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    Warm Rain Rates from AMSR-E 89-GHz Brightness Temperatures Trained Using CloudSat Rain-Rate Observations

    Source: Journal of Atmospheric and Oceanic Technology:;2019:;volume 036:;issue 006::page 1033
    Author:
    Eastman, Ryan
    ,
    Lebsock, Matthew
    ,
    Wood, Robert
    DOI: 10.1175/JTECH-D-18-0185.1
    Publisher: American Meteorological Society
    Abstract: AbstractCollocated CloudSat rain rates and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 89-GHz brightness temperature Tb retrievals allow for the development of an algorithm to estimate light, warm rain statistics as a function of AMSR-E 89-GHz Tb for shallow marine clouds. Four statistics are calculated from CloudSat rainfall rate estimates within each 4 km ? 6 km Tb pixel sampled by both sensors: the probability of rainfall, the mean rain rate, the mean rate when raining, and the maximum rain rate. Observations with overlying cold clouds are removed from the analysis. To account for confounding variables that modify Tb, curves are fit to the mean relationships between Tb and these four statistics within bins of constant column-integrated water vapor from AMSR-E, and sea surface temperature and wind speed from reanalysis grids. The coefficients that define these curves are then applied to all available AMSR-E Tb retrievals to estimate rain rate throughout the eastern subtropical oceans. A preliminary analysis shows strong agreement between AMSR-E rain rates and the CloudSat training dataset. Comparison with an existing microwave precipitation product shows that the new statistical product has an improved sensitivity to light rain. A climatology for the year 2007 shows that precipitation rates tend to be heavier where the sea surface is warmer and that rain is most frequent where stratocumulus transitions to trade cumulus in the subtropics.
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      Warm Rain Rates from AMSR-E 89-GHz Brightness Temperatures Trained Using CloudSat Rain-Rate Observations

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    contributor authorEastman, Ryan
    contributor authorLebsock, Matthew
    contributor authorWood, Robert
    date accessioned2019-10-05T06:46:41Z
    date available2019-10-05T06:46:41Z
    date copyright4/25/2019 12:00:00 AM
    date issued2019
    identifier otherJTECH-D-18-0185.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4263386
    description abstractAbstractCollocated CloudSat rain rates and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 89-GHz brightness temperature Tb retrievals allow for the development of an algorithm to estimate light, warm rain statistics as a function of AMSR-E 89-GHz Tb for shallow marine clouds. Four statistics are calculated from CloudSat rainfall rate estimates within each 4 km ? 6 km Tb pixel sampled by both sensors: the probability of rainfall, the mean rain rate, the mean rate when raining, and the maximum rain rate. Observations with overlying cold clouds are removed from the analysis. To account for confounding variables that modify Tb, curves are fit to the mean relationships between Tb and these four statistics within bins of constant column-integrated water vapor from AMSR-E, and sea surface temperature and wind speed from reanalysis grids. The coefficients that define these curves are then applied to all available AMSR-E Tb retrievals to estimate rain rate throughout the eastern subtropical oceans. A preliminary analysis shows strong agreement between AMSR-E rain rates and the CloudSat training dataset. Comparison with an existing microwave precipitation product shows that the new statistical product has an improved sensitivity to light rain. A climatology for the year 2007 shows that precipitation rates tend to be heavier where the sea surface is warmer and that rain is most frequent where stratocumulus transitions to trade cumulus in the subtropics.
    publisherAmerican Meteorological Society
    titleWarm Rain Rates from AMSR-E 89-GHz Brightness Temperatures Trained Using CloudSat Rain-Rate Observations
    typeJournal Paper
    journal volume36
    journal issue6
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH-D-18-0185.1
    journal fristpage1033
    journal lastpage1051
    treeJournal of Atmospheric and Oceanic Technology:;2019:;volume 036:;issue 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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