Ensemble Kalman filtering for assimilation of mesosphere and thermosphere observations

Details Ensemble Kalman filtering for assimilation of mesosphere and thermosphere observations Ensemble Kalman filtering for assimilation of mesosphere and thermosphere observations

Dec 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011agufmsh54a..08m&link_type=abstract

American Geophysical Union, Fall Meeting 2011, abstract #SH54A-08

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[3315] Atmospheric Processes / Data Assimilation, [3332] Atmospheric Processes / Mesospheric Dynamics, [6969] Radio Science / Remote Sensing, [7969] Space Weather / Satellite Drag

Scientific paper

We demonstrate the ability of ensemble Kalman filter (EnKF) to assimilate a realistic set of space-based observations of the upper atmosphere into general circulation models of the mesosphere, thermosphere and ionosphere. While the recent availability of global observations of ionospheric parameters, especially from GPS receivers on low Earth orbiting platforms, has motivated a number of attempts to assimilate ionospheric data, assimilation of sparse irregularly distributed mesosphere and thermosphere observations to global models remains to be a daunting task. EnKF assimilation systems have been constructed using the NCAR Data Assimilation Research Testbed with two different general circulation models: NCAR-TIEGCM and NCAR-ROSE. We present observing system simulation experiments to assess how well mesospheric and thermospheric model states can be constrained not only from in-situ observations (e.g., TIMED-SABER/TIDI and Champ neutral density) but also from the GPS-based ionospheric observations. Since these models are strongly controlled by model boundary conditions and other forcing parameters, it is important to account for model parameter estimation in the EnKF to reduce inconsistency between observations and model states. Furthermore, we discuss some of the challenges specific to upper atmospheric applications and the roles of auxiliary assimilation algorithms, such as adaptive covariance inflation and localization of covariance, in the EnKF to cope with these challenges.

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