• Anjum, M. N., and Coauthors, 2018: Performance evaluation of latest integrated multi-satellite retrievals for Global Precipitation Measurement (IMERG) over the northern highlands of Pakistan. Atmos. Res., 205, 134146, https://doi.org/10.1016/j.atmosres.2018.02.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arnaud, Y., M. Desbois, and J. Maizi, 1992: Automatic tracking and characterization of African convective systems on Meteosat pictures. J. Appl. Meteor., 31, 443453, https://doi.org/10.1175/1520-0450(1992)031<0443:ATACOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashouri, H., K.-L. Hsu, S. Sorooshian, D. K. Braithwaite, K. R. Knapp, L. D. Cecil, B. R. Nelson, and O. P. Prat, 2015: PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Amer. Meteor. Soc., 96, 6983, https://doi.org/10.1175/BAMS-D-13-00068.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, W., T. L’Ecuyer, and J. M. Haynes, 2010: The distribution of rainfall over oceans from spaceborne radars. J. Appl. Meteor. Climatol., 49, 535543, https://doi.org/10.1175/2009JAMC2330.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beucher, F., J. P. Lafore, F. Karbou, and R. Roca, 2014: High-resolution prediction of a major convective period over West Africa. Quart. J. Roy. Meteor. Soc., 140, 14091425, https://doi.org/10.1002/qj.2225.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brocca, L., and Coauthors, 2014: Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos., 119, 51285141, https://doi.org/10.1002/2014JD021489.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S. S., R. A. Houze Jr., and B. E. Mapes, 1996: Multiscale variability of deep convection in relation to large-scale circulation in TOGA COARE. J. Atmos. Sci., 53, 13801409, https://doi.org/10.1175/1520-0469(1996)053<1380:MVODCI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coppin, D., G. Bellon, A. Pletzer, and C. Scott, 2020: Detecting and tracking coastal precipitation in the tropics: Methods and insights into multiscale variability of tropical precipitation. J. Climate, 33, 66896705, https://doi.org/10.1175/JCLI-D-19-0321.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cui, W., X. Dong, B. Xi, Z. Feng, and J. Fan, 2020: Can the GPM IMERG Final product accurately represent MCSs’ precipitation characteristics over the central and eastern United States? J. Hydrometeor., 21, 3957, https://doi.org/10.1175/JHM-D-19-0123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dias, J., N. Sakaeda, G. N. Kiladis, and K. Kikuchi, 2017: Influences of the MJO on the space-time organization of tropical convection. J. Geophys. Res. Atmos., 122, 80128032, https://doi.org/10.1002/2017JD026526.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, Z., X. Dong, B. Xi, S. A. McFarlane, A. Kennedy, B. Lin, and P. Minnis, 2012: Life cycle of midlatitude deep convective systems in a Lagrangian framework. J. Geophys. Res., 117, D23201, https://doi.org/10.1029/2012JD018362.

    • Search Google Scholar
    • Export Citation
  • Fiolleau, T., and R. Roca, 2013a: An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite. IEEE Trans. Geosci. Remote Sens., 51, 43024315, https://doi.org/10.1109/TGRS.2012.2227762.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fiolleau, T., and R. Roca, 2013b: Composite life cycle of tropical mesoscale convective systems from geostationary and low Earth orbit satellite observations: Method and sampling considerations. Quart. J. Roy. Meteor. Soc., 139, 941953, https://doi.org/10.1002/qj.2174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fiolleau, T., R. Roca, S. Cloché, D. Bouniol, and P. Raberanto, 2020: Homogenization of geostationary infrared imager channels for cold cloud studies using Megha-Tropiques/ScaRaB. IEEE Trans. Geosci. Remote Sens., 58, 66096622, https://doi.org/10.1109/TGRS.2020.2978171.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Funk, C. C., and Coauthors, 2014: A quasi-global precipitation time series for drought monitoring. U.S. Geological Survey Data Series 832, 4 pp., https://doi.org/10.3133/ds832.

