Thailand Daily Rainfall and Comparison with TRMM Products

Roongroj Chokngamwong Center for Earth Observing and Space Research, George Mason University, Fairfax, Virginia

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Long S. Chiu Center for Earth Observing and Space Research, George Mason University, Fairfax, Virginia, and Institute of Space and Earth Information Science, Chinese University of Hong Kong, Shatin, Hong Kong

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Abstract

Daily rainfall data collected from more than 100 gauges over Thailand for the period 1993–2002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data binned at 1° × 1° with the Global Precipitation Climatology Centre (GPCC) monitoring product shows a small bias (1.11%), and the differences can be reconciled in terms of the increased number of stations in the TG dataset. Comparison of daily TG with Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 rain estimates shows improvements over version 5 (V5) in terms of bias and mean absolute difference (MAD). The V5 is computed from the adjusted Geostationary Operational Environmental Satellite (GOES) precipitation index (AGPI) and V6 is computed using the TRMM Multisatellite Precipitation Analysis (TMPA) algorithm. The V6 histogram is much closer to that of TG than V5 in terms of rain fraction and conditional rain rates. Scatterplots show that both versions of the satellite products are deficient in capturing heavy rain events. In terms of detecting rain events, a critical success index (CSI) shows no difference between V6 and V5 3B42. The CSI for V6 is higher for the rainy season than the dry season. These results are generally insensitive to rain-rate threshold and averaging periods. The temporal and spatial autocorrelation of daily rain rates for TG, V6, and V5 3B42 are computed. Autocorrelation function analyses show improved temporal and spatial autocorrelations for V6 compared to TG over V5 with e-folding times of 1, 1, and 2 days, and isotropic spatial decorrelation distances of 1.14°, 1.87°, and 3.61° for TG, V6, and V5, respectively. Rain event statistics show that the V6 3B42 overestimates the rain event durations and underestimates the rain event separations and the event conditional rain rates when compared to TG. This study points to the need to further improve the 3B42 algorithm to lower the false detection rate and improve the estimation of heavy rainfall events.

Corresponding author address: Long S. Chiu, Department of Earth Systems and GeoInformation Sciences, MSN 6A2, College of Science, George Mason University, Fairfax, VA 22030-4444. Email: rchoknga@gmu.edu

Abstract

Daily rainfall data collected from more than 100 gauges over Thailand for the period 1993–2002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data binned at 1° × 1° with the Global Precipitation Climatology Centre (GPCC) monitoring product shows a small bias (1.11%), and the differences can be reconciled in terms of the increased number of stations in the TG dataset. Comparison of daily TG with Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 rain estimates shows improvements over version 5 (V5) in terms of bias and mean absolute difference (MAD). The V5 is computed from the adjusted Geostationary Operational Environmental Satellite (GOES) precipitation index (AGPI) and V6 is computed using the TRMM Multisatellite Precipitation Analysis (TMPA) algorithm. The V6 histogram is much closer to that of TG than V5 in terms of rain fraction and conditional rain rates. Scatterplots show that both versions of the satellite products are deficient in capturing heavy rain events. In terms of detecting rain events, a critical success index (CSI) shows no difference between V6 and V5 3B42. The CSI for V6 is higher for the rainy season than the dry season. These results are generally insensitive to rain-rate threshold and averaging periods. The temporal and spatial autocorrelation of daily rain rates for TG, V6, and V5 3B42 are computed. Autocorrelation function analyses show improved temporal and spatial autocorrelations for V6 compared to TG over V5 with e-folding times of 1, 1, and 2 days, and isotropic spatial decorrelation distances of 1.14°, 1.87°, and 3.61° for TG, V6, and V5, respectively. Rain event statistics show that the V6 3B42 overestimates the rain event durations and underestimates the rain event separations and the event conditional rain rates when compared to TG. This study points to the need to further improve the 3B42 algorithm to lower the false detection rate and improve the estimation of heavy rainfall events.

Corresponding author address: Long S. Chiu, Department of Earth Systems and GeoInformation Sciences, MSN 6A2, College of Science, George Mason University, Fairfax, VA 22030-4444. Email: rchoknga@gmu.edu

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  • Adler, R. F., Huffman G. J. , and Keehn P. R. , 1994: Global tropical rain estimates from microwave-adjusted geosynchronous IR data. Remote Sens. Rev., 11 , 125152.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R. F., Kummerow C. , Bolvin D. , Curtis S. , and Kidd C. , 2003: Status of TRMM monthly estimates of tropical precipitation. Cloud Systems, Hurricanes, and TRMM, Meteor. Monogr., No. 51, Amer. Meteor. Soc., 223–234.

