First assimilation of temperature lidar data into an NWP model: impact on the simulation of the temperature field, inversion strength and PBL depth

The impact of assimilating lower–tropospheric lidar temperature profiles into a numerical weather prediction(NWP) model was investigated. The profiles were measured with the Temperature Rotational Raman Lidar (TRRL) of the University of Hohenheim on 24 April 2013. The day showed the development of a typical daytime planetary boundary layer(PBL) with no optically thick clouds. The Weather Researchand Forecasting modelwas operated with 57 vertical levels covering Central Europe with 3 km horizontal resolution. Three different experiments were carried out with a rapid update cyclewith hourly three–dimensional variational data assimilation. The impact run (ALL_DA) was performed with the assimilation of conventional data and the additional assimilation of TRRL profiles between 0900 and 1800 UTC in a height range from about 500 m to 3000 m above ground level with a vertical resolution of about 100 m. In CONV_DA and NO_DA, only conventional data and no data were assimilated, respectively. To consider the representativeness of the TRRL profiles, an observation errorof 0.7 K was used for all heights. The assimilation was performed using the radiosondeoperator. The TRRL data assimilationcorrected the temperature profiles towards the lidar data. In the mean, the boundary layer height was improved by 60 m in ALL_DA compared to the TRRL data and the temperature gradient in the entrainment layer by 0.19 K (100 m)− 1. While ALL_DA showed a root mean square error (RMSE) of 0.6 K compared to the TRRL data, the RMSE of CONV_DA was twice as large. Compared to data from radiosondeslaunched at the TRRL site, ALL_DA showed a significantly smaller RMSE than CONV_DA in two out of four times radiosondedata were available. We conclude that the assimilation of TRRL data has great potential to close the critical gap of missing temperature observations in the lower troposphere.