Editorial Type:
Article
Article Type:
Research Article

Mapping TRMM TMPA into Average Recurrence Interval for Monitoring Extreme Precipitation Events

Yaping Zhou Goddard Earth Sciences Technology and Research, Morgan State University, Baltimore, and Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland

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William K. M. Lau Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland

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George J. Huffman Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Print Publication:
01 May 2015
DOI:
https://doi.org/10.1175/JAMC-D-14-0269.1
Page(s):
979–995
Restricted access

Abstract

A prototype online extreme precipitation monitoring system is developed from the TRMM TMPA near-real-time precipitation product. The system utilizes estimated equivalent average recurrence interval (ARI) for up-to-date precipitation accumulations from the past 1, 2, 3, 5, 7, and 10 days to locate locally severe events. The mapping of precipitation accumulations into ARI is based on local statistics fitted into generalized extreme value (GEV) distribution functions. Initial evaluation shows that the system captures historic extreme precipitation events quite well. The system provides additional rarity information for ongoing precipitation events based on local climatology that could be used by the general public and decision makers for various hazard management applications. Limitations of the TRMM ARI due to short record length and data accuracy are assessed through comparison with long-term high-resolution gauge-based rainfall datasets from the NOAA Climate Prediction Center and the Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) project. TMPA-based extreme climatology captures extreme distribution patterns from gauge data, but a strong tendency to overestimate from TMPA over regimes of complex orography exists.

Corresponding author address: Dr. Yaping Zhou, GESTAR/Morgan State University, Climate and Radiation Laboratory, NASA/GSFC, 8800 Greenbelt Rd., Greenbelt, MD 20771. E-mail: yaping.zhou-1@nasa.gov

Abstract

A prototype online extreme precipitation monitoring system is developed from the TRMM TMPA near-real-time precipitation product. The system utilizes estimated equivalent average recurrence interval (ARI) for up-to-date precipitation accumulations from the past 1, 2, 3, 5, 7, and 10 days to locate locally severe events. The mapping of precipitation accumulations into ARI is based on local statistics fitted into generalized extreme value (GEV) distribution functions. Initial evaluation shows that the system captures historic extreme precipitation events quite well. The system provides additional rarity information for ongoing precipitation events based on local climatology that could be used by the general public and decision makers for various hazard management applications. Limitations of the TRMM ARI due to short record length and data accuracy are assessed through comparison with long-term high-resolution gauge-based rainfall datasets from the NOAA Climate Prediction Center and the Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) project. TMPA-based extreme climatology captures extreme distribution patterns from gauge data, but a strong tendency to overestimate from TMPA over regimes of complex orography exists.

Corresponding author address: Dr. Yaping Zhou, GESTAR/Morgan State University, Climate and Radiation Laboratory, NASA/GSFC, 8800 Greenbelt Rd., Greenbelt, MD 20771. E-mail: yaping.zhou-1@nasa.gov
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Journal of Applied Meteorology and Climatology

  • Adhikari, P., Y. Hong, K. Douglas, D. Kirschbaum, J. Gourley, R. Adler, and G. Robert Brakenridge, 2010: A digitized global flood inventory (1998–2008): Compilation and preliminary results. Nat. Hazards, 55, 405–422, doi:10.1007/s11069-010-9537-2.

    • Search Google Scholar
    • Export Citation

  • AghaKouchak, A., A. Behrangi, S. Sorooshian, K. Hsu, and E. Amitai, 2011: Evaluation of satellite-retrieved extreme precipitation rates across the central United States. J. Geophys. Res., 116, D02115, doi:10.1029/2010JD014741.

    • Search Google Scholar
    • Export Citation

  • Allan, R. P., and B. J. Soden, 2008: Atmospheric warming and the amplification of precipitation extremes. Science, 321, 1481–1484, doi:10.1126/science.1160787.

    • Search Google Scholar
    • Export Citation

  • Ashley, S. T., and W. S. Ashley, 2008: Flood fatalities in the United States. J. Appl. Meteor. Climatol., 47, 806–818, doi:10.1175/2007JAMC1611.1.

    • Search Google Scholar
    • Export Citation

  • Bonnin, G. M., D. Martin, B. Lin, T. Parzybok, M. Yekta, and D. Riley, 2011: Semiarid southwest (Arizona, southeast California, Nevada, New Mexico, Utah). Precipitation-Frequency Atlas of the United States, Vol. 1, Version 5.0, NOAA Atlas 14, 271 pp. [Available online at http://www.nws.noaa.gov/oh/hdsc/PF_documents/Atlas14_Volume1.pdf.]

