Life Cycle of Precipitating Cloud Systems from Synergistic Satellite Observations: Evolution of Macrophysical Properties and Precipitation Statistics from Geostationary Cloud Tracking and GPM Active and Passive Microwave Measurements

Clément Guilloteau aDepartment of Civil and Environmental Engineering, University of California, Irvine, Irvine, California

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Efi Foufoula-Georgiou aDepartment of Civil and Environmental Engineering, University of California, Irvine, Irvine, California
bDepartment of Earth System Science, University of California, Irvine, Irvine, California

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Abstract

Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Clément Guilloteau, cguillot@uci.edu

Abstract

Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Clément Guilloteau, cguillot@uci.edu
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  • Ai, Y., W. Li, Z. Meng, and J. Li, 2016: Life cycle characteristics of MCSs in Middle East China tracked by geostationary satellite and precipitation estimates. Mon. Wea. Rev., 144, 25172530, https://doi.org/10.1175/MWR-D-15-0197.1.

    • Search Google Scholar
    • Export Citation
  • Berthet, S., R. Roca, J. P. Duvel, and T. Fiolleau, 2017: Subseasonal variability of mesoscale convective systems over the tropical northeastern Pacific. Quart. J. Roy. Meteor. Soc., 143, 10861094, https://doi.org/10.1002/qj.2992.

    • Search Google Scholar
    • Export Citation
  • Biasutti, M., 2019: Rainfall trends in the African Sahel: Characteristics, processes, and causes. Wiley Interdiscip. Rev.: Climate Change, 10, e591, https://doi.org/10.1002/wcc.591.

    • Search Google Scholar
    • Export Citation
  • Biasutti, M., D. S. Battisti, and E. S. Sarachik, 2004: Mechanisms controlling the annual cycle of precipitation in the tropical Atlantic sector in an atmospheric GCM. J. Climate, 17, 47084723, https://doi.org/10.1175/JCLI-3235.1.

    • Search Google Scholar
    • Export Citation
  • Bouniol, D., R. Roca, T. Fiolleau, and D. E. Poan, 2016: Macrophysical, microphysical, and radiative properties of tropical mesoscale convective systems over their life cycle. J. Climate, 29, 33533371, https://doi.org/10.1175/JCLI-D-15-0551.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Elsaesser, G. S., R. Roca, T. Fiolleau, A. D. Del Genio, and J. Wu, 2022: A simple model for tropical convective cloud shield area growth and decay rates informed by geostationary IR, GPM, and Aqua/AIRS satellite data. J. Geophys. Res. Atmos., 127, e2021JD035599, https://doi.org/10.1029/2021JD035599.

    • Search Google Scholar
    • Export Citation
  • Escrig, H., F. J. Batlles, J. Alonso, F. M. Baena, J. L. Bosch, I. B. Salbidegoitia, and J. I. Burgaleta, 2013: Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast. Energy, 55, 853859, https://doi.org/10.1016/j.energy.2013.01.054.

    • Search Google Scholar
    • Export Citation
  • Farnsworth, A., E. White, C. J. Williams, E. Black, and D. R. Kniveton, 2011: Understanding the large scale driving mechanisms of rainfall variability over Central Africa. African Climate and Climate Change: Physical, Social and Political Perspectives, C. Williams and D. Kniveton, Eds., Advances in Global Change Research, Vol. 43, Springer, 101–122.

  • Feng, Z., and Coauthors, 2021: A global high‐resolution mesoscale convective system database using satellite‐derived cloud tops, surface precipitation, and tracking. J. Geophys. Res. Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202.

    • Search Google Scholar
    • Export Citation
  • Feng, Z., J. Hardin, H. C. Barnes, J. Li, L. R. Leung, A. Varble, and Z. Zhang, 2023: PyFLEXTRKR: A flexible feature tracking Python software for convective cloud analysis. Geosci. Model Dev., 16, 27532776, https://doi.org/10.5194/gmd-16-2753-2023.

    • 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.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Garreaud, R. D., M. Vuille, R. Compagnucci, and J. Marengo, 2009: Present-day South American climate. Palaeogeogr. Palaeoclimatol. Palaeoecol., 281, 180195, https://doi.org/10.1016/j.palaeo.2007.10.032.

