• Abel, S. J., and I. A. Boutle, 2012: An improved representation of the raindrop size distribution for single-moment microphysics schemes. Quart. J. Roy. Meteor. Soc., 138, 21512162, https://doi.org/10.1002/qj.1949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and Coauthors, 2018: The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138.

    • Search Google Scholar
    • Export Citation
  • Andersson, A., C. Klepp, K. Fennig, S. Bakan, H. Grassl, and J. Schulz, 2011: Evaluation of HOAPS-3 ocean surface freshwater flux components. J. Appl. Meteor. Climatol., 50, 379398, https://doi.org/10.1175/2010JAMC2341.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Battaglia, A., J. M. Haynes, T. L’Ecuyer, and C. Simmer, 2008: Identifying multiple-scattering-affected profiles in CloudSat observations over the oceans. J. Geophys. Res., 113, D00A17, https://doi.org/10.1029/2008JD009960.

    • Search Google Scholar
    • Export Citation
  • Behrangi, A., and Y. Song, 2020: A new estimate for oceanic precipitation amount and distribution using complementary precipitation observations from space and comparison with GPCP. Environ. Res. Lett., 15, 124042, https://doi.org/10.1088/1748-9326/abc6d1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., M. Lebsock, S. Wong, and B. Lambrigtsen, 2012: On the quantification of oceanic rainfall using spaceborne sensors. J. Geophys. Res., 117, D20105, https://doi.org/10.1029/2012JD017979.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., G. Stephens, R. F. Adler, G. J. Huffman, B. Lambrigtsen, and M. Lebsock, 2014: An update on the oceanic precipitation rate and its zonal distribution in light of advanced observations from space. J. Climate, 27, 39573965, https://doi.org/10.1175/JCLI-D-13-00679.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behrangi, A., and Coauthors, 2016: Status of high-latitude precipitation estimates from observations and reanalyses. J. Geophys. Res. Atmos., 121, 44684486, https://doi.org/10.1002/2015JD024546.

    • 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
  • Clough, S. A., and Coauthors, 2005: Atmospheric radiative transfer modeling: A summary of the AER codes. J. Quant. Spectrosc. Radiat. Transfer, 91, 233244, https://doi.org/10.1016/j.jqsrt.2004.05.058.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dolan, B., B. Fuchs, S. A. Rutledge, E. A. Barnes, and E. J. Thompson, 2018: Primary modes of global drop size distributions. J. Atmos. Sci., 75, 14531476, https://doi.org/10.1175/JAS-D-17-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duncan, D. I., P. Eriksson, S. Pfrundschuh, C. Klepp, and D. C. Jones, 2019: On the distinctiveness of observed oceanic raindrop distributions. Atmos. Chem. Phys., 19, 69696984, https://doi.org/10.5194/acp-19-6969-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dzambo, A. M., T. L’Ecuyer, O. O. Sy, and S. Tanelli, 2019: The observed structure and precipitation characteristics of southeast Atlantic stratocumulus from airborne radar during ORACLES 2016–17. J. Appl. Meteor. Climatol., 58, 21972215, https://doi.org/10.1175/JAMC-D-19-0032.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Giangrande, S. E., D. Wang, M. J. Bartholomew, M. P. Jensen, D. B. Mechem, J. C. Hardin, and R. Wood, 2019: Midlatitude oceanic cloud and precipitation properties as sampled by the ARM Eastern North Atlantic observatory. J. Geophys. Res. Atmos., 124, 47414760, https://doi.org/10.1029/2018JD029667.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grecu, M., L. Tian, W. S. Olsen, and S. Tanelli, 2011: A robust dual-frequency radar profiling algorithm. J. Appl. Meteor. Climatol., 50, 15431557, https://doi.org/10.1175/2011JAMC2655.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayden, L., and C. Liu, 2018: A multiyear analysis of global precipitation combining CloudSat and GPM precipitation retrievals. J. Hydrometeor., 19, 19351952, https://doi.org/10.1175/JHM-D-18-0053.1.

