A method for reconstructing spacetime variability in forecasted precipitation and temperature fields. J. Hydrometeor., 5, 243-262.
5. Demargne, J., and Coauthors, 2009: Towards standard verification strategies for operational hydrologic forecasts. National Weather Service Hydrologic Forecast Verification Team Final Rep., 65 pp. [Available online at http://www.nws.noaa.gov/oh/rfcdev/docs/NWS-Hydrologic-Forecast-Verification-Team_Final-report_Sep09.pdf.]
6. Franz, K. J., H. C. Hartmann, S. Sorooshian, and R. Bales, 2003: Verification of National Weather Service ensemble streamflow predictions for water supply forecasting in the Colorado River basin. J. Hydrometeor., 4, 1105-1118.
7. Glahn, H. R., and D. A. Lowry, 1972: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 1203-1211.
8. Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta-RSM short-range ensemble forecasts. Mon. Wea. Rev., 125, 1312-1327.
9. Hamill, T. M., and J. S. Whitaker, 2006: Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Mon. Wea. Rev., 134, 3209-3229.
10. Hamill, T. M., J. S. Whitaker, and X. Wei, 2004: Ensemble re-forecasting: Improving medium-range forecast skill using retrospective forecasts. Mon. Wea. Rev., 132, 1434-1447.
11. Hersbach, H., 2000: Decomposition of the continuous ranked probability score for ensemble prediction systems. Wea. Forecasting, 15, 559-570.
12. Hong, Y., R. F. Adler, F. Hossain, S. Curtis, and G. J. Huffman, 2007: A first approach to global runoff simulation using satellite rainfall estimation.Water Resour. Res., 43, W08502, doi:10.1029/2006WR005739.
13. Hong, Y., R. F. Adler, and G. J. Huffman, 2009: Applications of TRMM-based multi-satellite precipitation estimation for global runoff simulation: Prototyping a global flood monitoring system. Satellite Rainfall Applications to Surface Hydrology, M. Gebremichael and F. Hossain Eds., 245-265.
14. Hopson, T. M., and P. J. Webster, 2010: A 1-10-day ensemble forecasting scheme for the major river basins of Bangladesh: Forecasting severe floods of 2003-07. J. Hydrometeor., 11, 618-641.
15. Hossain, F., and N. Katiyar, 2006: Improving flood forecasting in international river basins. Eos, Trans. Amer. Geophys. Union, 87, 49.
16. Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38-55.
17. Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P.Xie, 2004:CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487-503.
18. Krzysztofowicz, R., 1998: Probabilistic hydrometeorological forecasts: Toward a new era in operational forecasting. Bull. Amer. Meteor. Soc., 79, 243-251.
19. Maurer, E. P., A. W. Wood, J. C. Adam, and D. P. Lettenmaier, 2002: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J. Climate, 15, 3237-3251.
20. Panofsky, H. A., and G. W. Brier, 1968: Some Applications of Statistics to Meteorology. The Pennsylvania State University, 224 pp.
21. Pielke, R. A., Jr., M. W. Downton, and J. Z. Barnard Miller, 2002: Flood damage in the United States, 1926-2000: A reanalysis of National Weather Service estimates. UCAR, 86 pp. [Available online at www.flooddamagedata.org.]
22. Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155-1174.
23. Schaake, J., and Coauthors, 2007: Precipitation and temperature ensemble forecasts from single-value forecasts. Hydrol. Earth Syst. Sci. Discuss., 4, 655-717.
24. Seo, D.-J., 1998a: Real-time estimation of rainfall fields using rain gage data under fractional coverage conditions. J. Hydrol., 208, 25-36.
25. Seo, D.-J., 1998b: Real-time estimation of rainfall fields using radar rainfall and rain gage data. J. Hydrol., 208, 37-52.
26. Seo, D.-J., S. Perica, E. Welles, and J. C. Schaake, 2000: Simulation of precipitation fields from probabilistic quantitative forecast. J. Hydrol., 239, 203-229.
27. Shepard, D. S., 1984: Computer mapping: The SYMAP interpolation algorithm, Spatial Statistics and Models, G. L. Gaile and C. J. Willmott, Eds., D. Reidel, 133-145.
28. Sloughter, J. M., A. E. Raftery, T. Gneiting, and C. Fraley, 2007: Probabilistic quantitative precipitation forecasting using Bayesian model averaging. Mon. Wea. Rev., 135, 3209-3220.
29. Su, F., Y. Hong, and D. P. Lettenmaier, 2008: Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in La Plata Basin. J. Hydrometeor., 9, 622-640.
30. Talagrand, O., and R. Vautard, 1997: Evaluation of probabilistic prediction systems. Proc. ECMWF Workshop on Predictability, Reading, United Kingdom, ECMWF, 1-25.
31. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.
32. Wood, A. W., and D. P. Lettenmaier, 2006: A testbed for new seasonal hydrologic forecasting approaches in the western United States. Bull. Amer. Meteor. Soc., 87, 1699-1712.
33. Wood, A. W., and J. C. Schaake, 2008: Correcting errors in streamflow forecast ensemble mean and spread. J. Hydrometeor., 9, 132-148.
34. Wood, A. W., E. P. Maurer, A. Kumar, and D. P. Lettenmaier, 2002: Longrange experimental hydrologic forecasting for the eastern United States. J. Geophys. Res., 107, 4429, doi:10.1029/2001JD000659.
35. Yilmaz, K., R. F. Adler, Y. Tian, Y. Hong, and H. F. Pierce, 2010: Evaluation of a satellite-based global flood monitoring system. Int. J. Remote Sens., 31, 3763-3782.