Comparison of TRMM Precipitation Retrievals with Rain Gauge Data from Ocean Buoys

Kenneth P. Bowman Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

Search for other papers by Kenneth P. Bowman in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Four years of precipitation retrievals from the Tropical Rainfall Measuring Mission (TRMM) satellite are compared with data from 25 surface rain gauges on the National Oceanic and Atmospheric Administration/Pacific Marine Environment Laboratory (NOAA/PMEL) Tropical Atmosphere–Ocean Array/Triangle Trans-Ocean Buoy Network TAO/TRITON buoy array in the tropical Pacific. The buoy gauges have a significant advantage over island-based gauges for this purpose because they represent open-ocean conditions and are not affected by island orography or surface heating. Because precipitation is correlated with itself in both space and time, comparisons between the two data sources can be improved by properly averaging in space and/or time. When comparing gauges with individual satellite overpasses, the optimal averaging time for the gauge (centered on the satellite overpass time) depends on the area over which the satellite data are averaged. For 1° × 1° areas there is a broad maximum in the correlation for gauge-averaging periods of ∼2 to 10 h. Maximum correlations r are in the range 0.6 to 0.7. For larger satellite averaging areas, correlations with the gauges are smaller (because a single gauge becomes less representative of the precipitation in the box) and the optimum gauge-averaging time is longer. For individual satellite overpasses averaged over a 1° × 1° box, the relative rms difference with respect to a rain gauge centered in the box is ∼200% to 300%. For 32-day time means over 1° × 1° boxes, the relative rms difference between the satellite data and a gauge is in the range of 40% to 70%. The bias between the gauges and the satellite retrievals is estimated by correlating the long-term time-mean precipitation estimates across the set of gauges. The TRMM Microwave Imager (TMI) gives an r2 of 0.97 and a slope of 0.970, indicating very little bias with respect to the gauges. For the Precipitation Radar (PR) the comparable numbers are 0.92 and 0.699. The results of this study are consistent with the sampling error estimates from the statistical model of Bell and Kundu.

Corresponding author address: Kenneth P. Bowman, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77845. Email: k-bowman@tamu.edu

Abstract

Four years of precipitation retrievals from the Tropical Rainfall Measuring Mission (TRMM) satellite are compared with data from 25 surface rain gauges on the National Oceanic and Atmospheric Administration/Pacific Marine Environment Laboratory (NOAA/PMEL) Tropical Atmosphere–Ocean Array/Triangle Trans-Ocean Buoy Network TAO/TRITON buoy array in the tropical Pacific. The buoy gauges have a significant advantage over island-based gauges for this purpose because they represent open-ocean conditions and are not affected by island orography or surface heating. Because precipitation is correlated with itself in both space and time, comparisons between the two data sources can be improved by properly averaging in space and/or time. When comparing gauges with individual satellite overpasses, the optimal averaging time for the gauge (centered on the satellite overpass time) depends on the area over which the satellite data are averaged. For 1° × 1° areas there is a broad maximum in the correlation for gauge-averaging periods of ∼2 to 10 h. Maximum correlations r are in the range 0.6 to 0.7. For larger satellite averaging areas, correlations with the gauges are smaller (because a single gauge becomes less representative of the precipitation in the box) and the optimum gauge-averaging time is longer. For individual satellite overpasses averaged over a 1° × 1° box, the relative rms difference with respect to a rain gauge centered in the box is ∼200% to 300%. For 32-day time means over 1° × 1° boxes, the relative rms difference between the satellite data and a gauge is in the range of 40% to 70%. The bias between the gauges and the satellite retrievals is estimated by correlating the long-term time-mean precipitation estimates across the set of gauges. The TRMM Microwave Imager (TMI) gives an r2 of 0.97 and a slope of 0.970, indicating very little bias with respect to the gauges. For the Precipitation Radar (PR) the comparable numbers are 0.92 and 0.699. The results of this study are consistent with the sampling error estimates from the statistical model of Bell and Kundu.

Corresponding author address: Kenneth P. Bowman, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77845. Email: k-bowman@tamu.edu

Save
  • Adler, R. F., G. J. Huffman, D. T. Bolvin, S. Curtis, and E. J. Nelkin, 2000: Tropical rainfall distributions determined using TRMM combined with other satellite and rain gauge information. J. Appl. Meteor., 39 , 20072023.

    • Search Google Scholar
    • Export Citation
  • Adler, R. F., C. Kummerow, D. Bolvin, S. Curtis, and C. Kidd, 2003: Status of TRMM monthly estimates of tropical precipitation. Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission (TRMM): A Tribute to Dr. Joanne Simpson, Meteor. Monogr., No. 51, Amer. Meteor. Soc., 223–234.

