Potential utility of the real-time TMPA-RT precipitation estimates in streamflow prediction

Journal of Hydrometeorology

Volumn 12, Issue 3, 2011, Pages 444-455

  Su, Fengge ^a,b   Gao, Huilin ^b   Huffman, George J ^c   Lettenmaier, Dennis P ^b  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b University of Washington   (United States)

^c NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

Microwave observations; Precipitation; Satellite observations; Streamflow; Surface observations

Indexed keywords

ACCURACY ASSESSMENT; AMSR-E; ESTIMATION METHOD; HYDROLOGICAL MODELING; OBSERVATIONAL METHOD; PERFORMANCE ASSESSMENT; PRECIPITATION ASSESSMENT; PREDICTION; RAINGAUGE; REAL TIME; SATELLITE DATA; STREAMFLOW;

LA PLATA BASIN;

EID: 79959627814     PISSN: 1525755X     EISSN: None     Source Type: Journal    
DOI: 10.1175/2010JHM1353.1     Document Type: Article

References (24)

1. Berbery, E. H., and V. R. Barros, 2002: The hydrologic cycle of the La Plata basin in South America. J. Hydrometeor., 3, 630-645.
2. Hong, Y., K.-L. Hsu, S. Sorooshian, and X. Gao, 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 1834-1853.
3. Hong, Y., K.-L. Hsu, H. Moradkhani, and S. Sorooshian, 2006: Uncertainty quantification of satellite precipitation estimation andMonteCarlo assessment of the error propagation into hydrologic response. Water Resour. Res., 42, W08421, doi:10.1029/2005WR004398.
4. 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.
5. Hossain, F., and E. N. Anagnostou, 2004: Assessment of current passive-microwave- and infrared-based satellite rainfall remote sensing for flood prediction. J. Geophys. Res., 109, D07102, doi:10.1029/2003JD003986.
6. Hossain, F., and D. P. Lettenmaier, 2006: Flood prediction in the future: Recognizing hydrologic issues in anticipation of the Global Precipitation Measurement mission. Water Resour. Res., 42, W11301, doi:10.1029/2006WR005202.
7. Hossain, F., N. Katiya, A. Wolf, and Y. Hong, 2007: The emerging role of satellite rainfall data in improving the hydro-political situation of flood monitoring in the under-developed regions of the world. Nat. Hazards, 43, 199-210, doi:10.1007/s11069-006-9094-x.
8. 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.
9. Huffman, G. J., R. F. Adler, D. T. Bolvin, and E. J. Nelkin, 2010: TheTRMM Multi-Satellite Precipitation Analysis (TMPA). Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer, 3-22.
10. 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.
11. Kidd, C., D. R. Kniveton, M. C. Todd, and T. J. Bellerby, 2003: Satellite rainfall estimation using combined passive microwave and infrared algorithms. J. Hydrometeor., 4, 1088-1104.
12. Kubota, T., and Coauthors, 2007: Global precipitation map using satellite-borne microwave radiometers by the GSMaP Project: Production and validation. IEEE Trans. Geosci. Remote Sens., 45, 2259-2275.
13. Liebmann, B., and D. Allured, 2005: Daily precipitation grids for South America. Bull. Amer. Meteor. Soc., 86, 1567-1570.
14. McCollum, J. R., W. F. Krajewski, R. R. Ferraro, and M. B. Ba, 2002: Evaluation of biases of satellite estimation algorithms over the continental United States. J. Appl. Meteor., 41, 1065-1080.
15. Nijssen, B., and D. P. Lettenmaier, 2004: Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites. J. Geophys. Res., 109, D02103, doi:10.1029/2003JD003497.
16. Scofield, R. A., and R. J. Kuligowski, 2003: Status and outlook of operational satellite precipitation algorithms for extremeprecipitation events. Wea. Forecasting, 18, 1037-1051.
17. Smith, E., and Coauthors, 2007: The International Global Precipitation Measurement (GPM) program and mission: An overview. Measuring Precipitation from Space: EURAINSAT and the Future, V. Levizzani and F. J. Turk, Eds., Advances in Global Change Research Series, Vol. 28, Springer, 611-653.
18. Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANNsystemsatellitebased estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035-2046.
19. Steiner, M., T. L. Bell, Y. Zhang, and E. F. Wood, 2003: Comparison of two methods for estimating the sampling-related uncertainty of satellite rainfall averages based on a large radar dataset. J. Climate, 16, 3759-3778.
20. Su, F., and D. P. Lettenmaier, 2009: Estimation of surface water budget of La Plata basin. J. Hydrometeor., 10, 981-998.
21. Su, F., H. Yang, and D. P. Lettenmaier, 2008: Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in the La Plata basin. J. Hydrometeor., 9, 622-640.
22. Tian,Y.D., andC.D. Peters-Lidard, 2007: Systematic anomalies over inland water bodies in satellite-based precipitation estimates. Geophys. Res. Lett., 34, L14403, doi:10.1029/2007GL030787.
23. Turk, F. J., and S. D. Miller, 2005: Toward improving estimates of remotely-sensed precipitation with MODIS/AMSR-E blended data techniques. IEEE Trans. Geosci. Remote Sens., 43, 1059-1069.
24. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. International Geophysics Series, Vol. 59, Academic Press, 467 pp.