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									                                                                                                                                          THE FUTURE OF RAINFALL ESTIMATES FROM GOES
                                                                                                                                                                                               Robert J. Kuligowski
                                                                                                                                                                      Office of Research and Applications, NOAA/NESDIS, Camp Springs, MD



       Why Estimate Rainfall Using Satellites?                                                                                                                                       GOES QPE: Physics and Advances                                                                                                     Applications of Planned and Potential GOES-R
• Large areas of the world are
                                                                                                                                                                  • Most satellite QPE algorithms relate cloud-                                                                                                                           Instruments
                                                                                                                                                                    top brightness temperature to cloud                                                                                                                Advanced Baseline Imager (ABI)
  outside radar and raingauge
                                                                                                                                                                    thickness and use it as a proxy for rainfall                                                                                                       • Improved spatial resolution (0.5 km visible, 2 km IR) will improve the spatial resolution
  coverage—oceans, sparsely
                                                                                                                                                                    rate; e.g. heavier rain is associated with                                                                                                             of satellite rainfall estimates--vital since extreme precipitation events frequently exhibit
  populated regions, and even
                                                                                                                                                                    colder clouds (Fig. 2).                                                                                                                                very sharp rainfall gradients.
  areas affecting U.S. interests
                                                                                                                                                                  • However, thin, nonraining cirrus clouds also                                                                                                       • Continuous 5-minute data will improve the accuracy and timeliness of GOES rainfall
  such as the Rio Grande
                                                                                                                                                                    have cold tops (Fig. 3).Some algorithms,                                                                                                               estimates, since rainfall rates change very rapidly with time during extreme precipitation
  Valley (Fig. 1).
                                                                                                                                                                    such as the GOES Multispectral Rainfall                                                                                                                events. Five-minute data will also improve GOES-based nowcasting capability.
• Radar estimates of rainfall
                                    Figure 1. Comparison of precipitation estimates from radar (left) and satellite (right) over the Rio Grande Valley of Texas
                                    and northern Mexico, illustrating the limitations of radar coverage in this region.
                                                                                                                                                                    Algorithm (GMSRA; Ba and Gruber 2001),                                                                                                             • A number of the channels proposed from the ABI will be valuable for producing
  are affected by range effects,                                                                                                                                                                                                     Figure 2. Rain rate-brightness temperature curve for the Auto-Estimator.
                                                                                                                                                                    use visible data to discriminate between                                                                                                               physically-based precipitation retrievals:
  beam overshoot, beam block, and differences in calibration from one radar to another, while
                                                                                                                                                                    thick and thin clouds.                                              Cumulonimbus                 Cirrus                                                 0.64 m –               more accurate determination of cloud optical depth and cloud
  raingauge coverage has tremendous gaps Satellite data offer a complimentary dataset in                                                                                                                                                  Tb=200 K                  Tb=200 K
                                                                                                                                                                  • Also, nimbostratus clouds are lower in the                                                                                                                                        ice/water path during the daytime
  regions where the quality of radar-based estimates is questionable.
                                                                                                                                                                    atmosphere than cumulus clouds and often                                                                                                                1.6 m –                Daytime retrieval of cloud particle effective radius
                     GOES QPE: Past to Present                                                                                                                      produce much heavier rainfall than their                                                                                                                                         Discrimination of water clouds from ice clouds
 Polar-Orbiter Era (1960’s):                                                                                                                                       relatively warm tops would suggest (Fig. 3).                                                                             Nimbostratus                   8.55 m --              Nighttime retrieval of cloud particle size
                                                                                                                                                                                                                                                                                              Tb=240 K
   • Television Infrared Observation Satellite (TIROS-IV) (Lethbridge 1967)                                                                                         Data from multiple satellite channels can be                                                                                                                                     Discrimination of water clouds from ice clouds
   • Environmental Science Service Administration (ESSA) Satellite (Barrett 1970)                                                                                   used to infer information about the cloud                                                                                                               10.35 m --             Additional information on cloud particle size.
   Pioneering work relating rainfall information to IR and visible data, but long time lapses                                                                       properties (e.g. particle size and thickness)                                                                                                      Advanced Baseline Sounder (ABS)/Geosynchronous Imaging Fourier Transform
      between imagesgenerally useful for long-term precipitation totals only.                                                                                      and thus identify relatively warm clouds that                    Figure 3. Illustration of the IR signal from different cloud types.                   Spectrometer (GIFTS)
 Pre-GOES geostationary era (mid 1960’s-early 1970’s):                                                                                                             are producing precipitation (e.g. Rosenfeld                                                                                                        • Hyperspectral imaging will enable much more accurate retrieval of cloud properties using
   • Applications Technology Satellite (ATS) (Woodley and Sancho 1971)                                                                                              and Gutman 1994; Miller et al. 2000). This                                                                                                             radiative transfer models, which in turn will lead to more physically-based and more
   • Synchronous Meteorological Satellite (SMS)                                                                                                                     is illustrated in Fig. 4.                                                                                                                              accurate estimates of rainfall from visible/infrared information.
   Continuous coverage, but not suited for operational real-time use.                                                                                             • Microwave-based estimates of precipitation                                                                                                         Geosyncrhonous Microwave (GEM) Sounder/Imager
 GOES era (mid 1970’s-today):                                                                                                                                      are generally more robust than IR-based                                                                                                            • Microwave frequencies are more suitable than IR for inferring cloud properties because
    Climatological applications: The improved spatial and temporal resolution of GOES                                                                              estimates, but are only available several                                                                                                              precipitating clouds are semitransparent in the microwave; numerous algorithms already
      data led to many techniques for large-scale / climatological applications, including                                                                          times per day. A number of experimental                                                                                                                exist for microwave-based retrievals of rain rate and cloud properties (e.g. Ferraro et al.
