8B.1
Assimilation of surface-based profiler observations during an observation system simulation experiment: Part 1: Analysis Impact

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Thursday, 27 January 2011: 8:30 AM
Assimilation of surface-based profiler observations during an observation system simulation experiment: Part 1: Analysis Impact
2B (Washington State Convention Center)
Jason A. Otkin, CIMSS/Univ. of Wisconsin, Madison, WI; and D. C. Hartung, D. Turner, R. A. Petersen, W. F. Feltz, and E. Janzon

In this study, an Observation System Simulation Experiment (OSSE) was used to examine how the assimilation of surface-based remote-sensing profiler observations impacts the accuracy of atmospheric analyses at mesoscale resolution. Data from a high-resolution “truth” simulation was used to generate simulated temperature, water vapor, and wind profiles emulating observations from a potential array of Doppler wind lidar (DWL), Raman lidar (RL), microwave radiometer (MWR), and Atmospheric Emitted Radiance Interferometer (AERI) sensors located at existing WSR-88D radar locations. Assimilation experiments were conducted using the EnKF algorithm implemented in the DART data assimilation system. Seven assimilation experiments were conducted with various combinations of observations assimilated once per hour during a 24-hr period.

Overall, the results demonstrate that the assimilation of high-quality observations from an array of surface-based profiling systems has the potential to greatly improve the accuracy of atmospheric analyses used by numerical weather prediction models. The impact of each profiling system was greatest on the observed variables in the lower and middle troposphere, though some minor improvements also occurred in the unobserved variables and in the upper troposphere. The smallest temperature and moisture errors generally occurred when RL observations were assimilated, particularly in the upper troposphere where the errors were much less than the other cases. Comparison of the AERI and MWR cases shows that the AERI observations had a larger impact than the MWR observations on the temperature and moisture fields. DWL observations degraded the temperature and moisture analyses, but greatly improved the wind field in the lower and middle troposphere. The best analysis overall was achieved when both DWL wind observations and temperature and moisture observations from the RL, AERI, or MWR were assimilated simultaneously.