• Adler, R. F., , and A. J. Negri, 1988: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteor., 27 , 3051.

    • Search Google Scholar
    • Export Citation
  • Arkin, P. A., , and B. N. Meisner, 1987: The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–84. Mon. Wea. Rev., 115 , 5174.

    • Search Google Scholar
    • Export Citation
  • Ba, M. B., , and A. Gruber, 2001: GOES Multispectral Rainfall Algorithm (GMSRA). J. Appl. Meteor., 40 , 15001514.

  • Bellerby, T., , M. Todd, , D. Kniveton, , and C. Kidd, 2000: Rainfall estimation from a combination of TRMM Precipitation Radar and GOES multiespectral satellite imagery through the use of an artificial neural network. J. Appl. Meteor., 39 , 21152128.

    • Search Google Scholar
    • Export Citation
  • Carvalho, L. M. V., , and C. Jones, 2001: A satellite method to identify structural properties of Mesoscale Convective Systems based on Maximum Spatial Correlation Tracking Technique (MASCOTTE). J. Appl. Meteor., 40 , 16831701.

    • Search Google Scholar
    • Export Citation
  • Delgado, G., and Coauthors, 2007: Cloud cover analysis associated to cut-off low pressure systems using Meteosat imagery. Meteor. Atmos. Phys., 96 , 141157.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., , M. J. Manton, , P. A. Arkin, , R. J. Allam, , G. E. Holpin, , and A. Gruber, 1996: Results from the GPCP algorithm intercomparison programme. Bull. Amer. Meteor. Soc., 77 , 28752887.

    • Search Google Scholar
    • Export Citation
  • Feidas, H., , and C. Cartalis, 2001: Monitoring mesoscale convective cloud systems associated with heavy storms with the use of Meteosat imagery. J. Appl. Meteor., 40 , 491512.

    • Search Google Scholar
    • Export Citation
  • Griffith, C. G., , W. L. Woodley, , P. G. Grube, , D. W. Martín, , J. Stout, , and D. N. Sikdar, 1978: Rain estimates from geosynchronous satellite imagery: Visible and infrared studies. Mon. Wea. Rev., 106 , 11531171.

    • Search Google Scholar
    • Export Citation
  • Hong, Y., , K. 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 , 18341852.

    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., , J. E. Janowiak, , P. A. Arking, , and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwaves and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5 , 487503.

    • Search Google Scholar
    • Export Citation
  • Kidd, C., , D. R. Kniveton, , M. C. Todd, , and T. J. Bellerby, 2003: Satellite rainfall estimation using combined passive microwaves and infrared algorithms. J. Hydrometeor., 4 , 10881104.

    • Search Google Scholar
    • Export Citation
  • Kuligowski, R. J., 2002: A self-calibrated real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3 , 112130.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., , W. Barnes, , T. Kozu, , J. Shiue, , and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15 , 809817.

    • Search Google Scholar
    • Export Citation
  • Laing, A. G., , J. M. Fritsch, , and A. J. Negri, 1999: Contribution of mesoscale convective complexes to rainfall in Sahelian Africa: Estimates from geostationary infrared and passive microwave data. J. Appl. Meteor., 38 , 957964.

    • Search Google Scholar
    • Export Citation
  • Lensky, M. I., , and D. Rosenfeld, 2003: Satellite-based insights into precipitation formation processes in continental and maritime convective clouds at nighttime. J. Appl. Meteor., 42 , 12271233.

    • Search Google Scholar
    • Export Citation
  • Levizzani, V., , J. Schmetz, , H. J. Lutz, , J. Kerkman, , P. P. Alberoni, , and M. Cervino, 2001: Precipitation estimations from geostationary orbit and prospects for METEOSAT second generation. Meteor. Appl., 8 , 2341.

    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., , and W. B. Rossow, 1993: Structural characteristics and radiative properties of tropical cloud clusters. Mon. Wea. Rev., 121 , 32343260.

    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., , and H. Laurent, 2004: The convective system area expansion over Amazonia and its relationships with convective system life duration and high-level wind divergence. Mon. Wea. Rev., 132 , 714725.

    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., , W. B. Rossow, , R. L. Guedes, , and A. W. Walker, 1998: Life cycle variations of mesoscale convective systems over the Americas. Mon. Wea. Rev., 126 , 16301654.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., , and R. A. Houze Jr., 1992: An integrated view of the 1987 Australian monsoon and its mesoscale convective systems. Part I: Horizontal structure. Quart. J. Roy. Meteor. Soc., 118 , 927963.

    • Search Google Scholar
    • Export Citation
  • Mathon, V., , and H. Laurent, 2001: Life cycle of the Sahelian mesoscale convective cloud systems. Quart. J. Roy. Meteor. Soc., 127 , 377406.

    • Search Google Scholar
    • Export Citation
  • Porcú, F., , and V. Levizzani, 1992: Cloud classification using METEOSAT VIS-IR imagery. Int. J. Remote Sens., 13 , 893909.

  • Rosenfeld, D., , and G. Gutman, 1994: Retrieving microphysical properties of cloud tops by multispectral analysis of AVHHR data. J. Atmos. Res., 34 , 259283.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., , and L. C. Garder, 1993: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP. J. Climate, 6 , 23412369.

    • Search Google Scholar
    • Export Citation
  • Scofield, R. A., 1987: The NESDIS operational convective precipitation estimation technique. Mon. Wea. Rev., 115 , 17731792.

  • Schumacher, R. S., , and R. H. Johnson, 2005: Organization and environmental properties of extreme-rain-producing mesoscale convective systems. Mon. Wea. Rev., 133 , 961976.

