Assimilating Doppler wind lidar observations from the ‘Swabian MOSES 2023’ campaign reveals wind biases in the ICON-D2 model

The development of summertime convection is sensitive to mesoscale atmospheric conditions, which, in particular in complex terrain, are not well captured by conventional observation systems. Here, we investigate whether the assimilation of substantial additional observations of lower-tropospheric thermodynamic and dynamic variables at high resolution has the potential to improve the resulting analysis used for forecast initialization, and ultimately the predictability of convective events.

The high-resolution observations used here have been obtained during the ‘Swabian MOSES 2023’ campaign, conducted in June, July and August 2023 in the southwest German mountain ranges, a region that is particularly prone to hailstorms. A key component was an unprecedented network of 12 Doppler wind lidars (DWL), a configuration never used before in data assimilation experiments. We adopted the regional forecasting system of Deutscher Wetterdienst, the ICON model at 2 km resolution (ICON-D2) coupled to the Kilometer Scale Ensemble Data Assimilation system (KENDA) based on the Local Ensemble Transform Kalman Filter (LETKF) with an hourly assimilation step to produce a campaign and a control reanalysis. The campaign reanalysis uses, in addition to all operationally available observations used in the control reanalysis, (i) DWL-retrieved vertical profiles of the horizontal wind components, (ii) X-band radar reflectivity, (iii) targeted radiosoundings from two sites during intensive observation periods, (iv) ground-based zenith path-delay data from a domain-wide Global Navigation Satellite Systems receiver network, and (v) 2m-temperature and relative humidity from meteorological masts at the campaign sites.

We show that the additional campaign observations have a substantial influence on the reanalysis. We focus specifically on the mesoscale flow in the Black Forest region where the dense wind lidar observations lead to systematic differences in wind speed and direction between the campaign and the reference analysis. We discuss possible sources of these differences by further stratifying the analysis increments by time of day, different flow regimes, and stability. Furthermore, we investigate the impact of additionally assimilated observations on reforecasts by comparing forecasts initialized from the campaign and control reanalysis. Our results motivate future reanalyses projects, such as a high-resolution reanalysis for the ‘Multi-scale transport and exchange processes in the atmosphere over mountains – programme and experiment’ (TEAMx) summer extensive observation period.