    • Crossref
    • Export Citation
  • Futyan, J. M., and A. D. Del Genio, 2007: Deep convective system evolution over Africa and the tropical Atlantic. J. Climate, 20, 50415060, https://doi.org/10.1175/JCLI4297.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gosset, M., M. Alcoba, R. Roca, S. Cloché, and G. Urbani, 2018: Evaluation of TAPEER daily estimates and other GPM-era products against dense gauge networks in West Africa, analysing ground reference uncertainty. Quart. J. Roy. Meteor. Soc., 144, 255269, https://doi.org/10.1002/qj.3335.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffith, C. G., W. L. Woodley, P. G. Grube, D. W. Martin, J. Stout, and D. N. Sikdar, 1978: Rain estimation from geosynchronous satellite imagery—Visible and infrared studies. Mon. Wea. Rev., 106, 11531171, https://doi.org/10.1175/1520-0493(1978)106<1153:REFGSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamada, A., Y. N. Takayabu, C. Liu, and E. J. Zipser, 2015: Weak linkage between the heaviest rainfall and tallest storms. Nat. Commun., 6, 6213, https://doi.org/10.1038/ncomms7213.

    • 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
  • Houze, R. A., Jr., and C. P. Cheng, 1977: Radar characteristics of tropical convection observed during GATE: Mean properties and trends over the summer season. Mon. Wea. Rev., 105, 964980, https://doi.org/10.1175/1520-0493(1977)105<0964:RCOTCO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, K. L., X. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36, 11761190, https://doi.org/10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, K. L., H. V. Gupta, X. Gao, and S. Sorooshian, 1999: Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation. Water Resour. Res., 35, 16051618, https://doi.org/10.1029/1999WR900032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, K. L., Y. Hong, and S. Sorooshian, 2007: Rainfall estimation using a cloud patch classification map. Measuring Precipitation from Space, Springer, 329–342.

    • Crossref
    • Export Citation
  • 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., D. T. Bolvin, and E. J. Nelkin, 2015: Integrated Multi-satellitE Retrievals for GPM (IMERG) technical documentation. NASA/GSFC Code 612 Tech. Doc., 48 pp., http://pmm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf.

  • Huffman, G. J., and Coauthors, 2020: Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG). Satellite Precipitation Measurement, Springer, 343–353.

    • Crossref
    • Export Citation
  • Imaoka, K., and K. Nakamura, 2012: Statistical analysis of the life cycle of isolated tropical cold cloud systems using MTSAT-1R and TRMM data. Mon. Wea. Rev., 140, 35523572, https://doi.org/10.1175/MWR-D-11-00364.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inoue, T., D. Vila, K. Rajendran, A. Hamada, X. Wu, and L. A. Machado, 2009: Life cycle of deep convective systems over the eastern tropical Pacific observed by TRMM and GOES-W. J. Meteor. Soc. Japan, 87A, 381391, https://doi.org/10.2151/jmsj.87A.381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janowiak, J. E., V. E. Kousky, and R. J. Joyce, 2005: Diurnal cycle of precipitation determined from the CMORPH high spatial and temporal resolution global precipitation analyses. J. Geophys. Res., 110, D23105, https://doi.org/10.1029/2005JD006156.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, H., C. Liu, and E. J. Zipser, 2011: A TRMM-based tropical cyclone cloud and precipitation feature database. J. Appl. Meteor. Climatol., 50, 12551274, https://doi.org/10.1175/2011JAMC2662.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., and P. Xie, 2011: Kalman filter–based CMORPH. J. Hydrometeor., 12, 15471563, https://doi.org/10.1175/JHM-D-11-022.1.

  • 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
  • Karbalaee, N., K. Hsu, S. Sorooshian, and D. Braithwaite, 2017a: Bias adjustment of infrared-based rainfall estimation using passive microwave satellite rainfall data. J. Geophys. Res. Atmos., 122, 38593876, https://doi.org/10.1002/2016JD026037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karbalaee, N., P. E. Kirstetter, and J. J. Gourley, 2017b: Improving precipitation estimates over the western United States using GOES-R precipitation data. 2017 Fall Meeting, San Francisco, CA, Amer. Geophys. Union, Abstract H54F-06.