    • Search Google Scholar
    • Export Citation
  • Haynes, J. M., R. T. Marchand, Z. Lou, A. Bodas-Salcedo, and G. L. Stephens, 2007: A multipurpose radar simulation package: QuickBeam. Bull. Amer. Meteor. Soc., 88, 17231728, https://doi.org/10.1175/BAMS-88-11-1723.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haynes, J. M., T. S. L’Ecuyer, G. L. Stephens, S. D. Miller, C. Mitrescu, N. B. Wood, and S. Tanelli, 2009: Rainfall retrieval over the ocean with spaceborne W-band radar. J. Geophys. Res., 114, D00A22, https://doi.org/10.1029/2008JD009973.

    • 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
  • Kalmus, P., and M. Lebsock, 2017: Correcting biased evaporation in CloudSat warm rain. IEEE Trans. Geosci. Remote Sens., 55, 62076217, https://doi.org/10.1109/TGRS.2017.2722469.

    • Search Google Scholar
    • Export Citation
  • Kazumori, M., and S. J. English, 2015: Use of the ocean surface wind direction signal in microwave radiance assimilation. Quart. J. Roy. Meteor. Soc., 141, 13541375, https://doi.org/10.1002/qj.2445.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., and P. Joe, 2007: Importance, identification and measurement of light precipitation at mid- to high-latitudes. Joint EUMETSAT Meteorological Satellite Conf. and 15th Satellite Meteorology and Oceanography Conf., Amsterdam, Netherlands, EUMETSAT–Amer. Meteor. Soc., https://www.eumetsat.int/media/5487.

    • Crossref
    • Export Citation
  • Kidd, C., E. Graham, T. Smyth, and M. Gill, 2021: Assessing the impact of light/shallow precipitation retrievals from satellite-based observations using surface radar and Micro Rain Radar observations. Remote Sens., 13, 1708, https://doi.org/10.3390/rs13091708.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klepp, C., and Coauthors, 2018: OceanRAIN, a new in-situ shipboard global ocean surface-reference dataset of all water cycle components. Sci. Data, 5, 180122, https://doi.org/10.1038/sdata.2018.122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., and Coauthors, 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor. Climatol., 39, 19651982, https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., D. Randel, M. Kulie, N.-Y. Wang, R. Ferraro, S. J. 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
  • Lebsock, M., 2018: Level 2C RAIN-PROFILE product process description and interface control document. CloudSat Data Processing Center Doc., 14 pp., https://www.cloudsat.cira.colostate.edu/cloudsat-static/info/dl/2c-rain-profile/2C-RAIN-PROFILE-PDICD.P_R04.20110620.pdf.

    • Crossref
    • Export Citation
  • Lebsock, M., and T. S. L’Ecuyer, 2011: The retrieval of warm rain from CloudSat. J. Geophys. Res., 116, D20209, https://doi.org/10.1029/2011JD016076.

