First Steps Towards Data Assimilation of Cloud Radar and Differential Absorption Lidar 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 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 nowcasting and (short-term) forecasting applications. The DWD is evaluating various remote sensing systems with regard to the continuous data supply, their operational use and their impact on NWP.  

In this contribution, we present the integration and assimilation of two such data sources into the kilometer-scale ensemble data assimilation system (KENDA): radar reflectivity from a cloud radar and water vapour mixing ratio from a Differential Absorption Lidar (DIAL). The complex forward operator EMVORADO (Efficient Modular Volume scan Radar Operator), originally developed and previously used only for precipitation radars, has been adapted for cloud radar data. In contrast, the DIAL observations do not require a complex forward operator, and only minor adjustments have been made to the data assimilation code environment of the DWD.

Observation minus first guess statistics, as well as first single observation data assimilation experiments have been shown to produce promising results. To assess the overall impact, dedicated data assimilation experiments were conducted and compared to reference experiments without these additional observations. First results indicate a positive impact of the DIAL data on first guess humidity and temperature fields in the analysis cycle, while the impact of cloud radar data appears neutral at this stage. These findings suggest that these ground-based remote sensing data can provide valuable additional information for convective-scale data assimilation and form a sound basis for further impact studies in the context of NWP.