Assimilation of Surface-Based Boundary Layer Profiler Observations during a Cool-Season Weather Event Using an Observing System Simulation Experiment. Part I: Analysis Impact

Jason A. Otkin Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Daniel C. Hartung Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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David D. Turner NOAA/National Severe Storms Laboratory, Norman, Oklahoma, and Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

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Ralph A. Petersen Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Wayne F. Feltz Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Erik Janzon Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

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Abstract

In this study, an Observing System Simulation Experiment was used to examine how the assimilation of temperature, water vapor, and wind profiles from a potential array of ground-based remote sensing boundary layer profiling instruments impacts the accuracy of atmospheric analyses when using an ensemble Kalman filter data assimilation system. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). The case study tracked the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results demonstrate that using networks of high-quality temperature, wind, and moisture profile observations of the lower troposphere has the potential to improve the accuracy of wintertime atmospheric analyses over land. The impact of each profiling system was greatest in the lower and middle troposphere on the variables observed or retrieved by that instrument; however, some minor improvements also occurred in the unobserved variables and in the upper troposphere, particularly when RAM observations were assimilated. The best analysis overall was achieved when DWL wind profiles and temperature and moisture observations from the RAM, AERI, or MWR were assimilated simultaneously, which illustrates that both mass and momentum observations are necessary to improve the analysis accuracy.

Corresponding author address: Jason A. Otkin, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. E-mail: jason.otkin@ssec.wisc.edu

Abstract

In this study, an Observing System Simulation Experiment was used to examine how the assimilation of temperature, water vapor, and wind profiles from a potential array of ground-based remote sensing boundary layer profiling instruments impacts the accuracy of atmospheric analyses when using an ensemble Kalman filter data assimilation system. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). The case study tracked the evolution of several extratropical weather systems that occurred across the contiguous United States during 7–8 January 2008. Overall, the results demonstrate that using networks of high-quality temperature, wind, and moisture profile observations of the lower troposphere has the potential to improve the accuracy of wintertime atmospheric analyses over land. The impact of each profiling system was greatest in the lower and middle troposphere on the variables observed or retrieved by that instrument; however, some minor improvements also occurred in the unobserved variables and in the upper troposphere, particularly when RAM observations were assimilated. The best analysis overall was achieved when DWL wind profiles and temperature and moisture observations from the RAM, AERI, or MWR were assimilated simultaneously, which illustrates that both mass and momentum observations are necessary to improve the analysis accuracy.

Corresponding author address: Jason A. Otkin, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. E-mail: jason.otkin@ssec.wisc.edu
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