• 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, https://doi.org/10.1029/2010JD014741.

    • Search Google Scholar
    • Export Citation
  • Blake, E. S., and D. A. Zelinsky, 2018: Tropical cyclone report: Hurricane Harvey (AL092017), 17 August–1 September 2017. NHC Tech. Rep., 77 pp., www.nhc.noaa.gov/data/tcr/AL092017_Harvey.pdf.

  • Blumenfeld, J., 2015: From TRMM to GPM: The evolution of NASA precipitation data. NASA, https://earthdata.nasa.gov/learn/articles/trmm-to-gpm.

  • Chen, F., Y. Fu, and Y. Yang, 2019: Regional variability of precipitation in tropical cyclones over the western North Pacific revealed by the GPM Dual-Frequency Precipitation Radar and Microwave Imager. J. Geophys. Res. Atmos., 124, 11 28111 296, https://doi.org/10.1029/2019JD031075.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M., and Coauthors, 2020: Can remote sensing technologies capture the extreme precipitation event and its cascading hydrological response? A case study of Hurricane Harvey using EF5 modeling framework. Remote Sens., 12, 445, https://doi.org/10.3390/rs12030445.

    • Crossref
    • 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, 413, https://doi.org/10.1016/j.jhydrol.2012.12.026.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y., E. E. Ebert, K. J. E. Walsh, and N. E. Davidson, 2013a: Evaluation of TMPA 3B42 daily precipitation estimates of tropical cyclone rainfall over Australia. J. Geophy. Res. Atmos., 118, 11 96611 978, https://doi.org/10.1002/2013JD020319.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Y., E. E. Ebert, K. J. E. Walsh, and N. E. Davidson, 2013b: Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAIN data. J. Geophys. Res. Atmos., 118, 21842196, https://doi.org/10.1002/jgrd.50250.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeGaetano, A. T., 2000: A serially complete simulated observation time metadata file for U.S. Daily Historical Climatology Network stations. Bull. Amer. Meteor. Soc., 81, 4968, https://doi.org/10.1175/1520-0477(2000)081<0049:ASCSOT>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deo, A., K. J. E. Walsh, and A. Peltier, 2017: Evaluation of TMPA 3B42 precipitation estimates during the passage of tropical cyclones over New Caledonia. Theor. Appl. Climatol., 129, 711727, https://doi.org/10.1007/s00704-016-1803-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dominguez, C., and V. Magaña, 2018: The role of tropical cyclones in precipitation over the tropical and subtropical North America. Front. Earth Sci., 6, 19, https://doi.org/10.3389/feart.2018.00019.

    • Crossref
    • 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, 4764, https://doi.org/10.1175/BAMS-88-1-47.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fang, J., W. Yang, Y. Luan, J. Du, A. Lin, and L. Zhao, 2019: Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China. Atmos. Res., 223, 2438, https://doi.org/10.1016/j.atmosres.2019.03.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fritz, C., Z. Wang, S. W. Nesbitt, and T. J. Dunkerton, 2016: Vertical structure and contribution of different types of precipitation during Atlantic tropical cyclone formation as revealed by TRMM PR. Geophys. Res. Lett., 43, 894901, https://doi.org/10.1002/2015GL067122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebregiorgis, A. S., and Coauthors, 2018: To what extent is the day 1 GPM IMERG satellite precipitation estimate improved as compared to TRMM TMPA-RT? J. Geophys. Res. Atmos., 123, 16941707, https://doi.org/10.1002/2017JD027606.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Golub, G. H., and C. F. Van Loan, 1996: Matrix Computations. 3d ed. The Johns Hopkins University Press, 694 pp.

