Data assimilation of temperature and water-vapor mixing-ratio lidar profiles in WRF
- 1University of Hohenheim, Institute of Physics and Meteorology, Stuttgart, Germany (diego.lange@uni-hohenheim.de)
- *A full list of authors appears at the end of the abstract
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.
Diego Lange Vega diego.lange@uni-hohenheim.de Thomas Schwitalla thomas.schwitalla@uni-hohenheim.de Paolo Di Girolamo paolo.digirolamo@unibas.it Patrick Chazette patrick.chazette@lsce.ipsl.fr Clotilde Augros clotilde.augros@meteo.fr Cyrille Flamant cyrille.flamant@latmos.ipsl.fr Andreas Behrendt andreas.behrendt@uni-hohenheim.de Michael Sicard michael.sicard@upc.edu Julien Totems julien.totems@lsce.ipsl.fr Noemi Franco noemi.franco@unibas.it Brousseau Pierre pierre.brousseau@meteo.fr Nadia Fourrie nadia.fourrie@meteo.fr Valérie Vogt valerie.vogt@meteo.fr Frederic Laly frederic.laly@institutoptique.fr Jeremy Lagarrigue jeremy.lagarrigue@lsce.ipsl.fr Marco Dipaolantonio Marco.dipaolantonio@artov.ismar.cnr.it José Luis Gomez-Amo Jose.L.Gomez-Amo@uv.es Donato Summa donato.summa@imaa.cnr.it Davide Dionisi davide.dionisi@artov.ismar.cnr.it Constantino Muñoz Porcar constantino.munoz@upc.edu Adolfo Comeron adolfo.comeron@upc.edu Pedro Catalan Pedro.Catalan@uv.es Alejandro Rodriguez Gomez alejandro.rodriguez.gomez@upc.edu Volker Wulfmeyer volker.wulfmeyer@uni-hohenheim.de
How to cite: Lange Vega, D. and the WaLiNeAs (Water vapor Lidar Network Assimilation) Team: Data assimilation of temperature and water-vapor mixing-ratio lidar profiles in WRF, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15570, https://doi.org/10.5194/egusphere-egu24-15570, 2024.
