• Doswell, C. A., III, , Brooks H. E. , , and Maddox R. A. , 1996: Flash flood forecasting: An ingredient-based methodology. Wea. Forecasting, 11, 560581, doi:10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., cited 2007: Forecast verification: Issues, methods and FAQ. [Available online at http://www.cawcr.gov.au/projects/verification/].

  • Feidas, H., , and Cartalis C. , 2001: Monitoring mesoscale convective cloud systems asscociated with heavy storms using Meteosat imagery. J. Appl. Meteor., 40, 491512, doi:10.1175/1520-0450(2001)040<0491:MMCCSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Haile, A. T., , Rientjes T. , , Gieske A. , , and Gebremichael M. , 2010: Multispectral remote sensing for rainfall detection and estimation at the source of the Blue Nile river. Int. J. Appl. Earth Obs. Geoinf., 12 (Suppl.), S76S82, doi:10.1016/j.jag.2009.09.001.

    • Search Google Scholar
    • Export Citation
  • Hamada, A., , and Nishi N. , 2010: Development of a cloud-top height estimation method by geostationary satellite split-window measurement trained with CloudSat data. J. Appl. Meteor. Climatol., 49, 20352049, doi:10.1175/2010JAMC2287.1.

    • Search Google Scholar
    • Export Citation
  • 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, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Iwabuchi, T., , Roken C. , , Mervart L. , , and Kanzaki M. , 2006: Nowcasting and weather forecasting, Real time estimation of ZTD in GEONET, Japan. Location, 1, 4449.

    • Search Google Scholar
    • Export Citation
  • Kinoti, J., , Su Z. , , Woldai T. , , and Maathuis B. , 2010: Estimation of spatial–temporal rainfall distribution using remote sensing techniques: A case study of Makanya catchment, Tanzania. Int. J. Appl. Earth Obs. Geoinf., 12 (Suppl.), S90S99, doi:10.1016/j.jag.2009.10.003.

    • Search Google Scholar
    • Export Citation
  • Kuligowski, J., cited 2003: Remote sensing in hydrology. [Available online at http://www.nws.noaa.gov/iao/InternationalHydrologyCourseCD1/1029/wmo_bk.ppt.]

  • Saito, K., and Coauthors, 2006: The operational JMA nonhydrostatic mesoscale model. Mon. Wea. Rev., 134, 12661298, doi:10.1175/MWR3120.1.

    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., , Hsu K.-L. , , Gao X. , , Gupta H. V. , , Imam B. , , and Braithwaite D. , 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 20352046, doi:10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Suseno, D. P. Y., , and Yamada T. J. , 2011: The use of cloud type classification to improve geostationary based rainfall estimation. Proc. Ninth Int. Symp. on Southeast Asian Water Environment, Bangkok, Thailand.

  • Suseno, D. P. Y., , and Yamada T. J. , 2012: Two-dimensional threshold-based cloud type classification using MTSAT data. Remote Sens. Lett.,3, 737–746, doi:10.1080/2150704X.2012.698320.

  • Vicente, G. A., , Scofield R. A. , , and Menzel W. P. , 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79, 18831898, doi:10.1175/1520-0477(1998)079<1883:TOGIRE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vicente, G. A., , Devenport J. C. , , and Scofield R. A. , 2002: The role of orographic and parallax correction on real time high resolution satellite rainfall rate distribution. Int. J. Remote Sens., 23, 221230, doi:10.1080/01431160010006935.

    • Search Google Scholar
    • Export Citation
  • Wardah, T., , Abu Bakar S. H. , , Bardosy A. , , and Maznorian M. , 2008: Use of geostationary meteorological satellite images in convective rain estimation for flash-flood forecasting. J. Hydrol., 356, 283298, doi:10.1016/j.jhydrol.2008.04.015.

    • Search Google Scholar
    • Export Citation
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The Role of GPS Precipitable Water Vapor and Atmosphere Stability Index in the Statistically Based Rainfall Estimation Using MTSAT Data

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  • 1 Faculty of Engineering, Hokkaido University, Hokkaido, Japan
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Abstract

A rainfall estimation method was developed based on the statistical relationships between cloud-top temperature and rainfall rates acquired by both the 10.8-μm channel of the Multi-Functional Transport Satellite (MTSAT) series and the Automated Meteorological Data Acquisition System (AMeDAS) C-band radar, respectively. The method focused on cumulonimbus (Cb) clouds and was developed in the period of June–September 2010 and 2011 over the landmass of Japan and its surrounding area. Total precipitable water vapor (PWV) and atmospheric vertical instability were considered to represent the atmospheric environmental conditions during the development of statistical models. Validations were performed by comparing the estimated values with the observed rainfall derived from the AMeDAS rain gauge network and the Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall estimation product. The results demonstrated that the models that considered the combination of total PWV and atmospheric vertical instability were relatively more sensitive to heavy rainfall than were the models that considered no atmospheric environmental conditions. The use of such combined information indicated a reasonable improvement, especially in terms of the correlation between estimated and observed rainfall. Intercomparison results with the TRMM 3B42 confirmed that MTSAT-based rainfall estimations made by considering atmospheric environmental conditions were more accurate for estimating rainfall generated by Cb cloud.

Corresponding author address: Dwi Prabowo Yuga Suseno, River and Watershed Engineering Laboratory, Faculty of Engineering, Hokkaido University, N13 W8 Kita-Ku, Sapporo-Shi 060-8628, Hokkaido, Japan. E-mail: dwiprab@yahoo.com

Abstract

A rainfall estimation method was developed based on the statistical relationships between cloud-top temperature and rainfall rates acquired by both the 10.8-μm channel of the Multi-Functional Transport Satellite (MTSAT) series and the Automated Meteorological Data Acquisition System (AMeDAS) C-band radar, respectively. The method focused on cumulonimbus (Cb) clouds and was developed in the period of June–September 2010 and 2011 over the landmass of Japan and its surrounding area. Total precipitable water vapor (PWV) and atmospheric vertical instability were considered to represent the atmospheric environmental conditions during the development of statistical models. Validations were performed by comparing the estimated values with the observed rainfall derived from the AMeDAS rain gauge network and the Tropical Rainfall Measuring Mission (TRMM) 3B42 rainfall estimation product. The results demonstrated that the models that considered the combination of total PWV and atmospheric vertical instability were relatively more sensitive to heavy rainfall than were the models that considered no atmospheric environmental conditions. The use of such combined information indicated a reasonable improvement, especially in terms of the correlation between estimated and observed rainfall. Intercomparison results with the TRMM 3B42 confirmed that MTSAT-based rainfall estimations made by considering atmospheric environmental conditions were more accurate for estimating rainfall generated by Cb cloud.

Corresponding author address: Dwi Prabowo Yuga Suseno, River and Watershed Engineering Laboratory, Faculty of Engineering, Hokkaido University, N13 W8 Kita-Ku, Sapporo-Shi 060-8628, Hokkaido, Japan. E-mail: dwiprab@yahoo.com
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