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    Evaluation of MRMS snowfall products over the western United States

    Source: Journal of Hydrometeorology:;2017:;Volume( 018 ):;issue: 006::page 1707
    Author:
    Wen, Yixin
    ,
    Kirstetter, Pierre
    ,
    Gourley, J. J.
    ,
    Hong, Yang
    ,
    Behrangi, Ali
    ,
    Flamig, Zachary
    DOI: 10.1175/JHM-D-16-0266.1
    Publisher: American Meteorological Society
    Abstract: now is important to water resources and is of critical importance to our society. Ground weather radar-based snowfall observations have been highly desirable for large-scale weather monitoring and water resources applications. This study conducts an evaluation of the Multi-Radar Multi-Sensor (MRMS) quantitative estimates of snow rate using the Snow Telemetry (SNOTEL) daily snow water equivalent (SWE) datasets. A detectability evaluation shows that MRMS is limited in detecting very light snow (daily snow accumulation < 5 mm) because of the quality control module in MRMS filtering out weak signals (<5 dBZ). For daily snow accumulation greater than 10 mm, MRMS has good detectability. The quantitative comparisons reveal a bias of -77.37 % between MRMS and SNOTEL. A majority of the underestimation bias occurs in relatively warm conditions with surface temperatures in the range of -10 to 0°C. A constant reflectivity-to-SWE intensity relationship does not capture the snow mass flux increase associated with denser snow particles at these relatively warm temperatures. There is no clear dependence of the bias on radar beam height. The findings in this study indicate that further improvement in radar snowfall products might occur by deriving appropriate reflectivity-to-SWE relationships considering the degree of riming and snowflake size.
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      Evaluation of MRMS snowfall products over the western United States

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4225619
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    • Journal of Hydrometeorology

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    contributor authorWen, Yixin
    contributor authorKirstetter, Pierre
    contributor authorGourley, J. J.
    contributor authorHong, Yang
    contributor authorBehrangi, Ali
    contributor authorFlamig, Zachary
    date accessioned2017-06-09T17:17:28Z
    date available2017-06-09T17:17:28Z
    date issued2017
    identifier issn1525-755X
    identifier otherams-82499.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4225619
    description abstractnow is important to water resources and is of critical importance to our society. Ground weather radar-based snowfall observations have been highly desirable for large-scale weather monitoring and water resources applications. This study conducts an evaluation of the Multi-Radar Multi-Sensor (MRMS) quantitative estimates of snow rate using the Snow Telemetry (SNOTEL) daily snow water equivalent (SWE) datasets. A detectability evaluation shows that MRMS is limited in detecting very light snow (daily snow accumulation < 5 mm) because of the quality control module in MRMS filtering out weak signals (<5 dBZ). For daily snow accumulation greater than 10 mm, MRMS has good detectability. The quantitative comparisons reveal a bias of -77.37 % between MRMS and SNOTEL. A majority of the underestimation bias occurs in relatively warm conditions with surface temperatures in the range of -10 to 0°C. A constant reflectivity-to-SWE intensity relationship does not capture the snow mass flux increase associated with denser snow particles at these relatively warm temperatures. There is no clear dependence of the bias on radar beam height. The findings in this study indicate that further improvement in radar snowfall products might occur by deriving appropriate reflectivity-to-SWE relationships considering the degree of riming and snowflake size.
    publisherAmerican Meteorological Society
    titleEvaluation of MRMS snowfall products over the western United States
    typeJournal Paper
    journal volume018
    journal issue006
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM-D-16-0266.1
    journal fristpage1707
    journal lastpage1713
    treeJournal of Hydrometeorology:;2017:;Volume( 018 ):;issue: 006
    contenttypeFulltext
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