    • Search Google Scholar
    • Export Citation
  • Gorooh, V. A., A. A. Asanjan, P. Nguyen, K. Hsu, and S. Sorooshian, 2022: Deep neural network high Spatiotemporal Resolution Precipitation Estimation (Deep-STEP) using passive microwave and infrared data. J. Hydrometeor., 23, 597617, https://doi.org/10.1175/JHM-D-21-0194.1.

    • Search Google Scholar
    • Export Citation
  • Grecu, M., W. S. Olson, S. J. Munchak, S. Ringerud, L. Liao, Z. Haddad, B. L. Kelley, and S. F. McLaughlin, 2016: The GPM combined algorithm. J. Atmos. Oceanic Technol., 33, 22252245, https://doi.org/10.1175/JTECH-D-16-0019.1.

    • Search Google Scholar
    • Export Citation
  • Gu, G., and R. F. Adler, 2004: Seasonal evolution and variability associated with the West African monsoon system. J. Climate, 17, 33643377, https://doi.org/10.1175/1520-0442(2004)017<3364:SEAVAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gu, G., and R. F. Adler, 2006: Interannual rainfall variability in the tropical Atlantic region. J. Geophys. Res., 111, D02106, https://doi.org/10.1029/2005JD005944.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Guilloteau, C., E. Foufoula-Georgiou, P. Kirstetter, J. Tan, and G. J. Huffman, 2021: How well do multisatellite products capture the space–time dynamics of precipitation? Part I: Five products assessed via a wavenumber–frequency decomposition. J. Hydrometeor., 22, 28052823, https://doi.org/10.1175/JHM-D-21-0075.1.

    • Search Google Scholar
    • Export Citation
  • Guilloteau, C., E. Foufoula-Georgiou, P. Kirstetter, J. Tan, and G. J. Huffman, 2022: How well do multisatellite products capture the space–time dynamics of precipitation? Part II: Building an error model through spectral system identification. J. Hydrometeor., 23, 13831399, https://doi.org/10.1175/JHM-D-22-0041.1.

    • Search Google Scholar
    • Export Citation
  • Guilloteau, C., P. V. V. Le, and E. Foufoula-Georgiou, 2023: Constraining the multiscale structure of geophysical fields in machine-learning: The case of precipitation. IEEE Geosci. Remote Sens. Lett., 20, 7503405, https://doi.org/10.1109/LGRS.2023.3284278.

    • Search Google Scholar
    • Export Citation
  • Hanel, R. A., J. Licht, W. Nordberg, R. A. Stampfl, and W. G. Stroud, 1960: The satellite Vanguard II: Cloud cover experiment. IRE Trans. Mil. Electron., MIL-4, 245247, https://doi.org/10.1109/IRET-MIL.1960.5008229.

    • Search Google Scholar
    • Export Citation
  • Henderson, D. S., C. D. Kummerow, D. A. Marks, and W. Berg, 2017: A regime-based evaluation of TRMM oceanic precipitation biases. J. Atmos. Oceanic Technol., 34, 26132635, https://doi.org/10.1175/JTECH-D-16-0244.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., K. L. Rasmussen, M. D. Zuluaga, and S. R. Brodzik, 2015: The variable nature of convection in the tropics and subtropics: A legacy of 16 years of the tropical rainfall measuring mission satellite. Rev. Geophys., 53, 9941021, https://doi.org/10.1002/2015RG000488.

    • Search Google Scholar
    • Export Citation
  • Kidd, C., G. Huffman, V. Maggioni, P. Chambon, and R. Oki, 2021: The global satellite precipitation constellation: Current status and future requirements. Bull. Amer. Meteor. Soc., 102, E1844E1861, https://doi.org/10.1175/BAMS-D-20-0299.1.

    • Search Google Scholar
    • Export Citation
  • Li, R., C. Guilloteau, P.-E. Kirstetter, and E. Foufoula-Georgiou, 2023: How well does the IMERG satellite precipitation product capture the timing of precipitation events? J. Hydrol., 620, 129563, https://doi.org/10.1016/j.jhydrol.2023.129563.