    • Search Google Scholar
    • Export Citation
  • Liao, L., R. Meneghini, T. Iguchi, and A. Tokay, 2020: Characteristics of DSD bulk parameters: Implication for radar rain retrieval. Atmosphere, 11, 670, https://doi.org/10.3390/atmos11060670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, X., and A. Y. Hou, 2012: Estimation of rain intensity spectra over the continental United States using ground radar–gauge measurements. J. Climate, 25, 19011915, https://doi.org/10.1175/JCLI-D-11-00151.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masaki, T., T. Iguchi, K. Kanemaru, K. Furukawa, N. Yoshida, T. Kaubota, and R. Oki, 2021: Calibration of the dual-frequency precipitation radar onboard the Global Precipitation Measurement Core Observatory. IEEE Trans. Geosci. Remote Sens., 60, 5100116, https://doi.org/10.1109/TGRS.2020.3039978.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., and Coauthors, 2021: Observations of clouds, aerosols, precipitation, and surface radiation over the Southern Ocean: An overview of CAPRICORN, MARCUS, MICRE, and SOCRATES. Bull. Amer. Meteor. Soc., 102, E894E928, https://doi.org/10.1175/BAMS-D-20-0132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mie, G., 1908: Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen. Ann. Phys., 330, 377445, https://doi.org/10.1002/andp.19083300302.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nuijens, L., K. Emanuel, H. Masunaga, and T. L’Ecuyer, 2017: Implications of warm rain in shallow cumulus and congestus clouds for large-scale circulations. Surv. Geophys., 38, 12571282, https://doi.org/10.1007/s10712-017-9429-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Porcacchia, L., P.-E. Kirstetter, V. Maggioni, and S. Tanelli, 2019: Investigating the GPM Dual-Frequency Precipitation Radar signatures of low-level precipitation enhancement. Quart. J. Roy. Meteor. Soc., 145, 31613174, https://doi.org/10.1002/qj.3611.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Protat, A., C. Klepp, V. Louf, W. A. Peterson, S. P. Alexander, A. Barros, J. Leinonen, and G. G. Mace, 2019a: The latitudinal variability of oceanic rainfall properties and its implication for satellite retrievals: 1. Drop size distribution properties. J. Geophys. Res. Atmos., 124, 13 29113 311, https://doi.org/10.1029/2019JD031010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Protat, A., C. Klepp, V. Louf, W. A. Peterson, S. P. Alexander, A. Barros, J. Leinonen, and G. G. Mace, 2019b: The latitudinal variability of oceanic rainfall properties and its implication for satellite retrievals: 2. The relationships between radar observables and drop size distribution parameters. J. Geophys. Res. Atmos., 124, 13 31213 324, https://doi.org/10.1029/2019JD031011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rapp, A. D., M. Lebsock, and T. L’Ecuyer, 2013: Low cloud precipitation climatology in the southeastern Pacific marine stratocumulus region using CloudSat. Environ. Res. Lett., 8, 014027, https://doi.org/10.1088/1748-9326/8/1/014027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Redemann, J., and Coauthors, 2021: An overview of the ORACLES (Observations of Aerosols above Clouds and Their Interactions) project: Aerosol–cloud–radiation interactions in the southeast Atlantic basin. Atmos. Chem. Phys., 21, 15071563, https://doi.org/10.5194/acp-21-1507-2021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rodgers, C. D., 2000: Inverse Methods for Atmospheric Sounding: Theory and Practice. Vol. 2. World Scientific, 256 pp.

    • Crossref
    • Export Citation
  • Schulte, R. M., C. D. Kummerow, C. Klepp, and G. G. Mace, 2022: How accurately can warm rain realistically be retrieved with satellite sensors? Part I: DSD uncertainties. J. Appl. Meteor. Climatol., 61, 10871105, https://doi.org/10.1175/JAMC-D-21-0158.1.

    • Search Google Scholar
    • Export Citation
  • Seto, S., T. Iguchi, R. Meneghini, J. Awaka, T. Kubota, T. Masaki, and N. Takahasi, 2021: The precipitation rate retrieval algorithms for the GPM Dual-Frequency Precipitation Radar. J. Meteor. Soc. Japan, 99, 205237, https://doi.org/10.2151/jmsj.2021-011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sinclair, K., B. van Diedenhoven, B. Cairns, M. Alexandrov, A. M. Dzambo, and T. L’Ecuyer, 2021: Inference of precipitation in warm stratiform clouds using remotely sensed observations of cloud top droplet size distribution. Geophys. Res. Lett., 48, e2021GL092547, https://doi.org/10.1029/2021GL092547.

    • Crossref
    • Export Citation
  • Skofronick-Jackson, G., and Coauthors, 2017: The Global Precipitation Measurement (GPM) mission for science and society. Bull. Amer. Meteor. Soc., 98, 16791695, https://doi.org/10.1175/BAMS-D-15-00306.1.

    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., D. Winker, J. Pelon, C. Trepte, D. Vane, C. Yuhas, T. L’Ecuyer, and M. Lebsock, 2018: CloudSat and CALIPSO within the A-train: Ten years of actively observing the Earth system. Bull. Amer. Meteor. Soc., 99, 569581, https://doi.org/10.1175/BAMS-D-16-0324.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stubenrauch, C. J., and Coauthors, 2013: Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX Radiation Panel. Bull. Amer. Meteor. Soc., 94, 10311049, https://doi.org/10.1175/BAMS-D-12-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanelli, S., S. L. Durden, E. Im, K. S. Pak, D. G. Reinke, P. Partain, J. M. Haynes, and R. T. Marchand, 2008: CloudSat’s Cloud Profiling Radar after two years in orbit: Performance, calibration, and processing. IEEE Trans. Geosci. Remote Sens., 46, 35603573, https://doi.org/10.1109/TGRS.2008.2002030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Testud, J., S. Oury, R. A. Black, P. Amayenc, and X. Dou, 2001: The concept of “normalized” distribution to describe raindrop spectra: A tool for cloud physics and cloud remote sensing. J. Appl. Meteor., 40, 11181140, https://doi.org/10.1175/1520-0450(2001)040<1118:TCONDT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Turk, F. J., and Coauthors, 2021: Applications of a CloudSat-TRMM and CloudSat-GPM satellite coincidence dataset. Remote Sens., 13, 2264, https://doi.org/10.3390/rs13122264.

