Impact of TRMM data on a low-latency, high-resolution precipitation algorithm for flash-flood forecasting

Journal of Applied Meteorology and Climatology

Volumn 52, Issue 6, 2013, Pages 1379-1383

  Kuligowski, Robert J ^a   Li, Yaping ^a   Zhang, Yu ^b  

^a OFFICE OF RESEARCH AND APPLICATIONS   (United States)

^b NOAA EARTH SYSTEM RESEARCH LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRIGHTNESS TEMPERATURES; HYDROLOGIC FORECASTING; LOW EARTH ORBIT(LEO); POTENTIAL IMPACTS; PRECIPITATION RADAR; SATELLITE RAINFALLS; TRMM MICROWAVE IMAGER; TROPICAL RAINFALL MEASURING MISSIONS;

CALIBRATION; FLOOD CONTROL; GEOSTATIONARY SATELLITES; ORBITS; RAIN GAGES;

RAIN;

ALGORITHM; CALIBRATION; DATA SET; FLASH FLOOD; FLOOD FORECASTING; GEOSTATIONARY SATELLITE; HYDROMETEOROLOGY; MICROWAVE IMAGERY; ORBIT DETERMINATION; PIXEL; PRECIPITATION (CLIMATOLOGY); RADAR; SATELLITE IMAGERY; SATELLITE SENSOR; TRMM;

EID: 84880180108     PISSN: 15588424     EISSN: 15588432     Source Type: Journal    
DOI: 10.1175/JAMC-D-12-0107.1     Document Type: Article

References (28)

1. Adler, R. F., and A. J. Negri, 1988:Asatellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteor., 27, 30-51.
2. Ba, M. B., and A. Gruber, 2001: GOES multispectral rainfall algorithm (GMSRA). J. Appl. Meteor., 40, 1500-1514.
3. Bellerby, T., M. Todd, D. Kniveton, and C. Kidd, 2000: Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network. J. Appl. Meteor., 39, 2115-2128.
4. Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull.Amer.Meteor. Soc., 88, 47-64.
5. Ferraro, R. R., 1997: SSM/I derived global rainfall estimates for climatological applications. J. Geophys. Res., 102, 16 715-16 735.
6. Ferraro, R. R., and Coauthors, 2005: NOAA operational hydrological products derived from the Advanced Microwave Sounding Unit. IEEE Trans. Geosci. Remote Sens., 43, 1036-1049.
7. Hong, Y. A., 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, 1834-1852.
8. Hsu, K.-L., X. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 1176-1190.
9. 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.
10. Iguchi, T., T. Kozu, J. Kwiatkowski, R. Meneghini, J. Awaka, and K. Okamoto, 2009: Uncertainties in the rain profiling algorithm 1392 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 52 for theTRMMprecipitation radar. J. Meteor. Soc. Japan, 87A, 1-30.
11. Joyce, R. J., and P. Xie, 2011: Kalman filter-based CMORPH. J. Hydrometeor., 12, 1547-1563.
12. Kuligowski, R. J., 2002:Aself-calibrating GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112-130.
13. Kummerow, C., and Coauthors, 2001: The evolution of theGoddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 1801-1820.
14. Lin, Y., and K. E. Mitchell, 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. Preprints, 19th. Conf. on Hydrology, San Diego, CA, Amer.
15. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/pdfpapers/88063.pdf.]
16. McCollum, J., and R. Ferraro, 2003: Next generation of NOAA/ NESDIS TMI, SSM/I, and AMSR-E microwave land rainfall algorithms. J. Geophys. Res., 108, 8382, doi:10.1029/ 2001JD001512.
17. Miller, S. W., P. A. Arkin, and J. Royce, 2001: A combined microwave/ infrared rain rate algorithm. Int. J. Remote Sens., 22, 3285-3307.
18. Scofield, R. A., 2001: Comments on ''A quantitative assessment of the NESDIS Auto-Estimator.'' Wea. Forecasting, 16, 277-278.
19. Stellman, K. M., H. E. Fuelberg, R. Garza, and M. Mullusky, 2001: An examination of radar and rain gauge-derived mean areal precipitation over Georgia watersheds. Wea. Forecasting, 16, 133-144.
20. Tian, Y., and Coauthors, 2009: Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res., 114, D24101, doi:10.1029/2009JD011949.
21. Turk, F. J., E. E. Ebert, H. J. Oh, B.-J. Sohn, V. Levizzani, E. A. Smith, and R. Ferraro, 2003: Validation of an operational global precipitation analysis at short time scales. Preprints, Third Conf. on Artificial Intelligence/12th Conf. on Satellite Meteorology and Oceanography, Long Beach, CA, Amer. Meteor. Soc., JP1.2. [Available online at https://ams.confex. com/ams/pdfpapers/56865.pdf.]
22. Ushio, T., and Coauthors, 2009: A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteor. Soc. Japan, 87A, 137-151.
23. Vicente, G. A., R. A. Scofield, and W. P. Menzel, 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79, 1883-1898.
24. Vicente, G. A., R. A. Scofield, and W. P. Menzel, J. C. Davenport, and R. A. Scofield, 2002: The role of orographic and parallax corrections on real time high resolution satellite rain rate distribution. Int. J. Remote Sens., 23, 221-230.
25. Vila, D., R. Ferraro, and R. Joyce, 2007: Evaluation and improvement of AMSU precipitation retrievals. J. Geophys. Res., 112, D20119, doi:10.1029/2007JD008617.
26. Wolff, D. B., and B. L. Fisher, 2009: Assessing the relative performance of microwave-based satellite rain-rate retrievals using TRMM ground validation data. J. Appl. Meteor. Climatol., 48, 1069-1099.
27. Young, C. B., A. A. Bradley, W. F. Krajewski, and A. Kruger, 2000: Evaluating NEXRAD multisensor precipitation estimates for operational hydrologic forecasting. J. Hydrometeor., 1, 241-254.
28. Zhang, Y., D.-J. Seo, D. Kitzmiller, H. Lee, R. J. Kuligowski, D. Kim, and C. R. Kondragunta, 2013: Comparative strengths of SCaMPR satellite QPEs with and without TRMM ingest vs. gridded gauge-only analyses. J. Hydrometeor., 14, 153-170.