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Activity Number:
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346
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #302517 |
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Title:
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Experimental Implementation of an Ensemble Adjustment Filter for an Intermediate ENSO Model
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Author(s):
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Alicia Karspeck*+ and Jeffrey Anderson
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Companies:
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National Center for Atmospheric Research and National Center for Atmospheric Research
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Address:
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1850 Table Mesa Dr., Louisville, CO, 80305,
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Keywords:
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data assimilation ; Kalman filter ; ENSO ; covariance localization
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Abstract:
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The assimilation of sea surface temperature (SST) anomalies into a coupled ocean-atmosphere model of the tropical Pacific is investigated using an ensemble adjustment Kalman filter (EAKF). The coupled numerical model used is routinely used for simulation and prediction of the El Nino Southern Oscillation. Operating under the "perfect model" experiment paradigm, we investigate how and why changes in the filter parameters (ensemble size, covariance localization, and covariance inflation) affect the quality of the analysis. It is shown that isotropic covariance localization does not benefit the analysis even when a small number of ensemble members are used. These results suggest that destruction of the dynamical balance between variables caused by localization is more detrimental than spurious correlation due to small ensemble size.
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