    • Search Google Scholar
    • Export Citation
  • Marengo, J. A., and C. A. Nobre, 2001: General characteristics and variability of climate in the Amazon Basin and its links to the global climate system. The Biogeochemistry of the Amazon Basin, M. E. McClain, R. Victoria, and J. E. Richey, Eds., Oxford University Press, 17–41.

  • Nicholson, S. E., 2018: Climate of the Sahel and West Africa. Oxford Research Encyclopedia: Climate Science, Oxford University Press, 1–45.

  • Petković, V., M. Orescanin, P. Kirstetter, C. Kummerow, and R. Ferraro, 2019: Enhancing PMW satellite precipitation estimation: Detecting convective class. J. Atmos. Oceanic Technol., 36, 23492363, https://doi.org/10.1175/JTECH-D-19-0008.1.

    • Search Google Scholar
    • Export Citation
  • Pfreundschuh, S., P. J. Brown, C. D. Kummerow, P. Eriksson, and T. Norrestad, 2022: GPROF-NN: A neural-network-based implementation of the Goddard profiling algorithm. Atmos. Meas. Tech., 15, 50335060, https://doi.org/10.5194/amt-15-5033-2022.

    • Search Google Scholar
    • Export Citation
  • Pfreundschuh, S., C. Guilloteau, P. J. Brown, C. D. Kummerow, and P. Eriksson, 2024: GPROF V7 and beyond: Assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean. Atmos. Meas. Tech., 17, 515538, https://doi.org/10.5194/amt-17-515-2024.

    • Search Google Scholar
    • Export Citation
  • Randel, D. L., C. D. Kummerow, and S. Ringerud, 2020: The Goddard Profiling (GPROF) precipitation retrieval algorithm. Satellite Precipitation Measurement, V. Levizzani, et al., Eds., Advances in Global Change Research, Vol. 67, Springer, 141–152.

  • Roca, R., and T. Fiolleau, 2020: Extreme precipitation in the tropics is closely associated with long-lived convective systems. Commun. Earth Environ., 1, 18, https://doi.org/10.1038/s43247-020-00015-4.

    • Search Google Scholar
    • Export Citation
  • Roca, R., J. Aublanc, P. Chambon, T. Fiolleau, and N. Viltard, 2014: Robust observational quantification of the contribution of mesoscale convective systems to rainfall in the tropics. J. Climate, 27, 49524958, https://doi.org/10.1175/JCLI-D-13-00628.1.

    • Search Google Scholar
    • Export Citation
  • Roca, R., T. Fiolleau, and D. Bouniol, 2017: A simple model of the life cycle of mesoscale convective systems cloud shield in the tropics. J. Climate, 30, 42834298, https://doi.org/10.1175/JCLI-D-16-0556.1.

    • Search Google Scholar
    • Export Citation
  • Schumacher, C., and A. Funk, 2023: Assessing convective‐stratiform precipitation regimes in the tropics and extratropics with the GPM satellite radar. Geophys. Res. Lett., 50, e2023GL102786, https://doi.org/10.1029/2023GL102786.

    • Search Google Scholar
    • Export Citation
  • Smith, E. A., and D. R. Phillips, 1972: Automated cloud tracking using precisely aligned digital ATS pictures. IEEE Trans. Comput., C-21, 715729, https://doi.org/10.1109/T-C.1972.223574.

    • Search Google Scholar
    • Export Citation
  • Sullivan, S. C., K. A. Schiro, C. Stubenrauch, and P. Gentine, 2019: The response of tropical organized convection to El Niño warming. J. Geophys. Res. Atmos., 124, 84818500, https://doi.org/10.1029/2019JD031026.

    • Search Google Scholar
    • Export Citation
  • Turk, F. J., and Coauthors, 2021: Adapting passive microwave-based precipitation algorithms to variable microwave land surface emissivity to improve precipitation estimation from the GPM constellation. J. Hydrometeor., 22, 17551781, https://doi.org/10.1175/JHM-D-20-0296.1.

    • Search Google Scholar
    • Export Citation
  • Wolf, D. E., D. J. Hall, and R. M. Endlich, 1977: Experiments in automatic cloud tracking using SMS-GOES data. J. Appl. Meteor., 16, 12191230, https://doi.org/10.1175/1520-0450(1977)016<1219:EIACTU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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