• Adler, R. F., and et al. , 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, doi:10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ahlgrimm, M., , and R. Forbes, 2014: Improving the representation of low clouds and drizzle in the ECMWF model based on ARM observations from the Azores. Mon. Wea. Rev., 142, 668685, doi:10.1175/MWR-D-13-00153.1.

    • Search Google Scholar
    • Export Citation
  • Andersson, A., , S. Bakan, , and H. Graßl, 2010: Satellite derived precipitation and freshwater flux variability and its dependence on the North Atlantic oscillation. Tellus, 62A, 453468, doi:10.1111/j.1600-0870.2010.00458.x.

    • 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, doi:10.1175/2010JAMC2341.1.

    • 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, doi:10.1029/2012JD017979.

    • 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, doi:10.1175/JCLI-D-13-00679.1.

    • Search Google Scholar
    • Export Citation
  • Brueck, M., , L. Nuijens, , and B. Stevens, 2015: On the seasonal and synoptic time-scale variability of the North Atlantic trade wind region and its low-level clouds. J. Atmos. Sci., doi:10.1175/JAS-D-14-0054.1, in press.

    • Search Google Scholar
    • Export Citation
  • Bumke, K., , and J. Seltmann, 2012: Analysis of measured drop size spectra over land and sea. ISRN Meteor., 2012, 110, doi:10.5402/2012/296575.

    • Search Google Scholar
    • Export Citation
  • Clemens, M., , G. Peters, , J. Seltmann, , and P. Winkler, 2006: Time-height evolution of measured raindrop size distributions. Proc. Fourth European Conf. on Radar in Meteorology and Hydrology, Barcelona, Spain, ERAD, 137140. [Available online at http://www.crahi.upc.edu/ERAD2006/proceedingsMask/00037.pdf.]

  • Doneaud, A., , S. Ionescu-Niscov, , D. L. Priegnitz, , and P. L. Smith, 1984: The area–time integral as an indicator for convective rain volumes. J. Climate Appl. Meteor., 23, 555561, doi:10.1175/1520-0450(1984)023<0555:TATIAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ellis, T. D., , T. L’Ecuyer, , J. M. Haynes, , and G. L. Stephens, 2009: How often does it rain over the global oceans? The perspective from CloudSat. Geophys. Res. Lett., 36, L03815, doi:10.1029/2008GL036728.

    • Search Google Scholar
    • Export Citation
  • Fennig, K., , A. Andersson, , S. Bakan, , C.-P. Klepp, , and M. Schröder, cited 2012: Hamburg ocean atmosphere parameters and fluxes from satellite data—HOAPS 3.2—monthly means/6-hourly composites. Satellite Application Facility on Climate Monitoring (CM SAF), doi:10.5676/EUM_SAF_CM/HOAPS/V001.

  • Field, P. R., , and G. J. Shutts, 2009: Properties of normalised rain-rate distributions in the tropical Pacific. Quart. J. Roy. Meteor. Soc., 135, 175186, doi:10.1002/qj.365.

    • Search Google Scholar
    • Export Citation
  • Hirota, N., , Y. N. Takayabu, , M. Watanabe, , and M. Kimoto, 2011: Precipitation reproducibility over tropical oceans and its relationship to the double ITCZ problem in CMIP3 and MIROC5 climate models. J. Climate, 24, 48594873, doi:10.1175/2011JCLI4156.1.

    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and et al. , 2014: The Global Precipitation Measurement (GPM) mission. Bull. Amer. Meteor. Soc., 95, 701–722, doi:10.1175/BAMS-D-13-00164.1.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., , S. Brodzik, , C. Schumacher, , S. E. Yuter, , and C. R. Williams, 2004: Uncertainties in oceanic radar rain maps at Kwajalein and implications for satellite validation. J. Appl. Meteor., 43, 11141132, doi:10.1175/1520-0450(2004)043<1114:UIORRM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., , and D. T. Bolvin, cited 2013: Version 1.2 GPCP one-degree daily precipitation data set documentation. NASA Goddard Space Flight Center, 27 pp. [Available online at ftp://rsd.gsfc.nasa.gov/pub/1dd-v1.2/1DD_v1.2_doc.pdf.]

