Clear and cloudy sky infrared brightness temperature assimilation using an ensemble Kalman filter

Abstract

A regional‐scale Observation System Simulation Experiment was used to examine how the assimilation of window infrared brightness temperatures for both clear‐sky and cloudy sky conditions impacts the accuracy of atmospheric analyses at convection‐permitting scales when using an ensemble Kalman filter data assimilation system. The case study tracked the evolution of a large extratropical cyclone and associated cloud features across the central United States during 4–5 June 2005. Overall, the assimilation results revealed that the infrared brightness temperatures had a large positive impact on the simulated cloud field with the best results achieved when both clear‐sky and cloudy sky observations were assimilated. The infrared brightness temperatures substantially reduced the bias and root mean square error in the cloud top pressure, cloud water path, 6.95 and 11.2 μm brightness temperatures, and vertical profiles of cloud condensate. Inspection of the thermodynamic variable statistics showed that the assimilation of conventional surface and upper‐air observations produced more accurate temperature and wind analyses. When both cloud‐affected and thermodynamic variables are considered, however, the best analysis was achieved when conventional observations and clear‐sky and cloudy sky 8.5 μm brightness temperatures were assimilated simultaneously.