  • Guilloteau, C., E. Foufoula-Georgiou, and C. D. Kummerow, 2017: Global multiscale evaluation of satellite passive microwave retrieval of precipitation during the TRMM and GPM eras: Effective resolution and regional diagnostics for future algorithm development. J. Hydrometeor., 18, 30513070, https://doi.org/10.1175/JHM-D-17-0087.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guirguis, K. J., and R. Avissar, 2008: A precipitation climatology and dataset intercomparison for the western United States. J. Hydrometeor., 9, 825841, https://doi.org/10.1175/2008JHM832.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 373388, https://doi.org/10.1016/j.atmosres.2009.06.015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • He, Z., L. Yang, F. Tian, G. Ni, A. Hou, and H. Lu, 2017: Intercomparisons of rainfall estimates from TRMM and GPM multisatellite products over the upper Mekong River basin. J. Hydrometeor., 18, 413430, https://doi.org/10.1175/JHM-D-16-0198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hegerl, G. C., and Coauthors, 2015: Challenges in quantifying changes in the global water cycle. Bull. Amer. Meteor. Soc., 96, 10971115, https://doi.org/10.1175/BAMS-D-13-00212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, Y., K.-L. Hsu, S. Sorooshian, and X. Gao, 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 18341853, https://doi.org/10.1175/JAM2173.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., 2020: The transition in multi-satellite products from TRMM to GPM (TMPA to IMERG). NASA Doc., 5 pp., https://gpm.nasa.gov/resources/documents/transition-multi-satellite-products-trmm-gpm-tmpa-imerg.

  • Huffman, G. J., and D. T. Bolvin, 2018a: TRMM and other data precipitation data set documentation. NASA Doc., 46 pp., https://pmm.nasa.gov/sites/default/files/document_files/3B42_3B43_doc_V7_180426.pdf.

  • Huffman, G. J., and D. T. Bolvin, 2018b: Real-time TRMM multi-satellite precipitation analysis data set documentation. NASA Doc., 51 pp., https://gpm.nasa.gov/sites/default/files/document_files/3B4XRT_doc_V7_180426.pdf.

  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, https://doi.org/10.1175/JHM560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., R. F. Adler, D. T. Bolvin, and E. J. Nelkin, 2010: The TRMM MultiSatellite Precipitation Analysis (TMPA). Satellite Rainfall Applications for Surface Hydrology, M. Gebremichael and F. Hossain, Eds., Springer, 3–22.

    • Crossref
    • Export Citation
  • Huffman, G. J., D. T. Bolvin, E. J. Nelkin, and J. Tan, 2019: Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation. NASA Tech. Doc., 77 pp., https://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc_190909.pdf.

  • Huffman, G. J., and Coauthors, 2020: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG). Advances in Global Change Research, V. Levizzani et al., Eds., Springer, 343–353.

    • Crossref
    • Export Citation
  • Jiang, S., and Coauthors, 2018: Statistical and hydrological evaluation of the latest Integrated Multi-satellitE Retrievals for GPM (IMERG) over a midlatitude humid basin in South China. Atmos. Res., 214, 418429, https://doi.org/10.1016/j.atmosres.2018.08.021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., and E. J. Zipser, 2010: Contribution of tropical cyclones to the global precipitation from eight seasons of TRMM data: Regional, seasonal, and interannual variations. J. Climate, 23, 15261543, https://doi.org/10.1175/2009JCLI3303.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., and E. M. Ramirez, 2013: Necessary conditions for tropical cyclone rapid intensification as derived from 11 years of TRMM data. J. Climate, 26, 64596470, https://doi.org/10.1175/JCLI-D-12-00432.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., C. Tao, and Y. Pei, 2019: Estimation of tropical cyclone intensity in the North Atlantic and northeastern Pacific basins using TRMM satellite passive microwave observations. J. Appl. Meteor. Climatol., 58, 185197, https://doi.org/10.1175/JAMC-D-18-0094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, P. W., 1999: First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Wea. Rev., 127, 22042210, https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487503, https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalpić, D., N. Hlupić, and M. Lovrić, 2011: Student’s t-tests. International Encyclopedia of Statistical Science, M. Lovric, Ed., Springer, 1559–1563.

    • Crossref
    • Export Citation
  • Khouakhi, A., G. Villarini, and G. A. Vecchi, 2017: Contribution of tropical cyclones to rainfall at the global scale. J. Climate, 30, 359372, https://doi.org/10.1175/JCLI-D-16-0298.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., and G. Huffman, 2011: Global Precipitation Measurement. Meteor. Appl., 18, 334353, https://doi.org/10.1002/met.284.