  • Huffman, G. J., , and D. T. Bolvin, cited 2014: TRMM and other data precipitation data set documentation. NASA Goddard Space Flight Center, 42 pp. [Available online at ftp://precip.gsfc.nasa.gov/pub/trmmdocs/3B42_3B43_doc.pdf.]

  • Huffman, G. J., , R. F. Adler, , M. M. Morrissey, , D. T. Bolvin, , S. Curtis, , R. Joyce, , B. McGavock, , and J. Susskind, 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 3650, doi:10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and et al. , 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 3855, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation
  • Iguchi, T., , T. Kozu, , R. Meneghini, , J. Awaka, , and K. Okamoto, 2000: Rain-profiling algorithm for the TRMM Precipitation Radar. J. Appl. Meteor., 39, 20382052, doi:10.1175/1520-0450(2001)040<2038:RPAFTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Keeler, R., , J. Lutz, , and J. Vivekanandan, 2000: S-pol: NCAR’s polarimetric Doppler research radar. Proc. IEEE Geoscience and Remote Sensing Symp. 2000, Honolulu, HI, IEEE, 1570–1573, doi:10.1109/IGARSS.2000.857275.

  • Kidd, C., , and G. Huffman, 2011: Global precipitation measurement. Meteor. Appl., 18, 334353, doi:10.1002/met.284.

  • Klepp, C.-P., , S. Bakan, , and H. Graßl, 2003: Improvements of satellite-derived cyclonic rainfall over the North Atlantic. J. Climate, 16, 657669, doi:10.1175/1520-0442(2003)016<0657:IOSDCR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Klepp, C.-P., , K. Bumke, , S. Bakan, , and P. Bauer, 2010: Ground validation of oceanic snowfall detection in satellite climatologies during LOFZY. Tellus, 62A, 469480, doi:10.1111/j.1600-0870.2010.00459.x.

    • Search Google Scholar
    • Export Citation
  • Knight, C. A., , and L. J. Miller, 1998: Early radar echoes from small, warm cumulus: Bragg and hydrometeor scattering. J. Atmos. Sci., 55, 29742992, doi:10.1175/1520-0469(1998)055<2974:EREFSW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kozu, T., and et al. , 2001: Development of precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. IEEE Trans. Geosci. Remote Sens., 39, 102116, doi:10.1109/36.898669.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., and et al. , 2001: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 18011820, doi:10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C. D., , S. Ringerud, , J. Crook, , D. Randel, , and W. Berg, 2011: An observationally generated a priori database for microwave rainfall retrievals. J. Atmos. Oceanic Technol., 28, 113130, doi:10.1175/2010JTECHA1468.1.

    • Search Google Scholar
    • Export Citation
  • Lau, K. M., , and H. T. Wu, 2003: Warm rain processes over tropical oceans and climate implications. Geophys. Res. Lett., 30, 2290, doi:10.1029/2003GL018567.

    • Search Google Scholar
    • Export Citation
  • Levizzani, V., , P. Bauer, , and F. J. Turk, 2007: Measuring Precipitation from Space: EURAINSAT and the Future. Advances in Global Change Research, Vol. 28, Springer, 722 pp.

  • Liu, C., , and R. P. Allan, 2012: Multisatellite observed responses of precipitation and its extremes to interannual climate variability. J. Geophys. Res., 117, D03101, doi:10.1029/2011JD016568.

    • Search Google Scholar
    • Export Citation
  • Lonitz, K., 2014: Susceptibility of trade wind cumulus clouds to precipitation. PhD thesis, Department of Geosciences, University of Hamburg, 117 pp.

  • METEK, 2009: MRR physical basics version 5.2.0.1. Meteorologische Messtechnik GmbH, 20 pp. [Available online at http://www.mpimet.mpg.de/fileadmin/atmosphaere/barbados/Instrumentation/MRR-physical-basics_20090707.pdf.]

  • Mitrescu, C., , T. L. Ecuyer, , J. Haynes, , S. Miller, , and J. Turk, 2010: CloudSat precipitation profiling algorithm—Model description. J. Appl. Meteor. Climatol., 49, 9911003, doi:10.1175/2009JAMC2181.1.

