【7/12更新】2022年4月1日からのCiNii ArticlesのCiNii Researchへの統合について

偏波レーダーから推定した定性的降水粒子情報の雲アンサンブル同化

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  • Cloud Ensemble Assimilation of Qualitative Microphysics Information Estimated from Polarimetric Radar Observation

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An impact on rainfall prediction by the data assimilation of the qualitative precipitation information estimated from the polarimetric radar measurements is evaluated. Our developed meso-scale data assimilation system, CReSS-LETKF, is employed as a data assimilation method. The observation operator of data assimilation which converts the model variables into the mixing ratio of each ice-phased cloud microphysics variables such as graupel, snowflake and ice crystal is developed using both the polarimetric radar data and the video-sonde observation. A case of tapering cloud that caused a heavy rainfall at Kyoto in 2012 is chosen as an application. In the assimilation case, radar reflectivity and Doppler velocity are also assimilated. As a result, a strong rain band is formed which is similar to observation. The results will have effective influence on the short lead time rainfall prediction

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