Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter |
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Authors: | Lars Nerger Watson W Gregg |
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Affiliation: | aGlobal Modeling and Assimilation Office, NASA/Goddard Space Flight Center, Greenbelt, Maryland, United States;bGoddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, United States |
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Abstract: | Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data. |
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Keywords: | Data assimilation Ecosystem modeling Kalman filter SEIK Bias correction Ocean color Ocean chlorophyll |
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