  • Kerns, B. W., and S. S. Chen, 2016: Large-scale precipitation tracking and the MJO over the Maritime Continent and Indo-Pacific warm pool. J. Geophys. Res. Atmos., 121, 87558776, https://doi.org/10.1002/2015JD024661.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kerns, B. W., and S. S. Chen, 2020: A 20-year climatology of Madden-Julian oscillation convection: Large-scale precipitation tracking from TRMM-GPM rainfall. J. Geophys. Res. Atmos., 125, e2019JD032142, https://doi.org/10.1029/2019JD032142.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kubota, T., and Coauthors, 2007: Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: Production and validation. IEEE Trans. Geosci. Remote Sens., 45, 22592275, https://doi.org/10.1109/TGRS.2007.895337.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., D. L. Randel, M. Kulie, N. Y. Wang, R. Ferraro, S. Joseph Munchak, and V. Petkovic, 2015: The evolution of the Goddard profiling algorithm to a fully parametric scheme. J. Atmos. Oceanic Technol., 32, 22652280, https://doi.org/10.1175/JTECH-D-15-0039.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laing, A. G., and M. Fritsch, 1993a: Mesoscale convective complexes in Africa. Mon. Wea. Rev., 121, 22542263, https://doi.org/10.1175/1520-0493(1993)121<2254:MCCIA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Laing, A. G., and M. Fritsch, 1993b: Mesoscale convective complexes over Indian monsoon region. J. Climate, 6, 911919, https://doi.org/10.1175/1520-0442(1993)006<0911:MCCOTI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, R., K. Wang, and D. Qi, 2018; Validating the integrated multisatellite retrievals for global precipitation measurement in terms of diurnal variability with hourly gauge observations collected at 50,000 stations in China. J. Geophys. Res. Atmos., 123, 10 42310 442, https://doi.org/10.1029/2018JD028991.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., E. J. Zipser, and S. W. Nesbitt, 2007: Global distribution of tropical deep convection: Different perspectives from TRMM infrared and radar data. J. Climate, 20, 489503, https://doi.org/10.1175/JCLI4023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, C., E. J. Zipser, D. J. Cecil, S. W. Nesbitt, and S. Sherwood, 2008: A cloud and precipitation feature database from nine years of TRMM observations. J. Appl. Meteor. Climatol., 47, 27122728, https://doi.org/10.1175/2008JAMC1890.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, Y., H. Wang, R. Zhang, W. Qian, and Z. Luo, 2013: Comparison of rainfall characteristics and convective properties of monsoon precipitation systems over South China and the Yangtze and Huai River basin. J. Climate, 26, 110132, https://doi.org/10.1175/JCLI-D-12-00100.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., and W. B. Rossow, 1993: Structural characteristics and radiative properties of tropical cloud clusters. Mon. Wea. Rev., 121, 32343260, https://doi.org/10.1175/1520-0493(1993)121<3234:SCARPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., and H. Laurent, 2004: The convective system area expansion over Amazonia and its relationships with convective system life duration and high-level wind divergence. Mon. Wea. Rev., 132, 714725, https://doi.org/10.1175/1520-0493(2004)132<0714:TCSAEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., W. B. Rossow, R. L. Guedes, and A. W. Walker, 1998: Life cycle variations of mesoscale convective systems over the Americas. Mon. Wea. Rev., 126, 16301654, https://doi.org/10.1175/1520-0493(1998)126<1630:LCVOMC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maranan, M., A. H. Fink, P. Knippertz, L. K. Amekudzi, W. A. Atiah, and M. Stengel, 2020: A process-based validation of GPM IMERG and its sources using a mesoscale rain gauge network in the West African forest zone. J. Hydrometeor., 21, 729749, https://doi.org/10.1175/JHM-D-19-0257.