”Cloud-Resolving 4D-Var Assimilation of Doppler Wind Lidar Data on a Meso-gamma Scale Convective System”

［ 2017年03月02日 ］

RIKEN International Symposium on Data Assimilation 2017
"Cloud-Resolving 4D-Var Assimilation of Doppler Wind Lidar Data on a Meso-gamma Scale Convective System"

We evaluated the effects of assimilating 3-dimensional Doppler wind lidar (DWL) data on the forecast of the heavy rainfall event of 5 July 2010 in Japan, produced by an isolated meso convective system (MCS) at a meso-gamma scale, in a system consisting of only warm rain clouds. Several impact experiments using the NHM-4DVAR and the Japan Meteorological Agency nonhydrostatic model with a 2-km horizontal grid spacing were conducted in which (1) no observations were assimilated (NODA), (2) radar reflectivity and radial velocity determined by Doppler radar and precipitable water vapor determined by GPS satellite observations were assimilated (CTL), and (3) radial velocity determined by DWL were added to the CTL experiment (LDR), and five data denial and two observational error sensitivity experiments. Although both NODA and CTL simulated an MCS, only LDR captured the intensity, location, and horizontal scale of the observed MCS. Assimilating DWL data improved the wind direction and speed of low-level airflows, thus improving the accuracy of the simulated water vapor flux. Our examination of the impacts of specific assimilations and assigned observation errors showed that assimilation of all data types is important for forecasting intense MCSs. Our investigation of the MCS structure showed that large amounts of water vapor were supplied to the rainfall event by southerly flow. A mid-level inversion layer led to production of exclusively liquid water particles in the MCS, and in combination with the humid airflow into the MCS, this inversion layer may be another important factor in its development.
http://www.data-assimilation.riken.jp/risda2017/

講師プロフィール
名前:Takuya Kawabata	所属:Meteorological Research Institute Japan
略歴:外部ページ

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