THE IMPACT OF DIABATIC INITIALIZATION ON STRATOSPHERIC ANALYSES, FORECASTS, AND TRANSPORT EXPERIMENTS

An efficient diabatic initialization procedure has been applied to the entire tropospheric and stratospheric domain within the Goddard Earth Observing System (GEOS) 4-D data assimilation system. The initializat ion, or initial balancing and noise control technique, is based on the application of the iterative Euler scheme as a highly selective, effi cient filter. The GEOS 46-layer General Circulation Model extending to 0.1 hPa was used to produce the first-guess fields within the data as similation system. This model is also used to calculate medium-range s tratospheric forecasts. Within the diabatic initialization approach, t he model itself is used to balance the resulting analysis, or initial fields. The diabatic initialization technique appears to be a useful t ool which improves stratospheric analyses and forecasts by controlling small-scale high-frequency non-meteorological oscillations and shocks , or spin-up effects resulting from initial imbalances. Changes due to initialization are cumulative with time, hence initialization affects positively the slowly varying components of stratospheric flows, resu lting in better representation of climate characteristics. Experiments performed to assess the impact of diabatic initialization on monthly mean analyses and diagnostic fields produced by the data assimilation system show that it provides an efficient noise control, especially fo r the upper stratospheric levels and in the polar regions. Stratospher ic analysis errors have been significantly reduced by applying diabati c initialization. The major characteristics of stratospheric circulati on, such as the residual mean meridional circulation, potential vortic ity and Eliassen-Palm or EP-flux distributions, are improved when usin g diabatic initialization. The ozone transport experiments show improv ements due to using initialized winds in terms of better agreement wit h data. Ten-day stratospheric forecast scores have been improved when using initialized, or balanced initial conditions. The initialization method used in the study is easy to implement and is computationally e fficient. It appears to be a useful component of a stratospheric data assimilation system.