      • Griffith-Woodley Technique (Griffith et al. 1978);                                                                                                          algorithms have been developed to combine                                                                                                              1997; Kummerow et al. 2000).
      • GOES Precipitation Index, (GPI; Arkin and Meisner 1987);                                                                                                    these data with continuously available                                                                                                             • However, microwave coverage is infrequent due to restriction to polar-orbiting platforms.
      • Convective-Stratiform Technique (CST;Adler and Negri 1988).                                                                                                 GOES data in order to improve the accuracy                                                                                                             Continuous microwave coverage (though at lower spatial resolution than the IR and
                                                                                                                                                                                                                                   Figure 4. An example of the relationship between ice water path (kg/m2;
    Forecasting applications:      GOES data were also ideal for small-scale QPE for                                                                               of the latter (e.g. Turk et al. 1998;                          top left), cloud particle effective radius (m; top right) and radar rainfall           visible) would support retrieval of cloud properties and precipitation rates via an optimal
                                                                                                                                                                                                                                   rate (mm/h; bottom) at 2100 UTC 13 June 1999.
      operational forecaster support in real time:                                                                                                                  Sorooshian et al. 2000; Kuligowski 2002).                                                                                                              combination of visible, IR, and microwave data.
      • Interactive Flash Flood Analyzer, (IFFA; Scofield 1987)
         Combination of automated algorithms and forecaster input                                                                                                                                                                                                                                    References
         Disseminated to Weather Forecast Offices (WFO’s) for forecaster use.                                                                                     Adler, R. F., and A. J. Negri, 1988: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl.           Miller, S. D., G. L. Stephens, C. K. Drummond, A. K. Heidinger, and P. T. Partain, 2000: A multisensor diagnostic satellite
                                                                                                                                                                       Meteor., 27, 30-51.                                                                                                                             cloud property retrieval scheme. J. Geophys. Res., 105, 19955-19971.
         Manual component precluded widespread application.                                                                                                       Arkin, P. A., and B. N. Meisner, 1987: The relationship between large-scale convective rainfall and cold cloud over the Western                 Rosenfeld, D., and G. Gutman, 1994: Retrieving microphysical properties near the tops of potential rain clouds by multi spectral
                                                                                                                                                                       Hemisphere during 1982-84. Mon. Wea. Rev., 115, 51-74.                                                                                          analysis of AVHRR data. Atmos. Res., 34, 259-283.
      • The Auto-Estimator, or A-E (Vicente et al. 1999, 2001)                                                                                                     Barrett, E. C., The estimation of monthly rainfall from satellite data. Mon. Wea. Rev., 98, 322-327.                                            Scofield, 1987: The NESDIS operational convective precipitation technique. Mon. Wea. Rev., 115, 1773-1792.
                                                                                                                                                                   Ferraro, R. R., Special Sensor Microwave Imager derived global rainfall estimates for climatological applications. J. Geophys. Res.,            Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: An evaluation of PERSIANN system
         Fully automated most of the IFFA features                                                                                                                    102, 16715-16736.                                                                                                                               satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035-2046.
         Estimates every half hour (and then every 15 minutes) over the entire CONUS                                                                              Griffith, C. G., W. L. Woodley, P. G. Grube, D. W. Martin, J. Stout, and D. N. Sikdar, 1978: Rain estimates from geosynchronous                 Turk, F. J., F. S. Marzaon, and E. A. Smith, 1998: Combining geostationary and SSM/I data for rapid rain rate estimation and
                                                                                                                                                                       satellite imagery: Visible and infraredstudies. Mon. Wea. Rev., 106, 1153-1171.                                                                 accumulation. Preprints, 9th Conf. On Satellite Meteorology and Oceanography, Paris, France, Amer. Meteor. Soc., 462-465.
         A-E tends to overestimate rain area and requires radar data as a corrective                                                                              Lethbridge, M., 1967: Precipitation probability and satellite radiation data. Mon. Wea. Rev., 95, 487 490.                                      Vicente, G., R. A. Scofield, and W. P. Menzel, 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer.
                                                                                                                                                                   Kuligowski, R. J., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor., 3,                Meteor. Soc., 79, 1883-1898.
         The Hydro-Estimator (H-E) was developed to overcome this deficiency and has                                                                                  112-130.                                                                                                                                    -----, J. C. Davenport, and R. A. Scofield, 2002: The role of orographic and parallax corrections on real time high resolution
                                                                                                                                                                   Kummerow, C., Y. Hong, W. S. Olson, S. Yang, R. F. Adler, J. McCollum, R. Ferraro, G. Petty, D. B. Shin, and T. T. Wilheit, 2001:                   satellite rainfall distribution. Int. J. Remote Sens., 23, 221-230.
           replaced the A-E in operational use, including dissemination on the Advanced
                                                                                                                                                                       The evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl.                   Woodley, W. L., and B. Sancho, 1971: A first step toward rainfall estimation from satellite cloud photographs. Weather, 26,
           Weather Information Processing System (AWIPS).                                                                                                              Meteor., 40, 1801-1820.                                                                                                                         279-289.

								
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