Articles | Volume 16, issue 13
https://doi.org/10.5194/acp-16-8193-2016
https://doi.org/10.5194/acp-16-8193-2016
Research article
 | 
07 Jul 2016
Research article |  | 07 Jul 2016

Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model

Douglas R. Allen, Karl W. Hoppel, and David D. Kuhl

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Subject: Dynamics | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Stratosphere | Science Focus: Physics (physical properties and processes)
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Cited articles

Allen, D. R., Hoppel, K. W., Nedoluha, G. E., Kuhl, D. D., Baker, N. L., Xu, L., and Rosmond, T. E.: Limitations of wind extraction from 4D-Var assimilation of ozone, Atmos. Chem. Phys., 13, 3501–3515, https://doi.org/10.5194/acp-13-3501-2013, 2013.
Allen, D. R., Hoppel, K. W., and Kuhl, D. D.: Wind extraction potential from 4D-Var assimilation of stratospheric O3, N2O, and H2O using a global shallow water model, Atmos. Chem. Phys., 14, 3347–3360, https://doi.org/10.5194/acp-14-3347-2014, 2014.
Allen, D. R., Hoppel, K. W., and Kuhl, D. D.: Wind extraction potential from ensemble Kalman filter assimilation of stratospheric ozone using a global shallow water model, Atmos. Chem. Phys., 15, 5835–5850, https://doi.org/10.5194/acp-15-5835-2015, 2015.
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, 59A, 210–224, https://doi.org/10.1111/j.1600-0870.2006.00216.x, 2007.
Andersson, E., Hólm, E., Bauer, P., Beljaars, A., Kelly, G. A., McNally, A. P., Simmons, A. J., Thépaut, J.-N., and Tompins, A. M.: Analysis and forecast impact of the main humidity observing systems, Q. J. Roy. Meteor. Soc., 133, 1473–1485, https://doi.org/10.1002/qj.112, 2007.
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Short summary
This study examines the extraction of wind information from assimilation of ozone observations in a simplified model of the atmosphere. Understanding this process is important for the stratosphere (10–50 km altitude), where wind observations are sparse. A key aspect is using multiple forecasts (ensembles) to determine the error correlations between ozone and wind. Experiments using different ensemble sizes show that ozone benefits winds more when ensemble information is blended into the system.
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