Assessing the Impact of the Covariance Localization Radius when Assimilating Infrared Brightness Temperature Observations Using an Ensemble Kalman Filter

摘要

A regional-scale Observing System Simulation Experiment is used to examine how changes in the horizontal covariance localization radius employed during the assimilation of infrared brightness temperature observations in an ensemble Kalman filter assimilation system impacts the accuracy of atmospheric analyses and short-range model forecasts. The case study tracks the evolution of several extratropicalweather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results indicate that assimilating 8.5-mm brightness temperatures improves the cloud analysis and forecast accuracy, but has the tendency to degrade the water vapor mixing ratio and thermodynamic fields unless a small localization radius is used. Vertical cross sections showed that varying the localization radius had aminimal impact on the shape of the analysis increments; however, their magnitude consistently increased with increasing localization radius. By the end of the assimilation period, themoisture, temperature, cloud, and wind errors generally decreased with decreasing localization radius and became similar to the Control case in which only conventional observations were assimilated if the shortest localization radius was used. Short-range ensemble forecasts showed that the large positive impact of the infrared observations on the final cloud analysis diminished rapidly during the forecast period, which indicates that it is difficult to maintain beneficial changes to the cloud analysis if themoisture and thermodynamic forcing controlling the cloud evolution are not simultaneously improved.These results showthat although assimilation of infrared observations consistently improves the cloud field regardless of the length of the localization radius, it may be necessary to use a smaller radius to also improve the accuracy of themoisture and thermodynamic fields.