  • Kidd, C., A. Becker, G. J. Huffman, C. L. Muller, P. Joe, G. Skofronick-Jackson, and D. B. Kirschbaum, 2017: So, how much of the Earth’s surface is covered by rain gauges? Bull. Amer. Meteor. Soc., 98, 6978, https://doi.org/10.1175/BAMS-D-14-00283.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidder, S. Q., and Coauthors, 2000: Satellite analysis of tropical cyclones using the Advanced Microwave Sounding Unit (AMSU). Bull. Amer. Meteor. Soc., 81, 12411260, https://doi.org/10.1175/1520-0477(2000)081<1241:SAOTCU>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, K., J. Park, J. Baik, and M. Choi, 2017: Evaluation of topographical and seasonal feature using GPM IMERG and TRMM 3B42 over Far-East Asia. Atmos. Res., 187, 95105, https://doi.org/10.1016/j.atmosres.2016.12.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klazura, G. E., J. M. Thomale, D. S. Kelly, and P. Jendrowski, 1999: A comparison of NEXRAD WSR-88D radar estimates of rain accumulation with gauge measurements for high- and low-reflectivity horizontal gradient precipitation events. J. Atmos. Oceanic Technol., 16, 18421850, https://doi.org/10.1175/1520-0426(1999)016<1842:ACONWR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., H. J. Diamond, J. P. Kossin, M. C. Kruk, and C. J. Schreck, 2018: International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4. NOAA/National Centers for Environmental Information, accessed 20 April 2019, https://doi.org/10.25921/82ty-9e16.

    • Crossref
    • Export Citation
  • Kunkel, K. E., and Coauthors, 2013: Monitoring and understanding trends in extreme storms: State of knowledge. Bull. Amer. Meteor. Soc., 94, 499514, https://doi.org/10.1175/BAMS-D-11-00262.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Larson, J., Y. Zhou, and R. W. Higgins, 2005: Characteristics of landfalling tropical cyclones in the United States and Mexico: Climatology and interannual variability. J. Climate, 18, 12471262, https://doi.org/10.1175/JCLI3317.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Legates, D. R., and G. J. McCabe Jr., 1999: Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res., 35, 233241, https://doi.org/10.1029/1998WR900018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Z., X. Li, Y. Wang, and S. M. Quiring, 2019: Impact of climate change on precipitation patterns in Houston, Texas, USA. Anthropocene, 25, 100193, https://doi.org/10.1016/j.ancene.2019.100193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Libertino, A., A. Sharma, V. Lakshmi, and P. Claps, 2016: A global assessment of the timing of extreme rainfall from TRMM and GPM for improving hydrologic design. Environ. Res. Lett., 11, 054003, https://doi.org/10.1088/1748-9326/11/5/054003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, J., and T. Qian, 2019: Rapid intensification of tropical cyclones observed by AMSU satellites. Geophys. Res. Lett., 46, 70547062, https://doi.org/10.1029/2019GL083488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Z., 2016: Comparison of Integrated Multisatellite Retrievals for GPM (IMERG) and TRMM Multisatellite Precipitation Analysis (TMPA) monthly precipitation products: Initial results. J. Hydrometeor., 17, 777790, https://doi.org/10.1175/JHM-D-15-0068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lonfat, M., F. D. Marks, and S. Y. S. Chen, 2004: Precipitation distribution in tropical cyclones using the Tropical Rainfall Measuring Mission (TRMM) microwave imager: A global perspective. Mon. Wea. Rev., 132, 16451660, https://doi.org/10.1175/1520-0493(2004)132<1645:PDITCU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lopes, R. H. C., 2011: Kolmogorov-Smirnov test. International Encyclopedia of Statistical Science, M. Lovric, Ed., Springer, 718–720.

    • Crossref
    • Export Citation
  • Matyas, C. J., 2014: Conditions associated with large rain-field areas for tropical cyclones landfalling over Florida. Phys. Geogr., 35, 93106, https://doi.org/10.1080/02723646.2014.893476.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mendelsohn, R., K. Emanuel, S. Chonabayashi, and L. Bakkensen, 2012: The impact of climate change on global tropical cyclone damage. Nat. Climate Change, 2, 205209, https://doi.org/10.1038/nclimate1357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menne, M. J., and Coauthors, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), version 3 [Version 3.12]. NOAA National Climatic Data Center, accessed 12 February 2019, https://doi.org/10.7289/V5D21VHZ.