    • Search Google Scholar
    • Export Citation
  • Arkin, P., Turk J. , and Ebert B. , 2005: Pilot Evaluation of High Resolution Precipitation Products (PEHRPP): A contribution to GPM planning. Proc. Fifth Global Precipitation Measurement (GPM) International Planning Workshop, Tokyo, Japan, Japan Aerospace Exploration Agency. [Available online at http://www.eorc.jaxa.jp/GPM/event/ws5/hplen/agenda_20051124.html.].

    • Search Google Scholar
    • Export Citation
  • Brown, J. E. M., 2006: An analysis of the performance of hybrid infrared and microwave satellite precipitation algorithms over India and adjacent regions. Remote Sens. Environ., 101 , 6381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiu, L., Liu Z. , Rui H. , and Teng W. , 2006a: TRMM data and access tools. Earth Science Satellite Remote Sensing, II, J. Qu et al., Eds., Springer and Tsinghua University Press, 202–219.

    • Search Google Scholar
    • Export Citation
  • Chiu, L., Liu Z. , Vongsaard J. , Morain S. , Budge A. , Bales C. , and Neville P. , 2006b: Comparison of TRMM and water division rain rates over New Mexico. Adv. Atmos. Sci., 23 , 113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chiu, L., Shin D-B. , and Kwaiktowski J. , 2006c: Surface rain rate from TRMM algorithms. Earth Science Satellite Remote Sensing, I, J. Qu et al., Eds., Springer and Tsinghua University Press, 317–336.

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2002: Verifying satellite precipitation estimates for weather and hydrological applications. Proc. First IPWG Workshop, Madrid, Spain, International Precipitation Working Group. [Available online at http://www.isac.cnr.it/~ipwg/meetings/madrid/pdf/ebert.pdf.].

    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., Janowiak J. E. , and Kidd C. , 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88 , 4763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., Adler R. F. , Rudolf B. , Schneider U. , and Keehn P. , 1995: Global precipitation estimates based on a technique for combining satellite-based estimates, rain gauge analysis, and NWP model precipitation information. J. Climate, 8 , 12841295.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 1997: The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset. Bull. Amer. Meteor. Soc., 78 , 520.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multi-satellite Precipitation Analysis (TMPA): Quasi-global, multi-year, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8 , 3855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Islam, M. N., and Uyeda H. , 2006: TRMM observed vertical structure and diurnal variation of precipitation in South Asia. Proc. IGARSS’06, Denver, CO, IEEE Geoscience and Remote Sensing Society, 1292–1295.

    • Crossref
    • Export Citation
  • Katsanos, D., Lagouvardos K. , Kotroni V. , and Huffmann G. J. , 2004: Statistical evaluation of MPA-RT high-resolution precipitation estimates from satellite platforms over the central and eastern Mediterranean. Geophys. Res. Lett., 31 .L06116, doi:10.1029/2003GL019142.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., and Coauthors, 2003a: Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part I: Validation of GPCC rainfall product and pre-TRMM satellite and blended products. J. Appl. Meteor., 42 , 13371354.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., and Coauthors, 2003b: Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part II: Validation of TRMM rainfall products. J. Appl. Meteor., 42 , 13551368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rudolf, B., Hauschild H. , Ruth W. , and Schneider U. , 1994: Terrestrial precipitation analysis: Operational method and required density of point measurements. Global Precipitation and Climate Change, M. Dubois and M. Desalmand, Eds., Springer-Verlag, 173–186.

    • Search Google Scholar
    • Export Citation
  • Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5 , 570575.

  • Sevruk, B., 1982: Methods of correction for systematic error in point precipitation measurement for operational use. WMO Rep. 589, Operational Hydrology Rep. 21, World Meteorological Organization, 91 pp.

  • Turk, F. J., Bauer P. , Ebert E. , and Arkin P. A. , 2006: Satellite-derived precipitation verification activities within the International Precipitation Working Group (IPWG). Preprints, 14th Conf. on Satellite Meteorology and Oceanography, Atlanta, GA, Amer. Meteor. Soc., P2.15.

  • Willmott, C. J., and Johnson M. L. , 2005: Resolution errors associated with gridded precipitation fields. Int. J. Climatol., 25 , 19571963.

  • Willmott, C. J., and Matsuura K. , 2005: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res., 30 , 7982.

    • Crossref
    • Search Google Scholar
    • Export Citation
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