    • Search Google Scholar
    • Export Citation
  • Witte, M. K., T. Yuan, P. Y. Chuang, S. Platnick, K. G. Meyer, G. Wind, and H. H. Jonsson, 2018: MODIS retrievals of cloud effective radius in marine stratocumulus exhibit no significant bias. Geophys. Res. Lett., 45, 10 65610 664, https://doi.org/10.1029/2018GL079325.

    • Search Google Scholar
    • Export Citation
  • Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 25392558, https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 373 373 31
Full Text Views 98 98 8
PDF Downloads 86 86 9

Can DSD Assumptions Explain the Differences in Satellite Estimates of Warm Rain?

Richard M. SchulteaDepartment of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Search for other papers by Richard M. Schulte in
Current site
Google Scholar
PubMed
Close
and
Christian D. KummerowaDepartment of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Search for other papers by Christian D. Kummerow in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Satellite-based oceanic precipitation estimates, particularly those derived from the Global Precipitation Measurement (GPM) satellite and CloudSat, suffer from significant disagreement over regions of the globe where warm rain processes are dominant. GPM estimates of average rain rate tend to be lower than CloudSat estimates, due in part to GPM being less sensitive to shallow and/or light precipitation. Using coincident observations between GPM and CloudSat, we find that the GPM_2BCMB product misses about two-thirds of total accumulated warm rain compared to the CloudSat 2C-RAIN-PROFILE product. This difference becomes much smaller when products are compared at 1000 m above the surface (mitigating surface clutter issues) and when forcing the frequency of rain from CloudSat to match the frequency from GPM (mitigating sensitivity issues). However, even then a gap of about 25% remains. Using an optimal estimation retrieval algorithm on the underlying data, we retrieve a similar result, but find that the remaining difference between the GPM and CloudSat retrieved rain rates can be almost entirely accounted for by inconsistent assumptions about the shape of the drop size distribution (DSD) that are made in the two retrievals. We conclude that DSD assumptions contribute significantly to the relative underestimation of warm rain by GPM compared to CloudSat. Because the choice of DSD model has such a large effect on retrieved rain rates, more work is needed to determine whether the DSD models assumed by either the GPM_2BCMB or 2C-RAIN-PROFILE algorithms are actually appropriate for warm rain.

© 2022 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: Richard Schulte, rick.schulte@colostate.edu

Abstract

Satellite-based oceanic precipitation estimates, particularly those derived from the Global Precipitation Measurement (GPM) satellite and CloudSat, suffer from significant disagreement over regions of the globe where warm rain processes are dominant. GPM estimates of average rain rate tend to be lower than CloudSat estimates, due in part to GPM being less sensitive to shallow and/or light precipitation. Using coincident observations between GPM and CloudSat, we find that the GPM_2BCMB product misses about two-thirds of total accumulated warm rain compared to the CloudSat 2C-RAIN-PROFILE product. This difference becomes much smaller when products are compared at 1000 m above the surface (mitigating surface clutter issues) and when forcing the frequency of rain from CloudSat to match the frequency from GPM (mitigating sensitivity issues). However, even then a gap of about 25% remains. Using an optimal estimation retrieval algorithm on the underlying data, we retrieve a similar result, but find that the remaining difference between the GPM and CloudSat retrieved rain rates can be almost entirely accounted for by inconsistent assumptions about the shape of the drop size distribution (DSD) that are made in the two retrievals. We conclude that DSD assumptions contribute significantly to the relative underestimation of warm rain by GPM compared to CloudSat. Because the choice of DSD model has such a large effect on retrieved rain rates, more work is needed to determine whether the DSD models assumed by either the GPM_2BCMB or 2C-RAIN-PROFILE algorithms are actually appropriate for warm rain.

© 2022 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: Richard Schulte, rick.schulte@colostate.edu
Save