  • Burn, D. H., 1990: Evaluation of regional flood frequency analysis with a region of influence approach. Water Resour. Res., 26, 2257–2265, doi:10.1029/WR026i010p02257.

    • Search Google Scholar
    • Export Citation

  • Chen, S., and Coauthors, 2013: Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan: Are remote-sensing products ready for gauge denial scenario of extreme events? J. Hydrol., 506, 4–13, doi:10.1016/j.jhydrol.2012.12.026.

    • Search Google Scholar
    • Export Citation

  • Cheng, L., A. AghaKouchak, E. Gilleland, and R. W. Katz, 2014: Non-stationary extreme value analysis in a changing climate. Climatic Change, 127, 353–369, doi:10.1007/s10584-014-1254-5.

    • Search Google Scholar
    • Export Citation

  • Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer, 208 pp.

  • Easterling, D. R., J. L. Evans, P. Ya. Groisman, T. R. Karl, K. E. Kunkel, and P. Ambenje, 2000: Observed variability and trends in extreme climate events: A brief review. Bull. Amer. Meteor. Soc., 81, 417–425, doi:10.1175/1520-0477(2000)081<0417:OVATIE>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull. Amer. Meteor. Soc., 88, 47–64, doi:10.1175/BAMS-88-1-47.

    • Search Google Scholar
    • Export Citation

  • Endreny, T. A., and N. E. A. Imbeah, 2009: Generating robust rainfall intensity-duration-frequency estimates with short-record satellite data. J. Hydrol., 371 (1–4), 182–191, doi:10.1016/j.jhydrol.2009.03.027.

    • Search Google Scholar
    • Export Citation

  • Gilleland, E., and R. W. Katz, 2006: Analyzing seasonal to interannual extreme weather and climate variability with the extremes toolkit (extRemes). Preprints, 18th Conf. on Climate Variability and Change, Atlanta, GA, Amer. Meteor. Soc., P2.15. [Available online at https://ams.confex.com/ams/pdfpapers/101830.pdf.]

  • Gochis, D., and Coauthors, 2015: The Great Colorado Flood of September 2013. Bull. Amer. Meteor. Soc., doi:10.1175/BAMS-D-13-00241.1, in press.

  • Groisman, P. Ya., R. W. Knight, D. R. Easterling, T. R. Karl, G. C. Hegerl, and V. N. Razuvaev, 2005: Trends in intense precipitation in the climate record. J. Climate, 18, 1326–1350, doi:10.1175/JCLI3339.1.

    • Search Google Scholar
    • Export Citation

  • Gruntfest, E., 2009: Editorial. J. Flood Risk Manage., 2, 83–84, doi:10.1111/j.1753-318X.2009.01028.x.

  • Habib, E., A. Henschke, and R. F. Adler, 2009: Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA. Atmos. Res., 94, 373–388, doi:10.1016/j.atmosres.2009.06.015.

    • Search Google Scholar
    • Export Citation

  • Higgins, R. W., W. Shi, E. Yarosh, and R. Joyce, 2000: Improved United States Precipitation Quality Control System and Analysis. NCEP/Climate Prediction Center Atlas 7. [Available online at http://www.cpc.ncep.noaa.gov/products/outreach/research_papers/ncep_cpc_atlas/7/.]

  • Hong, Y., R. F. Adler, A. Negri, and G. J. Huffman, 2007: Flood and landslide applications of near real-time satellite rainfall estimation. Nat. Hazards, 43, 285–294, doi:10.1007/s11069-006-9106-x.

    • Search Google Scholar
    • Export Citation

  • Hong, Y., R. F. Adler, G. J. Huffman, H. Pierce, 2010: Applications of TRMM-based multi-satellite precipitation estimation for global runoff prediction: Prototyping a global flood modeling system. Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer, 245–265.

  • Hosking, J. R. M., 1990: L-moments: Analysis and estimation of distributions using linear combinations of order statistics. J. Roy. Stat. Soc., 52B, 105–124.

    • Search Google Scholar
    • Export Citation

  • Hosking, J. R. M., and J. R. Wallis, 1997: Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge University Press, 242 pp.

  • Hossain, F., and D. P. Lettenmaier, 2006: Flood prediction in the future: Recognizing hydrologic issues in anticipation of the Global Precipitation Measurement mission. Water Resour. Res., 42, W11301, doi:10.1029/2006WR005202.

