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Impacts of Assimilation Frequency on Ensemble Kalman Filter Data Assimilation and Imbalances
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2020
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Source: Journal of Advances in Modeling Earth Systems, 12(10)
[PDF-3.57 MB]
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Journal Title:Journal of Advances in Modeling Earth Systems
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Description:The ensemble Kalman filter (EnKF) has been widely used in atmosphere, ocean, and land applications. The observing network has been signi ficantly developed, and thus, observations with highly dense temporal resolutions have become available. To better extract information from dense temporalobservations, one straightforward strategy is to increase the assimilation frequency. However, more frequent assimilation may exacerbate the model imbalance and result in degraded forecasts. To combat the imbalance caused by ensemble ‐based data assimilation due to sampling error and covariance localization, three ‐and four ‐dimensional incremental analysis update (IAU) were proposed, which gradually introduce the analysis increments into model rather than intermittently updating the state. The trade ‐off
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Source:Journal of Advances in Modeling Earth Systems, 12(10)
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ISSN:1942-2466;1942-2466;
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Rights Information:CC BY-NC
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