    • Crossref
    • Export Citation
  • Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol., 10, 282290, https://doi.org/10.1016/0022-1694(70)90255-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NCAR, 2014: The Climate Data Guide: Regridding overview. https://climatedataguide.ucar.edu/climate-data-tools-and-analysis/regridding-overview.

  • NOAA/NCEI, 2021: U.S. billion-dollar weather and climate disasters. NOAA/NCEI, www.ncdc.noaa.gov/billions/.

  • Passive Microwave Algorithm Team, 2018: Global Precipitation Measurement (GPM) Mission: GPROF2017 Version 1 and Version 2 (used in GPM V5 processing). Algorithm Theoretical Basis Doc., 65 pp., http://rain.atmos.colostate.edu/ATBD/ATBD_GPM_V5B_April15_2018.pdf.

  • Pei, Y., and H. Jiang, 2018: Quantification of precipitation asymmetries of tropical cyclones using 16-year TRMM observations. J. Geophys. Res. Atmos., 123, 80918114, https://doi.org/10.1029/2018JD028545.

    • Search Google Scholar
    • Export Citation
  • Peng, F., S. Zhao, C. Chen, D. Cong, Y. Wang, and H. Ouyang, 2020: Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China. Atmos. Res., 236, 104814, https://doi.org/10.1016/j.atmosres.2019.104814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prat, O. P., and B. R. Nelson, 2016: On the link between tropical cyclones and daily rainfall extremes derived from global satellite observations. J. Climate, 29, 61276135, https://doi.org/10.1175/JCLI-D-16-0289.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Prat, O. P., and B. R. Nelson, 2013: Mapping the world’s tropical cyclone rainfall contribution over land using the TRMM Multi-satellite Precipitation Analysis. Water Resour. Res., 49, 72367254, https://doi.org/10.1002/wrcr.20527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rendon, S. H., B. E. Vieux, and C. S. Pathak, 2013: Continuous forecasting and evaluation of derived Z-R relationships in a sparse rain gauge network using NEXRAD. J. Hydrol. Eng., 18, 175182, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000579.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rios Gaona, M. F., G. Villarini, W. Zhang, and G. A. Vecchi, 2018: The added value of IMERG in characterizing rainfall in tropical cyclones. Atmos. Res., 209, 95102, https://doi.org/10.1016/j.atmosres.2018.03.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sapiano, M. R. P., and P. A. Arkin, 2009: An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeor., 10, 149166, https://doi.org/10.1175/2008JHM1052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serafin, R. J., and J. W. Wilson, 2000: Operational weather radar in the United States: Progress and opportunity. Bull. Amer. Meteor. Soc., 81, 501518, https://doi.org/10.1175/1520-0477(2000)081<0501:OWRITU>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shepard, D., 1968: A two-dimensional interpolation function for irregularly-spaced data. ACM '68: Proceedings of the 1968 23rd ACM National Conference, ACM, 517–524, https://doi.org/10.1145/800186.810616.

    • Crossref
    • Export Citation
  • Shepherd, J. M., A. Grundstein, and T. L. Mote, 2007: Quantifying the contribution of tropical cyclones to extreme rainfall along the coastal southeastern United States. Geophys. Res. Lett., 34, L23810, https://doi.org/10.1029/2007GL031694.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevenson, S. N., and R. S. Schumacher, 2014: A 10-year survey of extreme rainfall events in the central and eastern United States using gridded multisensor precipitation analyses. Mon. Wea. Rev., 142, 31473162, https://doi.org/10.1175/MWR-D-13-00345.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stewart, S. R., and R. Berg, 2019: Tropical cyclone report: Hurricane Florence (AL062018). NHC Tech. Rep., 98 pp., www.nhc.noaa.gov/data/tcr/AL062018_Florence.pdf.

  • Sun, Q., C. Miao, Q. Duan, H. Ashouri, S. Sorooshian, and K.-L. Hsu, 2018: A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Rev. Geophys., 56, 79107, https://doi.org/10.1002/2017RG000574.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, M. L., and H. Santo, 2018: Comparison of GPM IMERG, TMPA 3B42 and PERSIANN-CDR satellite precipitation products over Malaysia. Atmos. Res., 202, 6376, https://doi.org/10.1016/j.atmosres.2017.11.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, G., Y. Ma, D. Long, L. Zhong, and Y. Hong, 2016: Evaluation of GPM Day-1 IMERG and TMPA version-7 legacy products over Mainland China at multiple spatiotemporal scales. J. Hydrol., 533, 152167, https://doi.org/10.1016/j.jhydrol.2015.12.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, J. R., 1997: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. 2nd ed. University Science Books, 327 pp.