    • Search Google Scholar
    • Export Citation

  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., and Coauthors, 2007: The TRMM Multi-Satellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38–55, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and E. Nelkin, 2010: The TRMM Multi-satellite Precipitation Analysis (TMPA). Satellite Rainfall Applications for Surface Hydrology, F. Hossain and M. Gebremichael, Eds., Springer Verlag, 3–22.

  • Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, and P. Xie, 2014: NASA Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (IMERG). NASA Algorithm Theoretical Basis Doc. version 4.4, 30 pp. [Available online at http://pmm.nasa.gov/sites/default/files/document_files/IMERG_ATBD_V4.4.pdf.]

  • Huntingford, C., R. G. Jones, C. Prudhomme, R. Lamb, H. C. Gash, and D. A. Jones, 2003: Regional climate-model predictions of extreme rainfall for a changing climate. Quart. J. Roy. Meteor. Soc., 129, 1607–1621, doi:10.1256/qj.02.97.

    • Search Google Scholar
    • Export Citation

  • IPCC, 2012: Summary for policymakers. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, C. B. Field et al., Eds., Cambridge University Press, 3–21.

    • Search Google Scholar
    • Export Citation

  • Katz, R. W., 2010: Statistics of extremes in climate change. Climatic Change, 100, 71–76, doi:10.1007/s10584-010-9834-5.

  • Katz, R. W., M. B. Parlange, and P. Naveau, 2002: Statistics of extremes in hydrology. Adv. Water Resour., 25, 1287–1304, doi:10.1016/S0309-1708(02)00056-8.

    • Search Google Scholar
    • Export Citation

  • Kirschbaum, D., T. Stanley, and Y. Zhou, 2015: Spatial and temporal analysis of a global landslide catalog. Geomorphology, doi:10.1016/j.geomorph.2015.03.016, in press.

    • Search Google Scholar
    • Export Citation

  • Kotz, S., and S. Nadarajah, 2000: Extreme Value Distributions: Theory and Applications. Imperial College Press, 191 pp.

  • Koutsoyiannis, D., and G. Baloutsos, 2000: Analysis of a long record of annual maximum rainfall in Athens, Greece, and design rainfall inferences. Nat. Hazards, 22, 29–48, doi:10.1023/A:1008001312219.

    • Search Google Scholar
    • Export Citation

  • Lau, K.-M., and H.-T. Wu, 2007: Detecting trends in tropical rainfall characteristics, 1979–2003. Int. J. Climatol., 27, 979–988, doi:10.1002/joc.1454.

    • Search Google Scholar
    • Export Citation

  • Leadbetter, M. R., G. Lindren, and H. RootzĂ©n, 1983: Extremes and Related Properties of Random Sequences and Processes. Springer, 336 pp.

  • Li, L., and Coauthors, 2009: Evaluation of the real-time TRMM-based multi-satellite precipitation analysis for an operational flood prediction system in Nzoia Basin, Lake Victoria, Africa. Nat. Hazards, 50, 109–123, doi:10.1007/s11069-008-9324-5.

    • Search Google Scholar
    • Export Citation

  • Liao, Z., Y. Hong, J. Wang, H. Fukuoka, K. Sassa, D. Karnawati, and F. Fathani, 2010: Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets. Landslides, 7, 317–324, doi:10.1007/s10346-010-0219-7.

    • Search Google Scholar
    • Export Citation

  • Mehran, A., and A. AghaKouchak, 2014: Capabilities of satellite precipitation datasets to estimate heavy precipitation rates at different temporal accumulations. Hydrol. Processes, 28, 2262–2270, doi:10.1002/hyp.9779.

    • Search Google Scholar
    • Export Citation

  • Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to more-intense precipitation extremes. Nature,470, 378–381, doi:10.1038/nature09763.

  • Parzybok, T., B. Clarke, and D. M. Hultstrand, 2011: Average recurrence interval of extreme rainfall in real-time. Earthzine. [Available online at http://earthzine.org/2011/04/19/average-recurrence-interval-of-extreme-rainfall-in-real-time/.]

  • Perica, S., and Coauthors, 2013: Midwestern states (Colorado, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Oklahoma, South Dakota, Wisconsin). Precipitation-Frequency Atlas of the United States, Vol. 8, Version 2.0, NOAA Atlas 14, 297 pp. [Available online at http://www.nws.noaa.gov/oh/hdsc/PF_documents/Atlas14_Volume8.pdf.]

  • Schubert, S. D., Y. Chang, M. J. Suarez, and P. J. Pegion, 2008: ENSO and wintertime extreme precipitation events over the contiguous United States. J. Climate, 21, 22–39, doi:10.1175/2007JCLI1705.1.

