Data assimilation of temperature and water-vapor mixing-ratio lidar profiles in WRF

The lack of accurate observations affects the initial conditions of numerical weather prediction (NWP) models resulting in suboptimal forecasts. The assimilation of temperature and moisture profiles obtained from active remote-sensing lidar systems offers great potential for improving the predictive skills of NWP models (Thundathil et al., 2021, Bauer et al. 2023). Advanced data assimilation (DA) techniques, with suitable observational forward operators, enable the model to make use of such observations efficiently. New lidar systems provide temperature and humidity observations with high accuracy and resolution, which is highly beneficial for DA. The high accuracy avoids the need for a challenging bias correction of the data. It also simplifies operational use and minimizes the latency of the lidar data available for DA. In this regard, we make use of lidar observations to investigate the extent to which the assimilation of these data through advanced DA systems improves the analyses and corresponding forecasts. Our automated thermodynamic profiler based on the Raman lidar technique, the Atmospheric Raman Temperature and Humidity Sounder (ARTHUS) (Lange et al. 2019) was deployed in the framework of the WaLiNeAs (Water vapor Lidar Network Assimilation) (Flamant et al. 2021) initiative at the west coast of Corsica between 15 September and 10 December 2022. The participation of ARTHUS was possible due to a project funded by the German Research Foundation (DFG). Together with ARTHUS, a network of several other autonomous water-vapor lidars was deployed for providing more thermodynamic data across the Western Mediterranean. We expect that this network during its operation closed critical gaps present in lower tropospheric observations of current operational networks and satellite observations. We will present the first results of the impact of high-resolution temperature and water vapour mixing ratio lidar profiles in our data assimilation studies on heavy precipitation events, using the WRF 3DVAR-ETKF approach on the kilometer-scale.