First Steps Towards Data Assimilation of Differential Absorption Lidar 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 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 with regard to the continuous data supply, their operational use, and their impact on NWP.  

In this contribution, we present first results of the assimilation of two ground-based remote sensing instruments into the kilometer-scale ensemble data assimilation system (KENDA): water vapour mixing ratio from a Differential Absorption Lidar (DIAL) and radar reflectivity 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 their observation minus first guess statistics have been shown to produce promising results. To assess the impact in an operational setting, dedicated data assimilation experiments were conducted and compared to reference experiments without these additional observations. Based on the successful data assimilation cycling experiments, first forecast experiments including the DIAL have been performed. Current results indicate a neutral to positive impact on humidity, temperature, and wind forecasts. The impact of cloud radar data in such experiments is currently under investigation by testing different settings.

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.