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    The Tornadic Supercell on the Kanto Plain on 6 May 2012: Polarimetric Radar and Surface Data Assimilation with EnKF and Ensemble-Based Sensitivity Analysis

    Source: Monthly Weather Review:;2016:;volume( 144 ):;issue: 009::page 3133
    Author:
    Yokota, Sho
    ,
    Seko, Hiromu
    ,
    Kunii, Masaru
    ,
    Yamauchi, Hiroshi
    ,
    Niino, Hiroshi
    DOI: 10.1175/MWR-D-15-0365.1
    Publisher: American Meteorological Society
    Abstract: tornadic supercell and associated low-level mesocyclone (LMC) observed on the Kanto Plain, Japan, on 6 May 2012 were predicted with a nonhydrostatic mesoscale model with a horizontal resolution of 350 m through assimilation of surface meteorological data (horizontal wind, temperature, and relative humidity) of high spatial density and C-band Doppler radar data (radial velocity and rainwater estimated from reflectivity and specific differential phase) with a local ensemble transform Kalman filter. With assimilation of both surface and radar data, a strong LMC was successfully predicted near the path of the actual tornado. When either surface or radar data were not assimilated, however, the LMC was not predicted. Therefore, both surface and radar data were essential for successful LMC forecasts. The factors controlling the strength of the predicted LMC, defined as a low-level maximum vertical vorticity, were clarified by an ensemble-based sensitivity analysis (ESA), which is a new approach for analyzing LMC intensification. The ESA showed that the strength of the LMC was sensitive to low-level convergence forward of the storm and to low-level relative humidity in the rear of the storm. Therefore, the correction of these low-level variables by assimilation of dense observations was found to be particularly important for forecasting and monitoring the LMC in the present case.
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      The Tornadic Supercell on the Kanto Plain on 6 May 2012: Polarimetric Radar and Surface Data Assimilation with EnKF and Ensemble-Based Sensitivity Analysis

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4230848
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    contributor authorYokota, Sho
    contributor authorSeko, Hiromu
    contributor authorKunii, Masaru
    contributor authorYamauchi, Hiroshi
    contributor authorNiino, Hiroshi
    date accessioned2017-06-09T17:33:34Z
    date available2017-06-09T17:33:34Z
    date copyright2016/09/01
    date issued2016
    identifier issn0027-0644
    identifier otherams-87204.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4230848
    description abstracttornadic supercell and associated low-level mesocyclone (LMC) observed on the Kanto Plain, Japan, on 6 May 2012 were predicted with a nonhydrostatic mesoscale model with a horizontal resolution of 350 m through assimilation of surface meteorological data (horizontal wind, temperature, and relative humidity) of high spatial density and C-band Doppler radar data (radial velocity and rainwater estimated from reflectivity and specific differential phase) with a local ensemble transform Kalman filter. With assimilation of both surface and radar data, a strong LMC was successfully predicted near the path of the actual tornado. When either surface or radar data were not assimilated, however, the LMC was not predicted. Therefore, both surface and radar data were essential for successful LMC forecasts. The factors controlling the strength of the predicted LMC, defined as a low-level maximum vertical vorticity, were clarified by an ensemble-based sensitivity analysis (ESA), which is a new approach for analyzing LMC intensification. The ESA showed that the strength of the LMC was sensitive to low-level convergence forward of the storm and to low-level relative humidity in the rear of the storm. Therefore, the correction of these low-level variables by assimilation of dense observations was found to be particularly important for forecasting and monitoring the LMC in the present case.
    publisherAmerican Meteorological Society
    titleThe Tornadic Supercell on the Kanto Plain on 6 May 2012: Polarimetric Radar and Surface Data Assimilation with EnKF and Ensemble-Based Sensitivity Analysis
    typeJournal Paper
    journal volume144
    journal issue9
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR-D-15-0365.1
    journal fristpage3133
    journal lastpage3157
    treeMonthly Weather Review:;2016:;volume( 144 ):;issue: 009
    contenttypeFulltext
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