On the Impact of the Diabatic Component in the Forecast Sensitivity Observation Impact Diagnostics Marta Janisková and Carla Cardinali Abstract Over the years, a comprehensive set of the linearized physical parame- trization schemes has been developed at ECMWF. These linearized schemes, oper- ationally used in data assimilation, parametrize both the dry physical processes (vertical diffusion, gravity wave drag, shortwave and longwave radiation) and the moist processes (convection, large-scale condensation and clouds) consistently with the physical parametrization of the nonlinear model (though some simplifications are applied). In this work, the representation of the moist physical processes in the adjoint assimilation model is compared with the representation of humidity in the energy norm used to compute the forecast sensitivity to observations in the short- range forecasts. Forecast Sensitivity Observation Impact using the adjoint model with only dry processes (dry adjoint) but moist energy norm in the sensitivity gradi- ent calculation is examined in contrast with the observation impact obtained when moist processes (moist adjoint) and dry energy norm are used. The performed study indicates that the use of the humidity term in the norm produces unrealistic humidity and temperature sensitivity gradients, which largely affect the observation forecast impact results. 1 Introduction Nowadays sophisticated data assimilation schemes are used for exploiting informa- tion from irregularly distributed observations in order to provide initial conditions for a numerical weather prediction (NWP) model. One of them is the four-dimensional variational (4D-Var) data assimilation, which is the operational system at the Euro- pean Centre for Medium-Range Weather Forecast (ECMWF) since November 1997 (Rabier et al. 2000). 4D-Var minimizes the distance between the model trajectory and the observations over a given time interval, using the adjoint equations of the M. Janisková ( ✉ ) ⋅ C. Cardinali European Centre for Medium–Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK e-mail: marta.janiskova@ecmwf.int © Springer International Publishing Switzerland 2017 S.K. Park and L. Xu (eds.), Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III), DOI 10.1007/978-3-319-43415-5_22 483