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A New Tool for Prediction of Observing System Impact (POSI)
NOAA/ESRL Global Systems Division
Among a variety of tools available to assess the impact of changes in observing systems on the quality of analyses and forecasts, Observing System Experiments (OSEs) provide the most reliable results. However, their large computational cost seriously limits the range of observing system configurations that can be assessed with OSEs. To eliminate the need for the computationally intensive execution of many individual OSE experiments, we propose a novel statistical approach to analyze output from an operational forecast system over a period of time (e.g., NCEP’s GFS/GSI over a month or season) for the Prediction of Observing System Impact (POSI).
POSI is based on the estimated time mean grid point Extracted Observational Information (EOI) derived by a data assimilation system from all available observations (baseline configuration). EOI 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 EOI of all observations. The resulting measures (e.g., correlation coefficients between overall EOI 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 observations. Traditional OSEs will also be carried out and their results carefully compared to those from POSI. Recommendations for the use of POSI in the assessment of current and optimization of future observing systems will also be discussed.
Wednesday, April 26
Noon to 1 p.m.
Mesa Lab, Damon Room