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marchand, R., G. G. Mace, T. Ackerman, and G. Stephens, 2008: Hydrometeor detection using Cloudsat—An Earth-orbiting 94-GHz cloud radar. J. Atmos. Oceanic Technol., 25, 519533, https://doi.org/10.1175/2007JTECHA1006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martin, D. W., and A. J. Schreiner, 1981: Characteristics of West African and east Atlantic cloud clusters: A survey from GATE. Mon. Wea. Rev., 109, 16711688, https://doi.org/10.1175/1520-0493(1981)109<1671:COWAAE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masunaga, H., 2012: A satellite study of the atmospheric forcing and response to moist convection over tropical and subtropical oceans. J. Atmos. Sci., 69, 150167, https://doi.org/10.1175/JAS-D-11-016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masunaga, H., 2013: A satellite study of tropical moist convection and environmental variability: A moisture and thermal budget analysis. J. Atmos. Sci., 70, 24432466, https://doi.org/10.1175/JAS-D-12-0273.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masunaga, H., M. Schröder, F. A. Furuzawa, C. Kummerow, E. Rustemeier, and U. Schneider, 2019: Inter-product biases in global precipitation extremes. Environ. Res. Lett., 14, 125016, https://doi.org/10.1088/1748-9326/ab5da9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McAnelly, R. L., and W. R. Cotton, 1989: The precipitation life cycle of mesoscale convective complexes over the central United States. Mon. Wea. Rev., 117, 784808, https://doi.org/10.1175/1520-0493(1989)117<0784:TPLCOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, D., and J. M. Fritsch, 1991: Mesoscale convective complexes in the western Pacific region. Mon. Wea. Rev., 119, 29782992, https://doi.org/10.1175/1520-0493(1991)119<2978:MCCITW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mohr, K. I., J. S. Famiglietti, and E. J. Zipser, 1999: The contribution to tropical rainfall with respect to convective system type, size, and intensity estimated from the 85-GHz ice-scattering signature. J. Appl. Meteor., 38, 596606, https://doi.org/10.1175/1520-0450(1999)038<0596:TCTTRW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nguyen, P., and Coauthors, 2017: Evaluation of CMIP5 model precipitation using PERSIANN-CDR. J. Hydrometeor., 18, 23132330, https://doi.org/10.1175/JHM-D-16-0201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nguyen, P., M. Ombadi, S. Sorooshian, K. Hsu, A. AghaKouchak, D. Braithwaite, H. Ashouri, and A. R. Thorstensen, 2018: The PERSIANN family of global satellite precipitation data: A review and evaluation of products. Hydrol. Earth Syst. Sci., 22, 58015816, https://doi.org/10.5194/hess-22-5801-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80, 22612288, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., A. Mekonnen, C. Pearl, and W. Goncalves, 2013: Tropical precipitation extremes. J. Climate, 26, 14571466, https://doi.org/10.1175/JCLI-D-11-00725.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., and J. R. Milford, 1993: On the generation of African squall lines. J. Climate, 6, 11811193, https://doi.org/10.1175/1520-0442(1993)006<1181:OTGOAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schumacher, C., and R. A. Houze Jr., 2003: Stratiform rain in the tropics as seen by the TRMM precipitation radar. J. Climate, 16, 17391756, https://doi.org/10.1175/1520-0442(2003)016<1739:SRITTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharifi, E., B. Saghafian, and R. Steinacker, 2019: Copula-based stochastic uncertainty analysis of satellite precipitation products. J. Hydrol., 570, 739754, https://doi.org/10.1016/j.jhydrol.2019.01.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., K. L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 20352046, https://doi.org/10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sorooshian, S., X. Gao, K. Hsu, R. A. Maddox, Y. Hong, H. V. Gupta, and B. Imam, 2002: Diurnal variability of tropical rainfall retrieved from combined GOES and TRMM satellite information. J. Climate, 15, 9831001, https://doi.org/10.1175/1520-0442(2002)015<0983:DVOTRR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, https://doi.org/10.1029/2008JD009982.

