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The choice and interaction of the parametrization schemes in a numerical weather prediction (NWP) model are crucial for the forecast quality. Because, classically, the parametrization schemes can only communicate to each other via the tendencies of the averaged grid‐scale prognostic variables, a considerable amount of subgrid‐scale information available within the schemes is lost. To overcome this problem, subgrid‐scale information should be directly exchanged between the parametrization schemes. In the present paper already existing subgrid‐scale communication paths, which have been successfully used in operational forecasts in the past, are extended to a physical subgrid‐scale information exchange (PSIE) system including all atmospheric parametrization schemes. For our studies we have chosen the parametrization schemes of microphysics, convection, turbulence, fractional cloud cover and radiation. The COSMO model of Deutscher Wetterdienst (DWD) with a grid size of Δx = 7 km serves as the hosting model for the implementation of the PSIE system. However, the couplings are formulated in a way that also allows the PSIE system to be applied to other NWP models. Simulations of real case‐studies of different weather situations are performed and compared to DWD observational precipitation data from RADOLAN (radar measurements calibrated by station data). Visual inspection of precipitation maps reveals that precipitation patterns look more realistic with the full PSIE system, especially during air‐mass convection situations. This is confirmed by fractional skill score (FSS) analyses, which also give an insight into the temporal development of the PSIE effects. The relative impact of the PSIE paths and their interaction is also studied in conjunction with a factor separation analysis. Usually parametrization schemes in numerical forecast models exchange their subgrid‐scale information via the grid scale. To avoid information loss as a result of Reynolds averaging, our PSIE system provides direct exchange paths as shown in the sketch. As demonstrated with precipitation forecasts of real cases (comparisons by eye, a statistical fraction skill score analysis and a factor separation method), direct communication of subgrid‐scale information by means of a PSIE system can significantly improve the forecast. [ABSTRACT FROM AUTHOR]

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]