    • Search Google Scholar
    • Export Citation
  • Tapiador, F. J., , C. Kidd, , V. Levizzani, , and F. S. Marzano, 2004: A neural networks-based PMW-IR fusion technique to derive half-hourly rainfall estimates at 0.1° resolution. J. Appl. Meteor., 43 , 576594.

    • Search Google Scholar
    • Export Citation
  • Uddstrom, M. J., , and W. R. Gray, 1996: Satellite cloud classification and rain-rate estimation using multispectral radiances and measures of spatial texture. J. Appl. Meteor., 35 , 839858.

    • Search Google Scholar
    • Export Citation
  • Vicente, G., , R. A. Scofield, , and W. P. Menzel, 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79 , 18811898.

    • Search Google Scholar
    • Export Citation
  • Vicente, G., , J. C. Davenport, , and R. A. Scofield, 2002: The role of orographic and parallax corrections on real time high resolution rainfall rate distribution. Int. J. Remote Sens., 23 , 2. 221230.

    • Search Google Scholar
    • Export Citation
  • Wylie, D. P., 1979: An application of a geostationary satellite estimation technique to an extratropical area. J. Appl. Meteor., 18 , 16401648.

    • Search Google Scholar
    • Export Citation
  • Xu, L., , S. Sorooshian, , X. Gao, , and H. Gupta, 1999: A cloud-patch technique for identification and removal of no-rain clouds from satellite infrared imagery. J. Appl. Meteor., 38 , 11701181.

    • Search Google Scholar
    • Export Citation
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Basis for a Rainfall Estimation Technique Using IR–VIS Cloud Classification and Parameters over the Life Cycle of Mesoscale Convective Systems

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  • 1 Astronomy and Meteorology Department, University of Barcelona, Barcelona, Spain
  • | 2 Instituto Nacional de Pesquisas Espaciais, Centro de Previsão de Tempo e Estudos Climáticos, Cachoeira Paulista-São Paulo, Brazil
  • | 3 Astronomy and Meteorology Department, University of Barcelona, Barcelona, Spain
  • | 4 Universidad de Vigo, Facultad de Ciencias de Ourense, Ourense, Spain, and University of Lisbon, CGUL, IDL, Lisbon, Portugal
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Abstract

This paper discusses the basis for a new rainfall estimation method using geostationary infrared and visible data. The precipitation radar on board the Tropical Rainfall Measuring Mission satellite is used to train the algorithm presented (which is the basis of the estimation method) and the further intercomparison. The algorithm uses daily Geostationary Operational Environmental Satellite infrared–visible (IR–VIS) cloud classifications together with radiative and evolution properties of clouds over the life cycle of mesoscale convective systems (MCSs) in different brightness temperature (Tb) ranges. Despite recognition of the importance of the relationship between the life cycle of MCSs and the rainfall rate they produce, this relationship has not previously been quantified precisely. An empirical relationship is found between the characteristics that describe the MCSs’ life cycle and the magnitude of rainfall rate they produce. Numerous earlier studies focus on this subject using cloud-patch or pixel-based techniques; this work combines the two techniques. The algorithm performs reasonably well in the case of convective systems and also for stratiform clouds, although it tends to overestimate rainfall rates. Despite only using satellite information to initialize the algorithm, satisfactory results were obtained relative to the hydroestimator technique, which in addition to the IR information uses extra satellite data such as moisture and orographic corrections. This shows that the use of IR–VIS cloud classification and MCS properties provides a robust basis for creating a future estimation method incorporating humidity Eta field outputs for a moisture correction, digital elevation models combined with low-level moisture advection for an orographic correction, and a nighttime cloud classification.

Corresponding author address: Germán Delgado, Dept. D’Astronomia i Meteorologia, Universitat de Barcelona, Diagonal 647, 08028 Barcelona, Spain. Email: isohipsa@yahoo.es

Abstract

This paper discusses the basis for a new rainfall estimation method using geostationary infrared and visible data. The precipitation radar on board the Tropical Rainfall Measuring Mission satellite is used to train the algorithm presented (which is the basis of the estimation method) and the further intercomparison. The algorithm uses daily Geostationary Operational Environmental Satellite infrared–visible (IR–VIS) cloud classifications together with radiative and evolution properties of clouds over the life cycle of mesoscale convective systems (MCSs) in different brightness temperature (Tb) ranges. Despite recognition of the importance of the relationship between the life cycle of MCSs and the rainfall rate they produce, this relationship has not previously been quantified precisely. An empirical relationship is found between the characteristics that describe the MCSs’ life cycle and the magnitude of rainfall rate they produce. Numerous earlier studies focus on this subject using cloud-patch or pixel-based techniques; this work combines the two techniques. The algorithm performs reasonably well in the case of convective systems and also for stratiform clouds, although it tends to overestimate rainfall rates. Despite only using satellite information to initialize the algorithm, satisfactory results were obtained relative to the hydroestimator technique, which in addition to the IR information uses extra satellite data such as moisture and orographic corrections. This shows that the use of IR–VIS cloud classification and MCS properties provides a robust basis for creating a future estimation method incorporating humidity Eta field outputs for a moisture correction, digital elevation models combined with low-level moisture advection for an orographic correction, and a nighttime cloud classification.

Corresponding author address: Germán Delgado, Dept. D’Astronomia i Meteorologia, Universitat de Barcelona, Diagonal 647, 08028 Barcelona, Spain. Email: isohipsa@yahoo.es

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