偏波ドップラーレーダの同化によるメソ対流系の降水予測精度向上に関する研究  [in Japanese] Improvement of Precipitation Forecast on Mesoscale Convective System using Data Assimilation of Polarimetric Doppler Radar  [in Japanese]

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Abstract

The short lead time rainfall prediction by Numerical Weather Prediction model has some difficulties in the spin-up problem. Therefore, data assimilation (DA) is expected to improve the initial condition in the model. In this study, our developed ensemble DA system, CReSS-LETKF, and the method of estimation of ice-water mixing ratios are employed. DA of rain, graupel, ice crystal, snowflake and Doppler velocity estimated by polarimetric Doppler radar are carried out after the first convective cloud in mesoscale convective systems is generated. As a result, the first convective clouds formed in initial condition have effective influence on the short lead time rainfall prediction. As the next challenging step, DA is carried out before the first convective cloud. As a result, convective clouds are not generated although the atmosphere conditions, such as potential temperature change

Journal

  • 京都大学防災研究所年報

    京都大学防災研究所年報 (59), 298-322, 2015

    京都大学防災研究所

Codes

  • NII Article ID (NAID)
    120005867074
  • NII NACSIS-CAT ID (NCID)
    AN00027786
  • Text Lang
    JPN
  • Article Type
    departmental bulletin paper
  • Journal Type
    大学紀要
  • ISSN
    0386-412X
  • NDL Article ID
    027653153
  • NDL Call No.
    YH247-547
  • Data Source
    NDL  IR 
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