Stochastic treatment of cloud related processes in nonhydrostatic weather prediction models

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Cloud processes have a strong influence on the energy and moisture budget of the atmosphere. Since in numerical weather prediction (NWP) models cloud related processes usually are of subgrid scale, they have to be parameterized in a set of parameterization schemes describing e.g. grid scale clouds and precipitation, subgrid scale cumulus convection and atmospheric radiative transfer. The aim of the proposed research project is to develop a new stochastic physics approach (SPA) for the treatment of cloud related processes in nonhydrostatic NWP models. The stochastic approach shall represent unresolved subgrid scale variability and model parameter uncertainties. For this purpose, selected fixed model parameters will be replaced by appropriate stochastic processes.The application of the SPA shall increase the forecast skill of the hosting NWP model. In an ensemble prediction system (EPS) the ensemble spread shall grow to more realistic values and thus help to overcome the current problem of too small spread (underdispersion) when only considering uncertainties in the initial or boundary conditions. The algorithms of the stochastic approach and the EPS will be developed for use in any nonhydrostatic NWP model that includes prognostic microphysics/precipitation, a radiation scheme with fractional cloud cover and an arbitrary mass flux convection scheme.