The Impact of Model Uncertainties on Analyzed Data in a Global Data Assimilation System

The impact of model uncertainties on analyzed data is investigated using a global data assimilation system. This issue is explored in a 3D-Var system based on the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) system using two convective parameterization schemes, the Simplified Arakawa scheme and the Community Climate Model (CCM) scheme. Two sets of six-hourly analysis data are generated for the summer of 2004. The difference between the resulting analyses using different convective parameterization schemes is found to be significantly greater than that between two well-known analyzed data sets, the NCEP/National Center for Atmospheric Research (NCAR) reanalysis (RA1) and the NCEP/DOE reanalysis (RA2). This dependency is more pronounced in data-sparse areas like the East Asian region than in data-rich areas like the North American region. Our study indicates that predictabilities for short- to medium-range forecasts in the global forecast system are indirectly influenced by forecast model accuracy via the quality of the initial conditions.