Daytime precipitation estimation using bispectral cloud classification system

Journal of Applied Meteorology and Climatology

Volumn 49, Issue 5, 2010, Pages 1015-1031

  Behrangi, Ali ^a,b   Hsu, Koulin ^a   Imam, Bisher ^a   Sorooshian, Soroosh ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Classification; Clouds; Precipitation; Spectral models

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLOUD ALBEDO; CLOUD CLASSIFICATION; CLOUD CLASSIFICATION SYSTEMS; CLOUD SEGMENTATION; CLOUD TYPES; CORRELATION COEFFICIENT; EQUITABLE THREAT SCORE; GEOSTATIONARY OPERATIONAL ENVIRONMENTAL SATELLITES; GRID SCALE; GRID-BOX; INTEGRATED TECHNIQUES; MULTI-DIMENSIONAL HISTOGRAMS; MULTI-SPECTRAL ANALYSIS; PERFORMANCE MEASURE; PERSIANN; PRECIPITATION ESTIMATION; PRECIPITATION ESTIMATION FROM REMOTELY SENSED INFORMATION; RAIN ESTIMATION; RAIN RATES; RAINFALL ESTIMATIONS; SPECTRAL MODELS; TEXTURAL FEATURE; THIN CIRRUS;

CLOUDS; ESTIMATION; GEOSTATIONARY SATELLITES; IMAGE MATCHING; NEURAL NETWORKS; RAIN; REMOTE SENSING; SOLAR RADIATION;

INFORMATION USE;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CLOUD CLASSIFICATION; CORRELATION; GOES; HISTOGRAM; IMAGE RESOLUTION; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; PRECIPITATION ASSESSMENT; REMOTE SENSING; SATELLITE DATA; SPECTRAL ANALYSIS;

EID: 77955562846     PISSN: 15588424     EISSN: None     Source Type: Journal    
DOI: 10.1175/2009JAMC2291.1     Document Type: Article

References (51)

