Articles | Volume 16, issue 6
https://doi.org/10.5194/acp-16-3927-2016
https://doi.org/10.5194/acp-16-3927-2016
Research article
 | 
23 Mar 2016
Research article |  | 23 Mar 2016

Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting

Juli I. Rubin, Jeffrey S. Reid, James A. Hansen, Jeffrey L. Anderson, Nancy Collins, Timothy J. Hoar, Timothy Hogan, Peng Lynch, Justin McLay, Carolyn A. Reynolds, Walter R. Sessions, Douglas L. Westphal, and Jianglong Zhang

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

Adhikary, B., Kulkarni, S., Dallura, A., Tang, Y., Chai, T., Leung, L.R., Qian, Y., Chung, C. E., Ramanathan, V., and Carmichael, G. R.: A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique, Atmos. Environ., 42, 8600–8615, https://doi.org/10.1016/j.atmosenv.2008.08.031, 2008.
Anderson, J. L.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, 1996.
Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2894–2903, 2001.
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus, 59A, 210–224, 2007.
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filters, Tellus, 61A, 72–83, 2009.
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Short summary
This work tests the use of an ensemble prediction system for aerosol forecasting, including an ensemble adjustment Kalman filter for MODIS AOT assimilation. Key findings include (1) meteorology and source-perturbed ensembles are needed to capture long-range transport and near-source aerosol events, (2) adaptive covariance inflation is recommended for assimilating spatially heterogeneous observations and (3) the ensemble system captures sharp gradients relative to a deterministic/variational system.
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