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Abstract: For a long time, classical ensemble forecasts have concentrated on simulating the uncertainties of the observations by variations of the initial and boundary conditions. To supplement this with inherent contributions from the forecast model itself, stochastic physics has been proposed as an appropriate approach to improve the forecast quality and to increase the ensemble spread of underdispersive ensembles. Concentrating on cloud processes and their temporal development we here introduce a set of stochastic parameters into existing deterministic physical parameterization schemes as for the trigger function, closure assumption and turbulent mixing in the convection scheme, for the initial ice concentration and turbulent collection kernel in the grid scale precipitation/microphysics scheme and for the subgrid scale cloud cover in the radiation scheme. Considering the stochastic parameters to be subject to unresolved turbulent fluctuations, we derive the corresponding stochastic processes from a Langevin equation based on an approach of direct numeric simulation. This allows for the inclusion of a turbulent autocorrelation time calculated from the eddy dissipation rate and the turbulent kinetic energy of the hosting model. Ensemble simulations of two meteorologicaly different real cases are compared to deterministic control simulations and observations of 1h precipitation sums. The turbulent autocorrelation time shows pronounced spatial variability correlated with frontal dynamics and convective activity. Maps of spatial precipitation patterns and continuous ranked probability score (CRPS) improvements show a better forecast quality of the ensemble mean including stochastic physics. Depending on the meteorological situation, the ensemble spread and the CRPS improvement both exhibit a spin up time and reach a saturation. For air-mass convection/postfrontal showers the spread and the CRPS improvement are higher than for frontal precipitation. The stochastic parameterization of subgrid scale cloud cover is found to degrade the forecast quality of the ensemble. This is explained by a too homogeneous spatial distribution of the added variability. Consequently, this part of the stochastic parameterization has been excluded from the ensemble simulations and is left in its operational deterministic version. [Copyright &y& Elsevier]

Abstract: Convection can cause severe precipitation events and is thus of major interest for quantitative precipitation forecasts. Due to its small horizontal scale convection usually has to be parameterized in numerical weather prediction (NWP) models as a subgrid scale phenomenon. Classical mass flux convection schemes assume grid box sizes much larger than the scale of the convective circulation. Thus, the convective mass transport is closed in the local grid column and no net mass transport occurs on the grid scale. In contemporary NWP models with grid sizes of a few kilometers, where convection is already partially resolved, the classical approach leads to a conceptual problem. This can be overcome by a hybrid mass flux convection scheme (HYMACS), in which only the small scale convective updrafts and downdrafts are parameterized, whereas the treatment of the larger scale environmental subsidence is left to the grid scale equations. Different to the classical schemes, HYMACS produces a net convective mass flux exerting pressure gradient forces to the grid scale model. We discuss real cases of different meteorological situations simulated with the operational weather forecast model COSMO of the German Meteorological Service as the hosting model of HYMACS. Precipitation and cloud top pressures simulated with HYMACS are compared with results from classical convection schemes and observational data from the DWD station network and the SEVIRI (MSG) satellite instrument. [Copyright &y& Elsevier]

The new one-dimensional forecast model PAFOG for radiation fogs and low-level stratiform clouds will be presented. The aim of the model is to improve the local visibility forecast on airports and other traffic locations where fog and low-level stratus frequently occur. PAFOG has been developed on the basis of the microphysical fog model MIFOG of Bott et al. [J. Atmos. Sci. 47 (1990) 2153]. To obtain a numerically efficient model, the detailed spectral cloud microphysics of MIFOG has been replaced by the parameterization scheme of Chaumerliac et al. [J. Geophys. Res. 92 (1987) 3114]. Furthermore, according to Siebert et al. [Beitr. Phys. Atmos. 65 (1992a) 93], a model for low vegetation is included in PAFOG so that now fog evolution as influenced by different types of vegetation can also be accounted for.The performance of PAFOG has been tested by comparing the model results with routine observations of the German Weather Service. Nine different weather periods comprising a total of 45 days have been investigated. In 41 cases, PAFOG yields agreement with the observations in terms of occurrence or nonoccurrence of fog or stratiform clouds. During radiation fogs, the calculated and observed visibilities are quite similar. However, in the model simulations the formation of dense fogs tends to be somewhat delayed. From the case studies with stratiform clouds, it is seen that cloud evolution in time and space strongly depends on the value of the large-scale subsidence. Since this quantity is not available from measurements, it must be provided by means of a numerical weather forecast model. [Copyright &y& Elsevier]