    • Search Google Scholar
    • Export Citation
  • Takahashi, H., and Z. J. Luo, 2014: Characterizing tropical overshooting deep convection from joint analysis of CloudSat and geostationary satellite observations. J. Geophys. Res. Atmos., 119, 112121, https://doi.org/10.1002/2013JD020972.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, J., G. J. Huffman, D. T. Bolvin, and E. J. Nelkin, 2019: Diurnal cycle of IMERG V06 precipitation. Geophys. Res. Lett., 46, 13 58413 592, https://doi.org/10.1029/2019GL085395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, G., M. P. Clark, S. M. Papalexiou, Z. Ma, and Y. Hong, 2020: Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets. Remote Sens. Environ., 240, 111697, https://doi.org/10.1016/j.rse.2020.111697.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, W. K., T. Iguchi, and S. Lang, 2019: Expanding the Goddard CSH algorithm for GPM: New extratropical retrievals. J. Appl. Meteor. Climatol., 58, 921946, https://doi.org/10.1175/JAMC-D-18-0215.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, F., S. Hou, L. Yang, H. Hu, and A. Hou, 2018: How does the evaluation of the GPM IMERG rainfall product depend on gauge density and rainfall intensity? J. Hydrometeor., 19, 339349, https://doi.org/10.1175/JHM-D-17-0161.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ushio, T., and Coauthors, 2009: A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data. J. Meteor. Soc. Japan, 87A, 137151, https://doi.org/10.2151/jmsj.87A.137.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vant-Hull, B., W. Rossow, and C. Pearl, 2016: Global comparisons of regional life cycle properties and motion of multiday convective systems: Tropical and midlatitude land and ocean. J. Climate, 29, 58375858, https://doi.org/10.1175/JCLI-D-15-0698.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velasco, I., and J. M. Fritsch, 1987: Mesoscale convective complexes in the Americas. J. Geophys. Res., 92, 95919613, https://doi.org/10.1029/JD092iD08p09591.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, H., L. Wang, J. He, F. Ge, Q. Chen, S. Tang, and S. Yao, 2020: Can the GPM IMERG hourly products replicate the variation in precipitation during the wet season over the Sichuan Basin, China? Earth Space Sci., 7, e2020EA001090, https://doi.org/10.1029/2020EA001090.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, R. H., D. S. Battisti, and G. Skok, 2017: Tracking precipitation events in time and space in gridded observational data. Geophys. Res. Lett., 44, 86378646, https://doi.org/10.1002/2017GL074011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, M., and R. A. Houze, 1987: Satellite-observed characteristics of winter monsoon cloud clusters. Mon. Wea. Rev., 115, 505519, https://doi.org/10.1175/1520-0493(1987)115<0505:SOCOWM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woodley, W. L., C. G. Griffith, J. S. Griffin, and S. C. Stromatt, 1980: The inference of GATE convective rainfall from SMS-1 imagery. J. Appl. Meteor., 19, 388408, https://doi.org/10.1175/1520-0450(1980)019<0388:TIOGCR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, W., and E. J. Zipser, 2011: Diurnal variations of precipitation, deep convection, and lightning over and east of the eastern Tibetan Plateau. J. Climate, 24, 448465, https://doi.org/10.1175/2010JCLI3719.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Young, A. H., K. R. Knapp, A. Inamdar, W. Hankins, and W. B. Rossow, 2018: The International Satellite Cloud Climatology Project H-series climate data record product. Earth Syst. Sci. Data, 10, 583593, https://doi.org/10.5194/essd-10-583-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., and R. A. Houze Jr., 1995: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123, 19411963, https://doi.org/10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., and E. N. Anagnostou, 2019: Evaluation of numerical weather model–based satellite precipitation adjustment in tropical mountainous regions. J. Hydrometeor., 20, 431445, https://doi.org/10.1175/JHM-D-18-0008.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, Y., K. Nelson, K. I. Mohr, G. J. Huffman, R. Levy, and M. Grecu, 2019: A spatial-temporal extreme precipitation database from GPM IMERG. J. Geophys. Res. Atmos., 124, 10 34410 363, https://doi.org/10.1029/2019JD030449.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 412 242 0
Full Text Views 302 214 47
PDF Downloads 294 190 38

Detection and Tracking of Tropical Convective Storms Based on Globally Gridded Precipitation Measurements: Algorithm and Survey over the Tropics

View More View Less
  • 1 Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, California
  • | 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • | 3 Department of Earth and Atmospheric Sciences, City University of New York, City College, New York
  • | 4 Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan
  • | 5 Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

This paper is the first attempt to document a simple convection-tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps to elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-20-0171.s1.

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

Publisher’s Note: This article was revised on 28 April 2021 to fix a labeling error in the x-axis for the two rightmost panels of Fig. 11.

Corresponding author: Hanii Takahashi, hanii.takahashi@jpl.nasa.gov

Abstract

This paper is the first attempt to document a simple convection-tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps to elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JAMC-D-20-0171.s1.

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

Publisher’s Note: This article was revised on 28 April 2021 to fix a labeling error in the x-axis for the two rightmost panels of Fig. 11.

Corresponding author: Hanii Takahashi, hanii.takahashi@jpl.nasa.gov

Supplementary Materials

    • Supplemental Materials (PDF 740.19 KB)
Save