ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Spatial distributions of oceanic non-precipitating warm clouds and in-cloud vertical structures of liquid water content as revealed by CloudSat measurements

  • School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.05.002
  • Received Date: 29 December 2018
  • Accepted Date: 10 May 2019
  • Rev Recd Date: 10 May 2019
  • Publish Date: 31 May 2020
    Abstract

  • Abstract

    Using cloud profile radar (CPR/CloudSat) data from 2007 to 2009, the horizontal distributions of non-precipitating warm clouds over global oceans were examined and the vertical variation characteristics of LWC (liquid water content) were analyzed for four major warm cloud types, i.e., cumulus (Cu), stratus (St), stratocumulus (Sc) and altocumulus (Ac). It was found that among all oceanic non-precipitating warm clouds, the proportion of each type is stratocumulus (76.46%), stratus (12.48%), cumulus (7.45%) and altocumulus (3.61%). Stratocumulus plays a dominant role in the total coverage area of non-precipitating warm clouds over oceans. After the global normalization of the sample volume, there are also large differences in the spatial distribution patterns among the four types. Stratocumulus and stratus are mainly concentrated in coastal waters near the west of North and South American continents, while cumulus and altocumulus clouds are widely distributed on the Pacific Ocean, Atlantic Ocean and Indian Ocean, and high occurrences generally appear in the central part of each ocean. In spite of distinct formation regimes and morphologies, the vertical structures of LWC show similar patterns among the four types. From the cloud bottom up to cloud top, LWC was found to increase first and then decrease. The approximately linearly increasing structure in the lower and middle part of the cloud column reflects the quasi-adiabatic growth characteristics of LWC. The upward decreasing structure near the upper part and near cloud top clearly reflects that cloud top is generally strongly affected by the intrusion of overhead dry air. The resulting evaporation of cloud water attenuates heavily downward from the cloud top. The LWC vertical structure was found to be affected by cloud top height and cloud thickness. As cloud thickness increases, the upward increasing part becomes thicker, while the upward decreasing part becomes thinner. Clouds with the same thickness but different cloud top heights also have different LWC structures. This indicates that for a particular type of the clouds, there are differences in the LWC structures corresponding to different periods during the cloud’s generation and development process.