1. 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.
2. Adler, R. F., C. Kidd, G. Petty, M. Morissey, and H. M. Goodman, 2001: Intercomparison of global precipitation products: The third Precipitation Intercomparison Project (PIP-3). Bull. Amer. Meteor. Soc., 82, 1377-1396.
3. Ba, M. B., and A. Gruber, 2001: GOES multispectral rainfall algorithm (GMSRA). J. Appl. Meteor., 40, 1500-1514.
4. Behrangi, A., K.-L. Hsu, B. Imam, S. Sorooshian, G. J. Huffman, and R. J. Kuligowski, 2009a: PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis. J. Hydrometeor., 10, 1414-1429.
5. Behrangi, A., K.-L. Hsu, B. Imam, S. Sorooshian, and R. J. Kuligowski, 2009b: Evaluating the utility of multi-spectral information in delineating the areal extent of precipitation. J. Hydrometeor., 10, 684-700.
6. Cheng, M., R. Brown, and C. G. 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.
7. Cotton, W. R., and R. A. Anthes, 1989: Storm and Cloud Dynamics. Academic Press, 883 pp.
8. Duda, R., and P. Hart, 1973: Pattern Classification and Scene Analysis. John Wiley and Sons, 482 pp.
9. Ebert, E. E., M. J. Manton, P. A. Arkin, R. J. Allam, C. E. Holpin, and A. Gruber, 1996: Results from the GPCP Algorithm In-tercomparison Programme. Bull. Amer. Meteor. Soc., 77, 2875-2887.
10. Everitt, B. S., 1993: Cluster Analysis. 3rd ed. Halsted Press, 170 pp.
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. Hollinger, J. P., R. C. Lo, G. A. Poe, R. Savage, and J. L. Peirce, 1987: Special Sensor Microwave/Imager: User's Guide. Naval Research Laboratory, 177 pp.
14. Hong, Y., K. L. Hsu, S. Sorooshian, and X. G. Gao, 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 1834-1852.
15. Hong, Y., K. L. Hsu, S. Sorooshian, and X. G. Gao, 2005: Improved representation of diurnal variabilityof rainfall retrieved from the Tropical Rainfall Measurement Mission Microwave Imager adjusted Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) system. J. Geophys. Res., 110, D06102, doi:10.1029/2004JD005301.
16. Hou, A., G. S. Jackson, C. Kummerow, and J. M. Shepherd, 2008: Global precipitation measurement. Precipitation: Advances in Measurement, Estimation, and Prediction, S. Michaelides, Ed., Springer, 131-170.
17. Hsu, K. L., X. G. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 1176-1190.
18. Hsu, K. L., H. V. Gupta, X. G. 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.
19. 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.
20. Janowiak, J. E., R. J. Joyce, and Y. Yarosh, 2001: A real-time global half-hourly pixel-resolution infrared dataset and its applications. Bull. Amer. Meteor. Soc., 82, 205-217.
21. 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.
22. 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.
23. 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.
24. Kohonen, T., 1984: Self Organization and Associative Memory. Springer-Verlag, 255 pp.
25. Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3, 112-130.
26. Kurino, T., 1997: A satellite infrared technique for estimating "deep/shallow" precipitation. Adv. Space Res., 19, 511-514.
27. 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.]
28. Lovejoy, S., and G. L. Austin, 1979: The delineation of rain areas from visible and IR satellite data for GATE and mid-latitudes. Atmos.-Ocean, 17, 77-92.
29. MacQueen, J. B., 1967: Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkeley Symp. on Mathematical Statistics and Probability, Vol. 1, Berkeley, CA, University of California Press, 281-297.
30. 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.
31. Manobianco, J., S. Koch, V. M. Karyampudi, and A. J. Negri, 1994: The impact of assimilating satellite-derived precipitation rates on numerical simulations of the ERICA IOP 4 cyclone. Mon. Wea. Rev., 122, 341-365.
32. Martin, D. W., B. Goodman, T. J. Schmit, and E. C. Cutrim, 1990: Estimation of daily rainfall over the Amazon basin. J. Geophys. Res., 95, 17 043-17 050.
33. 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.
34. 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.
35. Negri, A. J., R. F. Adler, and P. J. Wetzel, 1984: Rain estimation from satellites-An examination of the Griffith-Woodley technique. J. Climate Appl. Meteor., 23, 102-116.
36. 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.
37. Qiu, D., and A. C. Tamhane, 2007: A comparative study of the K-means algorithm and the normal mixture model for clustering: Univariate case. J. Stat. Plann. Inference, 137, 3722-3740.
38. 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.
39. Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570-575.
40. 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.
41. Sorooshian, S., X. Gao, K. Hsu, R. A. Maddox, Y. Hong, H. V. Gupta, and B. Imam, 2002: Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information. J. Climate, 15, 983-1001.
42. 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.
43. 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.
44. Tsonis, A. A., and G. A. Isaac, 1985: On a new approach for instantaneous rain area delineation in the mid-latitudes using GOES data. J. Climate Appl. Meteor., 24, 1208-1218.
45. Turk, F. J., and S. D. Miller, 2005: Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques. IEEE Trans. Geosci. Remote Sens., 43, 1059-1069.
46. 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. Satellite Meteorology and Oceanography, Long Beach, CA, Amer. Meteor. Soc., JP1.2. [Available online at http://ams.confex.com/ams/pdfpapers/56865.pdf.]
47. 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.
48. Woodley, W. L., C. G. Griffith, J. S. Griffin, and S. C. Stromatt, 1980: The inference of GATE convective rainfall from SMS-1 imagery. J. Appl. Meteor., 19, 388-408.
49. 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.
50. Xu, L., S. Sorooshian, X. Gao, and H. V. Gupta, 1999: A cloudpatch technique for identification and removal of no-rain clouds from satellite infrared imagery. J. Appl. Meteor., 38, 1170-1181.
51. Yang, G.-Y., and J. Slingo, 2001: The diurnal cycle in the tropics. Mon. Wea. Rev., 129, 784-801.