    • Search Google Scholar
    • Export Citation
  • Nuijens, L., 2005: Estimating precipitation from radar observations in the trade-wind cumulus region. Master’s thesis, Meteorology and Air Quality Section, Wageningen University, 46 pp.

  • Nuijens, L., , B. Stevens, , and A. P. Siebesma, 2009: The environment of precipitating shallow cumulus convection. J. Atmos. Sci., 66, 19621979, doi:10.1175/2008JAS2841.1.

    • Search Google Scholar
    • Export Citation
  • Nuijens, L., , I. Serikov, , L. Hirsch, , K. Lonitz, , and B. Stevens, 2014: The distribution and variability of low-level cloud in the North Atlantic trades: Distribution and variability of low-level cloud in the trades. Quart. J. Roy. Meteor. Soc., 140, 2364–2374, doi:10.1002/qj.2307.

    • Search Google Scholar
    • Export Citation
  • Olson, W. S., , C. D. Kummerow, , Y. Hong, , and W.-K. Tao, 1999: Atmospheric latent heating distributions in the tropics derived from satellite passive microwave radiometer measurements. J. Appl. Meteor., 38, 633664, doi:10.1175/1520-0450(1999)038<0633:ALHDIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rauber, R. M., and et al. , 2007: Rain in shallow cumulus over the ocean: The RICO campaign. Bull. Amer. Meteor. Soc., 88, 19121928, doi:10.1175/BAMS-88-12-1912.

    • Search Google Scholar
    • Export Citation
  • Sandu, I., , B. Stevens, , and R. Pincus, 2010: On the transitions in marine boundary layer cloudiness. Atmos. Chem. Phys., 10, 23772391, doi:10.5194/acp-10-2377-2010.

    • Search Google Scholar
    • Export Citation
  • Short, D. A., , and K. Nakamura, 2000: TRMM radar observations of shallow precipitation over the tropical oceans. J. Climate, 13, 41074124, doi:10.1175/1520-0442(2000)013<4107:TROOSP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Siongco, A. C., , C. Hohenegger, , and B. Stevens, 2015: The Atlantic ITCZ bias in CMIP5 models. Climate Dyn., doi:10.1007/s00382-014-2366-3, in press.

    • Search Google Scholar
    • Export Citation
  • Snodgrass, E. R., , L. Di Girolamo, , and R. M. Rauber, 2009: Precipitation characteristics of trade wind clouds during RICO derived from radar, satellite, and aircraft measurements. J. Appl. Meteor. Climatol., 48, 464483, doi:10.1175/2008JAMC1946.1.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., , and C. D. Kummerow, 2007: The remote sensing of clouds and precipitation from space: A review. J. Atmos. Sci., 64, 37423765, doi:10.1175/2006JAS2375.1.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and et al. , 2010: Dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and et al. , 2012: An update on Earth’s energy balance in light of the latest global observations. Nat. Geosci., 5, 691696, doi:10.1038/ngeo1580.

    • Search Google Scholar
    • Export Citation
  • Suzuki, K., , G. L. Stephens, , S. C. van den Heever, , and T. Y. Nakajima, 2011: Diagnosis of the warm rain process in cloud-resolving models using joint CloudSat and MODIS observations. J. Atmos. Sci., 68, 26552670, doi:10.1175/JAS-D-10-05026.1.

    • Search Google Scholar
    • Export Citation
  • Vila, D., , C. Hernandez, , R. Ferraro, , and H. Semunegus, 2013: The performance of hydrological monthly products using SSM/I–SSMI/S sensors. J. Hydrometeor., 14, 266274, doi:10.1175/JHM-D-12-056.1.

    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., , C. A. Knight, , S. A. Tessendorf, , and C. Weeks, 2011: Polarimetric radar analysis of raindrop size variability in maritime and continental clouds. J. Appl. Meteor. Climatol., 50, 19701980, doi:10.1175/2011JAMC2683.1.

