Title: Forecast error contribution of the global observing system using different energy norms and different representation of physical processes in the adjoint model

Authors: Marta Janiskova (ECMWF, Shinfield Park, Reading, RG2 9AX, UK)
Carla Cardinali (ECMWF, Shinfield Park, Reading, RG2 9AX, UK)

Over the years, a comprehensive set of the linearized physical parametrization schemes has been developed at ECMWF. These linearized schemes, operationally 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 nonlinear model (though some simplifications are applied). In particular, the moist processes representation in the adjoint model not only provide a better time evolution of the model state during the assimilation procedure compared to the adiabatic model or model with the dry processes only, but they also allow the assimilation of variables related to physical processes such as precipitations or clouds.

In this work, the representation of the moist physical processes in 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. Observation forecast error contribution using the adjoint model with only dry processes (dry adjoint) but moist energy norm in the sensitivity gradient calculation is compared with error contribution obtained with moist processes (moist adjoint) and dry energy norm. Results will be presented and summarized.

GMAO Head: Michele Rienecker Global Modeling and Assimilation Office NASA Goddard Space Flight Center

Curator: Nikki Privé Last Updated: May 27 2011