FAST-OSE is based on the estimated time mean gridpoint information content (IC) derived by a data assimilation system from all available observations (baseline configuration). IC is determined through an analysis of the behavior of data assimilation – forecast cycles. After a suitable assessment of the presence or proximity of each observing system in/to each gridpoint in the 3D analysis grid (Observing System Indicator fields - OSI), the OSI of each observing system is statistically related to the overall IC of all observations. The resulting measures (e.g., correlation coefficients between overall IC and individual OSIs) then are used to predict how analysis error variance will change due to any variations in the baseline configuration of the different observing systems.
The new method is tested and evaluated in perfect model data assimilation – forecast experiments with a quasi-geostrophic model and simulated radiosonde, aircraft, and radar wind observations. Traditional OSEs will also be carried out and their results carefully compared to those from FAST-OSE. Recommendations for the use of FAST-OSE in the assessment of current and optimization of future observing systems will also be discussed.