    • Search Google Scholar
    • Export Citation
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Evaluating Light Rain from Satellite- and Ground-Based Remote Sensing Data over the Subtropical North Atlantic

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  • 1 Max Planck Institute for Meteorology, Hamburg, Germany
  • | 2 Climate System Analysis and Prediction/Center for Earth System Research and Sustainability, University of Hamburg, Hamburg, Germany
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Abstract

Three state-of-the-art satellite climatologies are analyzed for their ability to observe light rain from predominantly shallow, warm clouds over the subtropical North Atlantic Ocean trade winds (1998–2005). HOAPS composite (HOAPS-C), version 3.2; TMPA, version 7; and GPCP 1 Degree Daily (1DD), version 1.2, are compared with ground-based S-Pol radar data from the Rain in Cumulus over the Ocean (RICO; winter 2004/05) campaign and Micro Rain Radar data from the Barbados Cloud Observatory (2010–12). Winter rainfall amounts to one-third of annual rainfall, whereby light rain from warm clouds dominates. Daily rain occurrence and rain intensity during RICO largely differ among the satellite climatologies. TMPA best captures the frequent light rain events, only missing 7% of days on which the S-Pol radar detects rain, whereas HOAPS-C misses 33% and GPCP 1DD misses 56%. Algorithm constraints mainly cause these differences. In HOAPS-C also few available passive microwave (PMW) sensor overpasses limit its performance. TMPA outperforms HOAPS-C when only comparing nonmissing time steps, yet HOAPS-C can detect rain for S-Pol rain-covered areas down to 2%. In GPCP 1DD’s algorithm, the underestimated rain occurrence derived from PMW scanners is linked to the overestimated rain intensity, being constrained by the GPCP monthly satellite–gauge combination, whereby IR sensors determine the timing. Algorithm improvements in version 1.2 increased the rain occurrence by 50% relative to version 1.1. In version 7 of TMPA, algorithm corrections in PMW sounder data largely improved the rain detection relative to version 6. TMPA best represents light rain in the North Atlantic trades, followed by HOAPS-C and GPCP 1DD.

Corresponding author address: Jörg Burdanowitz, Max Planck Institute for Meteorology, Bundesstraße 53, Hamburg, Germany. E-mail: joerg.burdanowitz@mpimet.mpg.de

Abstract

Three state-of-the-art satellite climatologies are analyzed for their ability to observe light rain from predominantly shallow, warm clouds over the subtropical North Atlantic Ocean trade winds (1998–2005). HOAPS composite (HOAPS-C), version 3.2; TMPA, version 7; and GPCP 1 Degree Daily (1DD), version 1.2, are compared with ground-based S-Pol radar data from the Rain in Cumulus over the Ocean (RICO; winter 2004/05) campaign and Micro Rain Radar data from the Barbados Cloud Observatory (2010–12). Winter rainfall amounts to one-third of annual rainfall, whereby light rain from warm clouds dominates. Daily rain occurrence and rain intensity during RICO largely differ among the satellite climatologies. TMPA best captures the frequent light rain events, only missing 7% of days on which the S-Pol radar detects rain, whereas HOAPS-C misses 33% and GPCP 1DD misses 56%. Algorithm constraints mainly cause these differences. In HOAPS-C also few available passive microwave (PMW) sensor overpasses limit its performance. TMPA outperforms HOAPS-C when only comparing nonmissing time steps, yet HOAPS-C can detect rain for S-Pol rain-covered areas down to 2%. In GPCP 1DD’s algorithm, the underestimated rain occurrence derived from PMW scanners is linked to the overestimated rain intensity, being constrained by the GPCP monthly satellite–gauge combination, whereby IR sensors determine the timing. Algorithm improvements in version 1.2 increased the rain occurrence by 50% relative to version 1.1. In version 7 of TMPA, algorithm corrections in PMW sounder data largely improved the rain detection relative to version 6. TMPA best represents light rain in the North Atlantic trades, followed by HOAPS-C and GPCP 1DD.

Corresponding author address: Jörg Burdanowitz, Max Planck Institute for Meteorology, Bundesstraße 53, Hamburg, Germany. E-mail: joerg.burdanowitz@mpimet.mpg.de
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