    Abstract

    Using cloud profile radar (CPR/CloudSat) data from 2007 to 2009, the horizontal distributions of non-precipitating warm clouds over global oceans were examined and the vertical variation characteristics of LWC (liquid water content) were analyzed for four major warm cloud types, i.e., cumulus (Cu), stratus (St), stratocumulus (Sc) and altocumulus (Ac). It was found that among all oceanic non-precipitating warm clouds, the proportion of each type is stratocumulus (76.46%), stratus (12.48%), cumulus (7.45%) and altocumulus (3.61%). Stratocumulus plays a dominant role in the total coverage area of non-precipitating warm clouds over oceans. After the global normalization of the sample volume, there are also large differences in the spatial distribution patterns among the four types. Stratocumulus and stratus are mainly concentrated in coastal waters near the west of North and South American continents, while cumulus and altocumulus clouds are widely distributed on the Pacific Ocean, Atlantic Ocean and Indian Ocean, and high occurrences generally appear in the central part of each ocean. In spite of distinct formation regimes and morphologies, the vertical structures of LWC show similar patterns among the four types. From the cloud bottom up to cloud top, LWC was found to increase first and then decrease. The approximately linearly increasing structure in the lower and middle part of the cloud column reflects the quasi-adiabatic growth characteristics of LWC. The upward decreasing structure near the upper part and near cloud top clearly reflects that cloud top is generally strongly affected by the intrusion of overhead dry air. The resulting evaporation of cloud water attenuates heavily downward from the cloud top. The LWC vertical structure was found to be affected by cloud top height and cloud thickness. As cloud thickness increases, the upward increasing part becomes thicker, while the upward decreasing part becomes thinner. Clouds with the same thickness but different cloud top heights also have different LWC structures. This indicates that for a particular type of the clouds, there are differences in the LWC structures corresponding to different periods during the cloud’s generation and development process.
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    • References(27)
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    KATO S, ROSE F G, SUN-MACK S, et al. Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties[J]. Journal of Geophysical Research: Atmospheres, 2011, 116：D19209.
    [16]
    CHAN M A, COMISO J C. Arctic cloud characteristcs as derived from MODIS, CALIPSO, and CloudSat[J]. Journal of Climate, 2013, 26(10): 3285-3306.
    [17]
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    [20]
    安洁. 基于CloudSat资料的东海及周边云层垂直分布特征[J]. 海洋预报, 2018, 35(5):60-73.
    [21]
    CHAI Q M, WANG W C, HUANG Z W. Analyzing the cloud micro-and macro-physical properties of the cyclone eye wall and its surrounding spiral cloud bands based on CloudSat and TRMM data[J]. Journal of Tropical Meteorology, 2018, 24(2): 253-262.
    [22]
    尚博, 周毓荃, 刘建朝, 等. 基于Cloudsat的降水云和非降水云垂直特征[J]. 应用气象学报, 2012, 23(1)：1-9.
    [23]
    SUZUKI K, STEPHENS G L. Global identification of warm cloud microphysical processes with combined use of A-Train observations[J]. Geophysical Research Letters, 2008, 35(8)：L08805.
    [24]
    DE SZOEKE S P, VERLINDEN K L, COVERT D. Cloud-scale droplet number sensitivity to liquid water path in marine stratocumulus[J]. Journal of Geophysical Research: Atmospheres, 2018，123（10）：5320-5334.
    [25]
    STEPHENS G L, VANE D G, BOAIN R J, et al. The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation[J]. Bulletin of the American Meteorological Society, 2002, 83(12): 1771-1790.
    [26]
    MARCHAND R, MACE G G, ACKERMAN T, et al. Hydrometeor detection using Cloudsat-An earthorbiting 94-GHz cloud radar[J]. Journal of Atmospheric and Oceanic Technology, 2008, 25: 519-533.
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    WOOD N. Level 2B radar-visible optical depth cloud water content (2B-CWC-RVOD) process description document[J]. Version, 2008, 5: 1-26.)
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    [1]
    HUANG L, JIANG J H, WANG Z, et al. Climatology of cloud water content associated with different cloud types observed by A-Train satellites[J]. Journal of Geophysical Research: Atmospheres, 2015, 120(9): 4196-4212.
