Representing model uncertainty in data assimilation with stochastic physics

All data assimilation systems that use ensemble forecasts to estimate background-error covariances require some method for representing missing sources of uncertainty, such as model error. The current NCEP operational hybrid 3d ensemble-Var system uses ad-hoc inflation (both multiplicative and additive). In this talk I will discuss the possibility of replacing the additive component with a stochastic representation of model uncertainty included in the forecast model itself. The stochastic representation is made up of three components; 1) stochastic kinetic-energy backscatter (SKEB), 2) perturbed boundary-layer humidity (SHUM) and 3) stochastically perturbed physics tendencies (SPPT). SKEB is meant to represent the effect of unresolved or poorly resolved scales of motion on the resolved scales, SPPT is mean to represent structural uncertainty in the physical parameterizations, and SHUM is meant to represent the effect of sub-grid scale variations of humidity on convection. Results will be presented showing the impact of replacing the current additive inflation scheme with a combination of these three stochastic schemes in the background forecast ensemble, and future plans for further development of model uncertainty parameterizations will be discussed.