Data Assimilation of Differential Absorption Lidar Data and Cloud Radar Data

To improve the forecast quality of numerical weather prediction (NWP), the German Meteorological Service (Deutscher Wetterdienst, DWD) has initiated a project aimed at assessing data quality and assimilation of profile observations from ground-based remote sensing instruments that have not yet been exploited operationally.

The objective of this initiative is to fill the observational gap in the atmospheric boundary layer, especially with respect to short time scales, by providing continuous, high-temporal-resolution profiles of thermodynamic variables, wind, and cloud properties. These observations are expected to be especially beneficial for weather forecasting applications. The DWD is evaluating various remote sensing systems for their ability to provide continuous operational feasibility and impact on NWP.  

In this contribution, we present results of the assimilation of two ground-based remote sensing instruments into the kilometre-scale ensemble data assimilation system (KENDA): water vapour mixing ratio profiles from a Differential Absorption Lidar (DIAL) and radar reflectivity profiles from a cloud radar. For the integration of the DIAL observations into the data assimilation code environment, only small adjustments were necessary. In contrast, the cloud radar data required an adaptation of the complex forward operator EMVORADO (Efficient Modular Volume scan Radar Operator), which was originally developed and previously used only for precipitation radars.

In an initial step, single observation data assimilation experiments and the corresponding observation minus first guess statistics showed promising results. To assess the impact in an operational setting, we performed dedicated data assimilation experiments with and without these additional observations. We considered both summer and winter periods, as well as different observation error specifications for the DIAL measurements. Based on the successful data assimilation cycling experiments, we conducted first forecast experiments, including DIAL water vapour mixing ratio observations. The results indicate a positive impact on humidity and temperature forecasts. We are currently investigating the impact of cloud radar reflectivity data in such experiments. Preliminary results show a neutral to slightly positive impact on the humidity first guess.

Our findings suggest that ground-based remote sensing data can provide valuable additional information for convective-scale data assimilation and justify more extensive impact studies in the context of NWP.