Evaluating the utility of multispectral information in delineating the areal extent of precipitation

Journal of Hydrometeorology

Volumn 10, Issue 3, 2009, Pages 684-700

  Behrangi, Ali ^a   Hsu, Kuo Lin ^a   Imam, Bisher ^a   Sorooshian, Soroosh ^a   Kuligowski, Robert J ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b OFFICE OF RESEARCH AND APPLICATIONS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; EOS; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; PRECIPITATION (CLIMATOLOGY);

EID: 68049111251     PISSN: 1525755X     EISSN: None     Source Type: Journal    
DOI: 10.1175/2009JHM1077.1     Document Type: Article

References (60)

1. Ackerman, S. A., 1996: Global satellite observations of negative brightness temperature differences between 11 and 6.7 μm. J. Atmos. Sci., 53, 2803-2812.
2. Adler, R. F., and A. J. Negri, 1988: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteor., 27, 30-51.
3. Adler, R. F., A. J. Negri, P. R. Keehn, and I. M. Hakkarinen, 1993: Estimation of monthly rainfall over Japan and surrounding waters from a combination of low-orbit microwave and geosynchronous IR data. J. Appl. Meteor., 32, 335-356.
4. Arkin, P. A., and P. Xie, 1994: The Global Precipitation Climatology Project: First algorithm intercomparison project. Bull. Amer. Meteor. Soc., 75, 401-419.
5. Arking, A., and J. D. Childs, 1985: Retrieval of cloud cover parameters from multispectral satellite images. J. Climate Appl. Meteor., 24, 322-333.
6. Ba, M. B., and A. Gruber, 2001: GOES multispectral rainfall algorithm (GMSRA). J. Appl. Meteor., 40, 1500-1514.
7. Capacci, D., and B. J. Conway, 2005: Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks. Meteor. Appl., 12, 291-305.
8. Cheng, M., and R. Brown, 1995: Delineation of precipitation areas by correlation of Meteosat visible and infrared data with radar data. Mon. Wea. Rev., 123, 2743-2757.
9. Cheng, M., R. Brown, and C. C. Collier, 1993: Delineation of precipitation areas using Meteosat infrared and visible data in the region of the United Kingdom. J. Appl. Meteor., 32, 884-898.
10. Gilbert, G. F., 1884: Finley's tornado predictions. Amer. Meteor. J., 1, 166-172.
11. Grassotti, C., and L. Garand, 1994: Classification-based rainfall estimation using satellite data and numerical forecast model fields. J. Appl. Meteor., 33, 159-178.
12. Griffith, C. G., W. L. Woodley, P. G. Grube, D. W. Martin, J. Stout, and D. N. Sikdar, 1978: Rain estimation from geosynchronous satellite imagery - Visible and infrared studies. Mon. Wea. Rev., 106, 1153-1171.
13. 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.
14. Hsu, K.-L., H. V. Gupta, X. Gao, and S. Sorooshian, 1999: Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation. Water Resour. Res., 35, 1605-1618.
15. Huffman, G. J., R. F. Adler, M. M. Morrissey, D. T. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 36-50.
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. Inoue, T., 1987: A cloud type classification with NOAA 7 split-window measurements. J. Geophys. Res., 92, 3991-4000.
18. Inoue, T., and K. Aonashi, 2000: A comparison of cloud and rainfall information from instantaneous visible and infrared scanner and precipitation radar observations over a frontal zone in East Asia during June 1998. J. Appl. Meteor., 39, 2292-2301.
19. Jolliffe, I. T., and D. B. Stephenson, 2003: Forecast Verification: A Practitioner's Guide in Atmospheric Science. John Wiley & Sons, 240 pp.
20. 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.
21. 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.
22. King, P. W. S., W. D. Hogg, and P. A. Arkin, 1995: The role of visible data in improving satellite rain-rate estimates. J. Appl. Meteor., 34, 1608-1621.
23. Kohonen, T., 1982: Self-organized formation of topologically correct feature maps. Biol. Cybern., 43, 59-69.
24. Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112-130.
25. Kummerow, C., and L. Giglio, 1995: A method for combining passive microwave and infrared rainfall observations. J. Atmos. Oceanic Technol., 12, 33-45.
26. Kummerow, C., W. S. Olson, and L. Giglio, 1996: A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors. IEEE Trans. Geosci. Remote Sens., 34, 1213-1232.
27. Kurino, T., 1997: A satellite infrared technique for estimating "deep/shallow" precipitation. Adv. Space. Res., 19, 511-514.
28. Lensky, I. M., and D. Rosenfeld, 2003: A night-rain delineation algorithm for infrared satellite data based on microphysical considerations. J. Appl. Meteor., 42, 1218-1226.
29. Levizzani, V., F. Porcú, F. S. Marzano, A. Mugnai, E. A. Smith, and F. Prodi, 1996: Investigating a SSM/I microwave algorithm to calibrate METEOSAT infrared instaneous rainrate estimates. Meteor. Appl., 3, 5-17.
30. 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. Meteor. Soc., 1.2. [Available online at http://ams.confex.com/ams/pdfpapers/83847.pdf.]
