Assimilation of Lidar Water Vapour Mixing Ratio and Temperature Profiles into a Convection-Permitting Model

ournal of the Meteorological Society of Japan

article

Rohith THUNDATHIL¹  Thomas SCHWITALLA¹  Andreas BEHRENDT¹  Shravan Kumar MUPPA¹  Stephan ADAM¹  Volker WULFMEYER¹ 

[1] Institute of Physics and Meteorology, University of Hohenheim

关键词: data assimilation;    numerical weather prediction;    water vapour;    temperature;    lidar;   

DOI  :  10.2151/jmsj.2020-049

来源: Meteorological Society of Japan

【 摘 要 】

The impact of assimilating thermodynamic profiles measured with lidars into the Weather Research and Forecasting (WRF)-Noah-Multiparameterization model system on a 2.5-km convection-permitting scale was investigated. We implemented a new forward operator for direct assimilation of the water vapor mixing ratio (WVMR). Data from two lidar systems of the University of Hohenheim were used: the water vapor differential absorption lidar (UHOH WVDIAL) and the temperature rotational Raman lidar (UHOH TRL). Six experiments were conducted with 1-hour assimilation cycles over a 10-hour period by applying a 3DVAR rapid update cycle (RUC): 1) no data assimilation 2) assimilation of conventional observations (control run), 3) lidar–temperature added, 4) lidar–moisture added with relative humidity (RH) operator, 5) same as 4) but with the WVMR operator, 6) both lidar–temperature and moisture profiles assimilated (impact run). The root-mean-square-error (RMSE) of the temperature with respect to the lidar observations was reduced from 1.1 K in the control run to 0.4 K in the lidar–temperature assimilation run. The RMSE of the WVMR with respect to the lidar observations was reduced from 0.87 g kg −1 in the control run to 0.53 g kg −1 in the lidar-moisture assimilation run with the WVMR operator, while no improvement was found with the RH operator; it was reduced further to 0.51 g kg −1 in the impact run. However, the RMSE of the temperature in the impact run did not show further improvement. Compared to independent radiosonde measurements, the temperature assimilation showed a slight improvement of 0.71 K in the RMSE to 0.63 K, while there was no conclusive improvement in the moisture impact. The correlation between the temperature and WVMR variables in the static-background error-covariance matrix affected the improvement in the analysis of both fields simultaneously. In the future, we expect better results with a flow-dependent error covariance matrix. In any case, the initial attempt to develop an exclusive thermodynamic lidar operator gave promising results for assimilating humidity observations directly into the WRF data assimilation system.

【 授权许可】

Unknown