Abstract Atmospheric stability plays an essential role in the evolution of weather events. While the upper troposphere is sampled by satellite sensors, and in situ sensors measure the atmospheric state close to the surface, only sporadic information from radiosondes or aircraft observations is available in the planetary boundary layer. Ground-based remote sensing offers the possibility to continuously and automatically monitor the atmospheric state in the boundary layer. Microwave radiometers (MWR) provide temporally resolved temperature and humidity profiles in the boundary layer and accurate values of integrated water vapor and liquid water path, and the differential absorption lidar (DIAL) measures humidity profiles with high vertical and temporal resolution up to 3000-m height. Both instruments have the potential to complement satellite observations by additional information from the lowest atmospheric layers, particularly under cloudy conditions. This study presents a neural network retrieval for stability indices, integrated water vapor, and liquid water path from simulated satellite- and ground-based measurements based on the COSMO regional reanalysis (COSMO-REA2). Focusing on the temporal resolution, the satellite-based instruments considered in the study are the currently operational Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the future Infrared Sounder (IRS), both in geostationary orbit. Relative to the retrieval based on satellite observations, the additional ground-based MWR/DIAL measurements provide valuable improvements not only in the presence of clouds, which represent a limiting factor for infrared SEVIRI/IRS, but also under clear-sky conditions. The root-mean-square error for convective available potential energy, for instance, is reduced by 24% if IRS observations are complemented by ground-based MWR measurements.

<p>State-of-the-art high resolution, convection resolving NWP models and reanalysis typically operate on a horizontal resolution of 1-5 km. These models require specific data assimilation schemes with frequent analysis (every 1-6 h) and corresponding dense and frequent observations to define the detailed initial conditions. Key variables needed for convection-resolving data assimilation are, among others, the 3-dimensional fields of temperature and humidity. Both variables are not adequately (vertically, horizontally and temporally) measured by current observing systems. The vertical resolution of atmospheric profiles provided by satellite sensors is poor, especially in the atmospheric boundary layer, where convection resolving models have many layers close to the surface to better describe the surface-atmosphere exchange processes. To the necessary information, a new generation of observations through the lowest few kilometers of the atmosphere is required. A network of ground-based remote sensing sensors (e.g., microwave radiometer, MWR, or water vapor DIAL) has the potential to provide real time profile observations to forecasting centers. Maintaining an operational observing network is a difficult and expensive task. Therefore, it is essential to evaluate the impact of different components of the current observing system and to assess the potential contribution of a new observing components to the analysis of the atmospheric state.<br>In our study we perform an Observing System Simulation Experiment (OSSE) to show the potential benefit of ground-based MWR for improving the initial thermodynamic state of the atmosphere. The Nature Run (NR), representing the “true” atmosphere, is performed using the ICON-LES model for a 150x150 km domain in the western part of Germany with 500 m horizontal resolution for summer convective cases. The MWR observations, dependent on cloudiness, temperature and humidity profiles, are simulated with the radiative transfer model RTTOV-gb and assimilated into the convection resolving ICON model (2km horizontal resolution). In this contribution, we will present first impact studies of assimilating synthetic observations of single MWR instruments on the initial temperature and humidity fields and extend the approach for evaluating the effect of a network of instruments with respect to other variables relevant for solar power applications, such as precipitation and solar radiation.</p>