This study presents the first convective-scale 1,000-member ensemble simulation over central Europe, which provides a unique data set for various applications. A comparison with the operational regional 40-member ensemble of Deutscher Wetterdienst shows that the 1,000-member simulation exhibits realistic spread properties overall. Based on this, we discuss two potential applications. First, we quantify the sampling error of spatial covariances of smaller subsets compared with the 1,000-member simulation. Knowledge about sampling errors and their dependence on ensemble size is crucial for ensemble and hybrid data assimilation and for developing better approaches for localization in this context. Secondly, we present an approach for estimating the relative potential impact of different observable quantities using ensemble sensitivity analysis. This will provide the basis for consecutive studies developing future observation and data assimilation strategies. Sensitivity studies on the ensemble size indicate that about 200 ensemble members are required to estimate the potential impact of observable quantities with respect to precipitation forecasts. K E Y W O R D Sconvective-scale, covariance, data assimilation, ensemble sensitivity analysis, localization, observing system, sampling error

Abstract. There is a rising interest in improving the representation of clouds in numerical weather prediction models. This will directly lead to improved radiation forecasts and, thus, to better predictions of the increasingly important production of photovoltaic power. Moreover, a more accurate representation of clouds is crucial for assimilating cloud-affected observations, in particular high-resolution observations from instruments on geostationary satellites. These observations can also be used to diagnose systematic errors in the model clouds, which are influenced by multiple parameterisations with many, often not well-constrained, parameters. In this study, the benefits of using both visible and infrared satellite channels for this purpose are demonstrated. We focus on visible and infrared Meteosat SEVIRI (Spinning Enhanced Visible InfraRed Imager) images and their model equivalents computed from the output of the ICON-D2 (ICOsahedral Non-hydrostatic, development version based on version 2.6.1; Zängl et al., 2015) convection-permitting, limited area numerical weather prediction model using efficient forward operators. We analyse systematic deviations between observed and synthetic satellite images derived from semi-free hindcast simulations for a 30 d summer period with strong convection. Both visible and infrared satellite observations reveal significant deviations between the observations and model equivalents. The combination of infrared brightness temperature and visible reflectance facilitates the attribution of individual deviations to specific model shortcomings. Furthermore, we investigate the sensitivity of model-derived visible and infrared observation equivalents to modified model and visible forward operator settings to identify dominant error sources. Estimates of the uncertainty of the visible forward operator turned out to be sufficiently low; thus, it can be used to assess the impact of model modifications. Results obtained for various changes in the model settings reveal that model assumptions on subgrid-scale water clouds are the primary source of systematic deviations in the visible satellite images. Visible observations are, therefore, well-suited to constrain subgrid cloud settings. In contrast, infrared channels are much less sensitive to the subgrid clouds, but they can provide information on errors in the cloud-top height.

Abstract. Satellite observations provide a wealth of information on atmospheric clouds and cover almost every region of the globe with high spatial resolution. The measured radiances constitute a valuable data set for evaluating and improving clouds and radiation representation in climate and numerical weather prediction (NWP) models. An accurate, bias-free representation of clouds and radiation is crucial for data assimilation and the increasingly important solar photovoltaic (PV) power production prediction. The present study demonstrates that visible (VIS) and infrared (IR) Meteosat SEVIRI observations contain valuable and complementary cloud information for these purposes. We analyse systematic deviations between satellite observations and convection-permitting, semi-free ICON-D2 hindcast simulations for a 30-day period with strong convection. Both visible and infrared satellite observations reveal significant deviations between the observations and model equivalents. The combination of infrared brightness temperature and visible solar reflectance allowed to attribute individual deviations to specific model shortcomings. Furthermore, we investigate the sensitivity of model-derived VIS and IR observation equivalents to modified model and visible forward operator settings to identify dominant error sources. The results reveal that model assumptions on subgrid-scale water clouds are the primary source of systematic deviations in the visible spectrum. Visible observations are, therefore, well-suited to advance this essential model assumption. The visible forward operator uncertainty is lower than uncertainties introduced by model parameter assumptions by one order of magnitude. In contrast, infrared satellite observations are very sensitive to ice cloud model assumptions. Finally, we show a strong negative correlation between VIS solar reflectance and global horizontal irradiance. This implies that improvements in VIS satellite reflectance prediction will coincide with improvements in the prediction of surface irradiation and PV power production.

<p>Satellite provide high-resolution information on the state of the atmosphere and thus represent observations are well-suited for data assimilation and model evaluation. So far mainly the thermal infrared channels have been utilized for these purposes. However, there is a rising interest to use also the channels in the solar part of the spectrum, which contain additional, complementary information. Visible channels can provide better information on the water and ice content of clouds than thermal infrared channels, have no problems to detect low clouds and are sensitive to cloud microphysics and the cloud top structure. Moreover, visible reflectances are strongly correlated with the solar irradiation at the surface and thus their assimilation has a clear potential to improve also radiation forecasts.</p><p>So far visible satellite images have not been assimilated directly for operational purposes, as multiple scattering dominates in the visible spectral range and makes radiative transfer (RT) computations with standard methods complex and slow. Only recently, we developed a sufficiently fast and accurate forward operator that relies on a compressed reflectance look-up table (LUT) computed with slow standard RT methods. Here we report on using feed-forward neural networks as an alternative to the look-up table and demonstrate that it is possible to achieve higher speed and accuracy. Moreover, both the amount of training data and the memory required by the operator can be reduced by three orders of magnitude. A further advantage is that tangent-linear and adjoint versions can easily be derived for arbitrary network structures and do not have to be changed when the network is trained with different data.</p><p>We will also discuss two ways to use the forward operator to improve forecasts. First, we show that observed and synthetic visible  Meteosat SEVIRI images can be used to detect systematic errors in the model clouds that can cause severe problems for data assimilation. Second, based on assimilation experiments using the ICON-D2 model and the local ensemble transformation Kalman filter implemented in DWDs data assimilation coding environment (DACE) we demonstrate for test periods of several weeks that errors in the cloud distribution and the surface radiation can be significantly reduced by assimilating visible SEVIRI images.</p>