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Original Research Papers

Impact of tropospheric and stratospheric data assimilation on mesospheric prediction

Authors:

Yulia Nezlin ,

Department of Physics, University of Toronto, 60 St. George Street, Toronto, Ontario, CA
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Yves J. Rochon,

Atmospheric Science and Technology Directorate, Environment Canada, Toronto, Ontario, CA
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Saroja Polavarapu

Atmospheric Science and Technology Directorate, Environment Canada, Toronto, Ontario, CA
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Abstract

Numerical experiments are used to assess the potential benefit of the assimilation of tropospheric and stratospheric observations on mesospheric prediction. A simulated atmosphere, taken as truth, is created using the Canadian Middle Atmosphere Model (CMAM). The truth is sampled at the locations of the measurements from the actual observing system to produce observations, which are then assimilated with the CMAM-DAS (Data Assimilation System). Obtained forecasts are compared with the truth, and error statistics are calculated. An assessment based on predictability shows that upward propagation of information resulting from the assimilation of tropospheric and stratospheric observations improves the mesosphere in the largest scales (with horizontal wavenumbers less than approximately 10). At the same time, the principal inability of the system to predict mesospheric small scales is demonstrated. Numerical experiments are used to assess the potential benefit of the assimilation of tropospheric and stratospheric observations on mesospheric prediction. A simulated atmosphere, taken as truth, is created using the Canadian Middle Atmosphere Model (CMAM). The truth is sampled at the locations of the measurements from the actual observing system to produce observations, which are then assimilated with the CMAM-DAS (Data Assimilation System). Obtained forecasts are compared with the truth, and error statistics are calculated. An assessment based on predictability shows that upward propagation of information resulting from the assimilation of tropospheric and stratospheric observations improves the mesosphere in the largest scales (with horizontal wavenumbers less than approximately 10). At the same time, the principal inability of the system to predict mesospheric small scales is demonstrated.

How to Cite: Nezlin, Y., Rochon, Y.J. and Polavarapu, S., 2009. Impact of tropospheric and stratospheric data assimilation on mesospheric prediction. Tellus A: Dynamic Meteorology and Oceanography, 61(1), pp.154–159. DOI: http://doi.org/10.1111/j.1600-0870.2007.00368.x
  Published on 01 Jan 2009
 Accepted on 10 Sep 2008            Submitted on 9 Apr 2008

References

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