    • Search Google Scholar
    • Export Citation
  • Bell, T. L., 1987: A space–time stochastic model of rainfall for satellite remote-sensing studies. J. Geophys. Res., 92D , 96319640.

    • Search Google Scholar
    • Export Citation
  • Bell, T. L., and P. K. Kundu, 1996: A study of the sampling error in satellite rainfall estimates using optimal averaging of data and a stochastic model. J. Climate, 9 , 12511268.

    • Search Google Scholar
    • Export Citation
  • Bell, T. L., and P. K. Kundu, 2000: Dependence of satellite sampling error on monthly averaged rain rates: Comparison of simple models and recent studies. J. Climate, 13 , 449462.

    • Search Google Scholar
    • Export Citation
  • Bell, T. L., and P. K. Kundu, 2003: Comparing satellite rainfall estimates with rain gauge data: Optimal strategies suggested by a spectral model. J. Geophys. Res., 108 .4121, doi:10.1029/2002JD002641.

    • Search Google Scholar
    • Export Citation
  • Bell, T. L., A. Abdullah, R. L. Martin, and G. R. North, 1990: Sampling errors for satellite-derived tropical rainfall: Monte Carlo study using a space–time stochastic model. J. Geophys. Res., 95D , 21952205.

    • Search Google Scholar
    • Export Citation
  • Bell, T. L., P. K. Kundu, and C. D. Kummerow, 2001: Sampling errors of SSM/I and TRMM rainfall averages: Comparison with error estimates from surface data and a simple model. J. Appl. Meteor., 40 , 938954.

    • Search Google Scholar
    • Export Citation
  • Bowman, K. P., A. B. Phillips, and G. R. North, 2003: Comparison of TRMM rainfall retrievals with rain gauge data from the TAO/TRITON buoy array. Geophys. Res. Lett., 30 .1757, doi:10.1029/2003GL017552.

    • Search Google Scholar
    • Export Citation
  • Hayes, S. P., L. J. Mangum, J. Picaut, A. Sumi, and K. Takeuchi, 1991: A moored array for real-time measurements in the tropical Pacific Ocean. Bull. Amer. Meteor. Soc., 72 , 339347.

    • Search Google Scholar
    • Export Citation
  • Koschmieder, H., 1934: Methods and results of definite rain measurements. Mon. Wea. Rev., 62 , 57.

  • Kummerow, C. D., 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
  • Kummerow, C. D., and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39 , 19651982.

    • Search Google Scholar
    • Export Citation
  • Masunaga, H., T. Iguchi, R. Oki, and M. Kachi, 2002: Comparison of rainfall products devived from TRMM Microwave Imager and precipitation radar. J. Appl. Meteor., 41 , 849862.

    • Search Google Scholar
    • Export Citation
  • McConnell, A., and G. R. North, 1987: Sampling errors in satellite estimates of tropical rain. J. Geophys. Res., 92D , 95679570.

  • North, G. R., and S. Nakamoto, 1989: Formalism for comparing rain estimation designs. J. Atmos. Oceanic Technol., 6 , 985992.

  • Phillips, A. B., 2002: Comparing rain rate measurements from TRMM and next generation ATLAS buoys: A ground truth experiment. M.S. thesis, Dept. of Atmospheric Sciences, Texas A&M University, 47 pp.

  • Serra, Y. L., and M. J. McPhaden, 2003: Multiple time- and space-scale comparisons of ATLAS rain gauge measurements with TRMM satellite precipitation measurements. J. Appl. Meteor., 42 , 10451059.

    • Search Google Scholar
    • Export Citation
  • Serra, Y. L., P. A’Hearn, H. P. Freitag, and M. J. McPhaden, 2001: ATLAS self-siphoning rain gauge error estimates. J. Atmos. Oceanic Technol., 18 , 19892002.

    • Search Google Scholar
    • Export Citation
  • Shin, K-S., and G. R. North, 1988: Sampling error study for rainfall estimate by satellite using a stochastic model. J. Appl. Meteor., 27 , 12181231.

    • Search Google Scholar
    • Export Citation
  • Simpson, J., R. F. Adler, and G. R. North, 1988: A proposed Tropical Rainfall Measuring Mission (TRMM) satellite. Bull. Amer. Meteor. Soc., 69 , 278295.

    • Search Google Scholar
    • Export Citation
  • WMO, 1962: Precipitation measurements at sea. WMO Tech. Note 47, Tech. Rep. 124.TP.55, 18 pp.

  • Yang, D., B. E. Goddison, J. R. Metcalfe, V. S. Golubev, R. Bates, and T. Pangburn, 1998: Accuracy of NWS 8” standard nonrecording precipitation gauge: Results and application of WMO intercomparison. J. Atmos. Oceanic Technol., 15 , 5468.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 429 88 9
PDF Downloads 294 80 6