    • Search Google Scholar
    • Export Citation

  • Shen, Y., A. Xiong, Y. Wang, and P. Xie, 2010: Performance of high-resolution satellite precipitation products over China. J. Geophys. Res., 115, D02114, doi:10.1029/2009JD012097.

    • Search Google Scholar
    • Export Citation

  • Smith, K., and R. Ward, 1998: Floods: Physical Processes and Human Impacts. John Wiley and Sons, 394 pp.

  • Sorooshian, S., and Coauthors, 2011: Advanced concepts on remote sensing of precipitation at multiple scales. Bull. Amer. Meteor. Soc., 92, 1353–1357, doi:10.1175/2011BAMS3158.1.

    • Search Google Scholar
    • Export Citation

  • Stampoulis, D., and E. N. Anagnostou, 2012: Evaluation of global satellite rainfall products over continental Europe. J. Hydrometeor., 13, 588–603, doi:10.1175/JHM-D-11-086.1.

    • Search Google Scholar
    • Export Citation

  • Su, F. G., Y. Hong, and D. P. Lettenmaier, 2008: Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and its utility in hydrologic prediction in La Plata basin. J. Hydrometeor., 9, 622–640, doi:10.1175/2007JHM944.1.

    • Search Google Scholar
    • Export Citation

  • Su, F. G., H. Gao, G. J. Huffman, and D. P. Lettenmaier, 2011: Potential utility of the real-time TMPA-RT precipitation estimates in streamflow prediction. J. Hydrometeor., 12, 444–455, doi:10.1175/2010JHM1353.1.

    • Search Google Scholar
    • Export Citation

  • Tian, Y., C. D. Peters-Lidard, B. J. Choudhury, and M. Garcia, 2007: Multitemporal analysis of TRMM-based satellite precipitation products for land data assimilation applications. J. Hydrometeor., 8, 1165–1183, doi:10.1175/2007JHM859.1.

    • Search Google Scholar
    • Export Citation

  • Tian, Y., and Coauthors, 2009: Component analysis of errors in satellite-based precipitation estimates. J. Geophys. Res., 114, D24101, doi:10.1029/2009JD011949.

    • Search Google Scholar
    • Export Citation

  • Towler, E., and Coauthors, 2010: Modeling hydrologic and water quality extremes in a changing climate: A statistical approach based on extreme value theory. Water Resour. Res., 46, W11504, doi:10.1029/2009WR008876.

    • Search Google Scholar
    • Export Citation

  • Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 1205–1217, doi:10.1175/BAMS-84-9-1205.

    • Search Google Scholar
    • Export Citation

  • U.S. Department of the Interior Bureau of Reclamation, 1987: Design of small dams. 3rd ed. U.S. Government Printing Office Water Resources Tech. Publ., 860 pp. [Available online at http://www.usbr.gov/pmts/hydraulics_lab/pubs/manuals/SmallDams.pdf.]

  • Villarini, G., 2010: Evaluation of the research-version TMPA rainfall estimate at its finest spatial and temporal scales over the Rome metropolitan area. J. Appl. Meteor. Climatol., 49, 2591–2602, doi:10.1175/2010JAMC2462.1.

    • Search Google Scholar
    • Export Citation

  • Villarini, G., and W. F. Krajewski, 2007: Evaluation of the research version TMPA three-hourly 0.25° Ă— 0.25° rainfall estimates over Oklahoma. Geophys. Res. Lett., 34, L05402, doi:10.1029/2006GL029147.

    • Search Google Scholar
    • Export Citation

  • Wang, J., and Coauthors, 2011: The coupled routing and excess storage (CREST) distributed hydrological model. Hydrol. Sci. J., 56, 84–98, doi:10.1080/02626667.2010.543087.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 704 pp.

  • Wu, H., R. F. Adler, Y. Hong, Y. Tian, and F. Policelli, 2012: Evaluation of global flood detection using satellite-based rainfall and a hydrologic model. J. Hydrometeor., 13, 1268–1284, doi:10.1175/JHM-D-11-087.1.

    • Search Google Scholar
    • Export Citation

  • Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, 2012: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc., 93, 1401–1415, doi:10.1175/BAMS-D-11-00122.1.

    • Search Google Scholar
    • Export Citation

  • Yilmaz, K. K., R. F. Adler, Y. Tian, Y. Hong, and H. F. Pierce, 2010: Evaluation of a satellite-based global flood monitoring system. Int. J. Remote Sens., 31, 3763–3782, doi:10.1080/01431161.2010.483489.

    • Search Google Scholar
    • Export Citation
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