    [2]
    LIU D, LIU Q, QI L, et al. Oceanic single-layer warm clouds missed by the Cloud Profiling Radar as inferred from MODIS and CALIOP measurements[J]. Journal of Geophysical Research:Atmospheres，2016，121(21): 12947-12965.
    [3]
    BAKER M B. Cloud microphysics and climate[J]. Science, 276(5315): 1072-1078.
    [4]
    周毓荃, 欧建军. 利用探空数据分析云垂直结构的方法及其应用研究[J]. 气象, 2010, 35(11)：50-58.
    [5]
    ZHANG J, LI Z, CHEN H, et al. Cloud vertical distribution from radiosonde, remote sensing, and model simulations[J]. Climate Dynamics, 2014, 43:1129-1140.
    [6]
    ZENG S, RIEDI J, TREPTE C R, et al. Study of global cloud droplet number concentration with A-Train satellites[J]. Atmospheric Chemistry and Physics, 2014, 14(14): 7125-7134.
    [7]
    HU Y, VAUGHAN M, MCCLAIN C, et al. Global statistics of liquid water content and effective number concentration of water clouds over ocean derived from combined CALIPSO and MODIS measurements[J]. Atmospheric Chemistry and Physics, 2007, 7(12): 3353-3359.
    [8]
    周天, 黄忠伟, 黄建平, 等. 黄土高原地区云垂直结构的激光雷达遥感研究[J]. 干旱气象, 2013, 31(2): 246-253.
    [9]
    段皓, 刘煜. 近20年中国地区云量变化趋势[J]. 气象科技, 2011, 39(3): 280-288.
    [10]
    NAKAJIMA T Y, SUZUKI K, STEPHENS G L. Droplet growth in warm water clouds observed by the A-Train. Part I: Sensitivity analysis of the MODIS-derived cloud droplet sizes[J]. Journal of the Atmospheric Sciences, 2010, 67(6): 1884-1896.
    [11]
    谢磊, 刘奇. 基于卫星遥感的全球洋面降水暖云与非降水暖云的云参数差异[J]. 中国科学技术大学学报, 2017, 47（12）：1006-1014.
    [12]
    COSTANTINO L, BRéON F M. Aerosol indirect effect on warm clouds over South-East Atlantic, from co-located MODIS and CALIPSO observations[J]. Atmospheric Chemistry and Physics, 2013, 13(1): 69-88.
    [13]
    WEISZ E, LI J, MENZEL W P, et al. Comparison of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals[J]. Geophysical Research Letters, 2007, 34：L17811.
    [14]
    IM E, WU C, DURDEN S L. Cloud profiling radar for the CloudSat mission[J]. IEEE Aerospace and Electronic Systems Magazine, 2005, 20(10): 15-18.
    [15]
    KATO S, ROSE F G, SUN-MACK S, et al. Improvements of top-of-atmosphere and surface irradiance computations with CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties[J]. Journal of Geophysical Research: Atmospheres, 2011, 116：D19209.
    [16]
    CHAN M A, COMISO J C. Arctic cloud characteristcs as derived from MODIS, CALIPSO, and CloudSat[J]. Journal of Climate, 2013, 26(10): 3285-3306.
    [17]
    NAKAJIMA T Y, SUZUKI K, STEPHENS G L. Droplet growth in warm water clouds observed by the A-Train. Part II: A multisensor view[J]. Journal of the Atmospheric Sciences, 2010, 67(6): 1897-1907.
    [18]
    MASSIE S T, DELANO J, BARDEEN C G, et al. Changes in the shape of cloud ice water content vertical structure due to aerosol variations[J]. Atmospheric Chemistry and Physics, 2016, 16(10): 6091-6105.
    [19]
    赵姝慧, 班显秀, 袁健, 等. 8，9月沈阳地区卫星观测云垂直结构的气候特征分析[J]. 高原气象, 2014, 33(6)：1640-1647.
    [20]
    安洁. 基于CloudSat资料的东海及周边云层垂直分布特征[J]. 海洋预报, 2018, 35(5):60-73.
    [21]
    CHAI Q M, WANG W C, HUANG Z W. Analyzing the cloud micro-and macro-physical properties of the cyclone eye wall and its surrounding spiral cloud bands based on CloudSat and TRMM data[J]. Journal of Tropical Meteorology, 2018, 24(2): 253-262.
    [22]
    尚博, 周毓荃, 刘建朝, 等. 基于Cloudsat的降水云和非降水云垂直特征[J]. 应用气象学报, 2012, 23(1)：1-9.
    [23]
    SUZUKI K, STEPHENS G L. Global identification of warm cloud microphysical processes with combined use of A-Train observations[J]. Geophysical Research Letters, 2008, 35(8)：L08805.
    [24]
    DE SZOEKE S P, VERLINDEN K L, COVERT D. Cloud-scale droplet number sensitivity to liquid water path in marine stratocumulus[J]. Journal of Geophysical Research: Atmospheres, 2018，123（10）：5320-5334.
    [25]
    STEPHENS G L, VANE D G, BOAIN R J, et al. The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation[J]. Bulletin of the American Meteorological Society, 2002, 83(12): 1771-1790.
    [26]
    MARCHAND R, MACE G G, ACKERMAN T, et al. Hydrometeor detection using Cloudsat-An earthorbiting 94-GHz cloud radar[J]. Journal of Atmospheric and Oceanic Technology, 2008, 25: 519-533.
    [27]
    WOOD N. Level 2B radar-visible optical depth cloud water content (2B-CWC-RVOD) process description document[J]. Version, 2008, 5: 1-26.)
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