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 imagesgenerally 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. 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