31. Lovejoy, S., and G. L. Austin, 1979: The delineation of rain areas from visible and IR satellite data for GATE and midlatitudes. Atmos.-Ocean, 17, 77-92.
32. Maddox, R. A., J. Zhang, J. J. Gourley, and K. W. Howard, 2002: Weather radar coverage over the contiguous United States. Wea. Forecasting, 17, 927-934.
33. Martin, D. W., R. A. Kohrs, F. R. Mosher, C. M. Medaglia, and C. Adamo, 2008: Over-ocean validation of the global convective diagnostic. J. Appl. Meteor. Climatol., 47, 525-543.
34. Miller, S. W., P. A. Arkin, and R. Joyce, 2001: A combined microwave/ infrared rain rate algorithm. Int. J. Remote Sens., 22, 3285-3307.
35. Minnis, P., and E. F. Harrison, 1984: Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data. 1. Analysismethod. J. Climate Appl. Meteor., 23, 993-1011.
36. Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory. J. Atmos. Sci., 47, 1878-1893.
37. Nakajima, T., M. D. King, J. D. Spinhirne, and L. F. Radke, 1991: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part II: Marine stratocumulus observations. J. Atmos. Sci., 48, 728-751.
38. Negri, A. J., and R. F. Adler, 1987a: Infrared and visible satellite rain estimation. Part I: A grid cell approach. J. Climate Appl. Meteor., 26, 1553-1564.
39. Negri, A. J., and R. F. Adler, 1987b: Infrared and visible satellite rain estimation. Part II: A cloud definition approach. J. Climate Appl. Meteor., 26, 1565-1576.
40. O'Sullivan, F., C. H. Wash, M. Stewart, and C. E. Motell, 1990: Rain estimation from infrared and visible GOES satellite data. J. Appl. Meteor., 29, 209-223.
41. Pilewskie, P., and S. Twomey, 1987: Cloud phase discrimination by reflectance measurements near 1.6 and 2.2 μm. J. Atmos. Sci., 44, 3419-3420.
42. Rosenfeld, D., and G. Gutman, 1994: Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data. Atmos. Res., 34, 259-283.
43. Rosenfeld, D., and I. M. Lensky, 1998: Satellite-based insights into precipitation formation processes in continental and maritime convective clouds. Bull. Amer. Meteor. Soc., 79, 2457-2476.
44. Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570-575.
45. Schmetz, J., S. A. Tjemkes, M. Gube, and L. Van de berg, 1997: Monitoring deep convection and convective overshooting with METEOSAT. Adv. Space Res., 19, 433-441.
46. Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035-2046.
47. Tapiador, F. J., C. Kidd, K.-L. Hsu, and F. Marzano, 2004: Neural networks in satellite rainfall estimation. Meteor. Appl., 11, 83-91.
48. Tian, Y., C. D. Peters-Lidard, B. J. Choudhury, and M. Garcia, 2007: Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications. J. Hydrometeor., 8, 1165-1183.
49. Tjemkes, S., L. van de Berg, and J. Schmetz, 1997: Warm water vapor pixels over high clouds as observed by METEOSAT. Beitr. Phys. Atmos., 70, 15-21.
50. Todd, M. C., C. Kidd, D. Kniveton, and T. J. Bellerby, 2001: A combined satellite infrared and passive microwave technique for estimation of small-scale rainfall. J. Atmos. Oceanic Technol., 18, 742-755.
51. Tsonis, A. A., 1984: On the separability of various classes from the GOES visible and infrared data. J. Appl. Meteor., 23, 1393-1410.
52. Tsonis, A. A., 1988: Single thresholding and rain area delineation from satellite imagery. J. Appl. Meteor., 27, 1302-1306.
53. Tsonis, A. A., and G. A. Isaac, 1985: On a new approach for instantaneous rain area delineation in the midlatitudes using GOES data. J. Climate Appl. Meteor., 24, 1208-1218.
54. Turk, F. J., J. Hawkins, E. A. Smith, F. S. Marzano, A. Mugnai, and V. Levizzani, 2000: Combining SSM/I, TRMM and infrared geostationary satellite data in a near-realtime fashion for rapid precipitation updates: Advantages and limitations. Proc. EUMETSAT Meteorological Satellite Data Users' Conf., Vol. 2, Bologna, Italy, EUMETSAT, 705-707.
55. Turk, F. J., E. E. Ebert, H.-J. Oh, B.-J. Sohn, V. Levizzani, E. A. Smith, and R. R. Ferraro, 2003: Validation of an operational global precipitation analysis at short time scales. Preprints, 12th Conf. on Satellite Meteorology and Oceanography, Long Beach, CA, Amer. Meteor. Soc., JP1.2. [Available online at http://ams.confex.com/ams/pdfpapers/ 56865.pdf.]
56. 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.
57. Weng, F. Z., L. M. Zhao, R. R. Ferraro, G. Poe, X. F. Li, and N. C. Grody, 2003: Advanced microwave sounding unit cloud and precipitation algorithms. Radio Sci., 38, 8068, doi:10.1029/2002RS002679.
58. Wu, R., J. A. Weinman, and R. T. Chin, 1985: Determination of rainfall rates from GOES satellite images by a pattern recognition technique. J. Atmos. Oceanic Technol., 2, 314-330.
59. Xu, L., X. Gao, S. Sorooshian, P. A. Arkin, and B. Imam, 1999: A microwave infrared threshold technique to improve the GOES precipitation Index. J. Appl. Meteor., 38, 569-579.
60. Zhao, L. M., and F. Z. Weng, 2002: Retrieval of ice cloud parameters using the advanced microwave sounding unit. J. Appl. Meteor., 41, 384-395.