Shallow convection is a subgrid process in cloud-resolving models for which their grid box is larger than the size of small cumulus clouds (Cu). At the same time such Cu substantially affect radiation properties and thermodynamic parameters of the low atmosphere. The main microphysical parameters used for calculation of radiative properties of Cu in cloud-resolving models are liquid water content (LWC), effective droplet radius, and cloud fraction (CF). In this study, these parameters of fields of small, warm Cu are calculated using large-eddy simulations (LESs) performed using the System for Atmospheric Modeling (SAM) with spectral bin microphysics. Despite the complexity of microphysical processes, several fundamental properties of Cu were found. First, despite the high variability of LWC and droplet concentration within clouds and between different clouds, the volume mean and effective radii per specific level vary only slightly. Second, the values of effective radius are close to those forming during adiabatic ascent of air parcels from cloud base. These findings allow for characterization of a cloud field by specific vertical profiles of effective radius and of mean liquid water content, which can be calculated using the theoretical profile of adiabatic liquid water content and the droplet concentration at cloud base. Using the results of these LESs, a simple parameterization of cloud-field-averaged vertical profiles of effective radius and of liquid water content is proposed for different aerosol and thermodynamic conditions. These profiles can be used for calculation of radiation properties of Cu fields in large-scale models. The role of adiabatic processes in the formation of microstructure of Cu is discussed.

In the absence of convection permitting numerical weather prediction (NWP) ensembles, the most recent deterministic NWP precipitation forecast is usually addressed. However, the exact intensity, location and timing of a deterministic precipitation forecast is not always reliable because of the chaotic nature and complexity of precipitation formation mechanisms. This study examines a way to optimize the use of precipitation forecasts for deterministic NWP models. More specifically, it suggests using a spatially smoothed time‐lagged ensemble (TLE) to obtain more reliable precipitation forecasts. A global NWP model (integrated forecast system—IFS) and a regional convection permitting NWP model (COSMO) over the Eastern Mediterranean during the period 2016–2018 were used for the analysis. First, the paper defines light, light–moderate and moderate intensities for 6 hr accumulated precipitation (6hAP) and investigates the corresponding definitions for 1 hr accumulated precipitation (1hAP). Next, fractional skill score (FSS) are used to estimate the optimal spatial smoothing scale of a deterministic precipitation forecast for the three intensity categories for the 6hAP and 1hAP. The FSS is also used to compare COSMO and IFS deterministic precipitation forecasts, and to analyse the skill degradation with the forecast range. It is quantitatively shown that the useful scale of precipitation forecasts is smaller for larger accumulation time intervals. Finally, precipitation forecasts for TLE are formed from successive smoothed deterministic forecasts and compared with the most recent deterministic forecast. It is found that, on average, TLEs have better skills for both 6hAP and 1hAP. The reason for this improvement in skill is illustrated using a case study as an example.

Parametrization of radiation transfer through clouds is an important factor in the ability of Numerical Weather Prediction models to correctly describe the weather evolution. Here we present a practical parameterization of both liquid droplets and ice optical properties in the longwave and shortwave radiation. An advanced spectral averaging method is used to calculate the extinction coefficient, single scattering albedo, forward scattered fraction and asymmetry factor (β_ext, ϖ, f, g), taking into account the nonlinear effects of light attenuation in the spectral averaging. An ensemble of particle size distributions was used for the ice optical properties calculations, which enables the effective size range to be extended up to 570 μm and thus be applicable for larger hydrometeor categories such as snow, graupel, and rain. The new parameterization was applied both in the COSMO limited-area model and in ICON global model and was evaluated by using the COSMO model to simulate stratiform ice and water clouds. Numerical weather prediction models usually determine the asymmetry factor as a function of effective size. For the first time in an operational numerical weather prediction (NWP) model, the asymmetry factor is parametrized as a function of aspect ratio. The method is generalized and is available on-line to be readily applied to any optical properties dataset and spectral intervals of a wide range of radiation transfer models and applications.

The ICON (icosahedral non-hydrostatic) numerical weather prediction model (NWP)’s sensitivity is evaluated for the geographical area of Greece. As ICON model has recently been endorsed operationally by the Members of the COSMO (Consortium for Small-scale Modeling); this attempt is in line with the further understanding of the model features, especially in the considered domain, characterized by a complex orography as well as an almost equally partitioned land–sea surface area. An extraordinary number of 24 model parameters have been tested for the whole 2020 year in reference to 88 Greek meteorological stations, with regard to the standard synoptic meteorological variables of 2 m temperature, 2 m minimum and maximum temperatures, dew-point temperature, 10 m wind intensity and 12 h accumulated precipitation. For these variables, the model sensitivity is given in terms of the annual average of all stations for the fifth lead day of the model runs when the sensitivity is expected to reach its peak. It was found that there is a considerable impact regarding the minimum and maximum values for many of the examined parameters in reference to their default values, and consideration is given to a heuristic recommendation on the selection of the most sensitive parameters.

The sensitivity of the ICON model is gauged towards the establishment of ICON-LEPS in place of the currently operational COSMO-LEPS. A broad list of the model’s parameters were tested over a domain covering the wider area of Greece and Italy in the year 2020. ICON model runs were performed on a 6.5 km horizontal grid and were enforced by the ECMWF operational forecast in 3 h intervals. The model’s sensitivities were applied to several surface meteorological fields via their areal averages in the last (132nd) lead time hour of the model runs, when they were expected to be at their climax. It was found that there was a considerable impact regarding the minimum and maximum values for many of the examined parameters in reference to their default values, providing a valuable insight into the understanding of the ICON model in reference to previous works regarding the COSMO model. In addition, due to the extensive forecast period, a seasonal dependence with respect to the considered meteorological fields was displayed. These features are expected to be of major importance in deciding the most important parameters for the application of perturbation techniques in the ensemble process and methodology.