  • Thakur, M. K., T. V. Lakshmi Kumar, S. Dwivedi, and M. S. Narayanan, 2018: On the rainfall asymmetry and distribution in tropical cyclones over Bay of Bengal using TMPA and GPM rainfall products. Nat. Hazards, 94, 819832, https://doi.org/10.1007/s11069-018-3426-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Touma, D., A. M. Michalak, D. L. Swain, and N. S. Diffenbaugh, 2018: Characterizing the spatial scales of extreme daily precipitation in the United States. J. Climate, 31, 80238037, https://doi.org/10.1175/JCLI-D-18-0019.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van Oldenborgh, G. J., and Coauthors, 2017: Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environ. Res. Lett., 12, 124009, https://doi.org/10.1088/1748-9326/aa9ef2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Villarini, G., J. A. Smith, M. L. Baeck, T. Marchok, and G. A. Vecchi, 2011: Characterization of rainfall distribution and flooding associated with U.S. landfalling tropical cyclones: Analyses of Hurricanes Frances, Ivan, and Jeanne (2004). J. Geophys. Res., 116, D23116, https://doi.org/10.1029/2011JD016175.

    • Search Google Scholar
    • Export Citation
  • Wang, W., H. Lu, T. Zhao, L. Jiang, and J. Shi, 2017: Evaluation and comparison of daily rainfall from latest GPM and TRMM products over the Mekong River basin. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 10, 25402549, https://doi.org/10.1109/JSTARS.2017.2672786.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weissman, D. E., and M. A. Bourassa, 2011: The influence of rainfall on scatterometer backscatter within tropical cyclone environments—Implications on parameterization of sea-surface stress. IEEE Trans. Geosci. Remote Sens., 49, 48054814, https://doi.org/10.1109/TGRS.2011.2170842.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Willmott, C. J., and K. Matsuura, 2005: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res., 30, 7982, https://doi.org/10.3354/cr030079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, R., F. Tian, L. Yang, H. Hu, H. Lu, and A. Hou, 2017: Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network. J. Geophys. Res. Atmos., 122, 910924, https://doi.org/10.1002/2016JD025418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, F., and Coauthors, 2017: Assessment of GPM and TRMM multi-satellite precipitation products in streamflow simulations in a data-sparse mountainous watershed in Myanmar. Remote Sens., 9, 302, https://doi.org/10.3390/rs9030302.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, L., and S. M. Quiring, 2017: An extraction method for long-term tropical cyclone precipitation from daily rain gauges. J. Hydrometeor., 18, 25592576, https://doi.org/10.1175/JHM-D-16-0291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, L., S. M. Quiring I. Guneralp, and W. G. Peacock, 2015: Variations in tropical cyclone-related discharge in four watersheds near Houston, Texas. Climate Risk Manage., 7, 110, https://doi.org/10.1016/j.crm.2015.01.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zulkafli, Z., W. Buytaert, C. Onof, B. Manz, E. Tarnavsky, W. Lavado, and J.-L. Guyot, 2014: A comparative performance analysis of TRMM 3B42 (TMPA) versions 6 and 7 for hydrological applications over Andean–Amazon river basins. J. Hydrometeor., 15, 581592, https://doi.org/10.1175/JHM-D-13-094.1.

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Comparison of Two Multisatellite Algorithms for Estimation of Tropical Cyclone Precipitation in the United States and Mexico: TMPA and IMERG

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  • 1 Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, China
  • 2 Department of Geography, Environment, and Tourism, Western Michigan University, Kalamazoo, Michigan
  • 3 Atmospheric Sciences Program, Department of Geography, The Ohio State University, Columbus, Ohio
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Abstract

Tropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico, and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multisatellite Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multisatellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event, and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash–Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the time scales.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shanshui Yuan, yuan.750@osu.edu

Abstract

Tropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico, and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multisatellite Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multisatellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event, and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash–Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the time scales.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shanshui Yuan, yuan.750@osu.edu
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