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  • 1
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    Assimilation of satellite ocean chlorophyll data for biogeochemical state estimation - univariate and multivariate aspects (2007)
    In:  EPIC3AGU Joint Meeting, Acapulco, Mexico, May 22-25, 2007 p.
    Details
    Publication Date: 2019-07-17
    Description: Chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) is assimilated into the three-dimensional global NASA OceanBiogeochemical Model (NOBM) for the period 1998-2004. Theensemble-based SEIK filter is applied in a multivariateconfiguration. It is used here with a localized analysis andsimplified by the use of a constant covariance matrix. In addition, anonline bias estimation algorithm is applied. The multivariateassimilation updates the four phytoplankton groups of the model aswell as nutrient fields. With assimilation, the chlorophyll estimatesbecome superior to both the free-run model and SeaWiFS data. However,the results are less clear for the nutrients. We discuss the behaviorand issues involved by the multivariate assimilation process.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 2
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    Assimilation of Satellite Ocean Chlorophyll Data with Bias Correction for Biogeochemical State Estimation (2007)
    In:  EPIC3Young Scientists Workshop "Data assimilation, dealing with uncertainties, and the prediction capabilities of models in water research" of the DFG -Senate Commission on Water Research (KoWa), October 8-11, Lueneburg, Germany.
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    Publication Date: 2019-07-17
    Repository Name: EPIC Alfred Wegener Institut
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  • 3
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    Estimation of Model Bias by the Assimilation of Satellite Ocean Chlorophyll Data into a Global Model (2008)
    In:  EPIC3Ocean Sciences Meeting, Orlando, FL, USA, March, pp. 3-7
    Details
    Publication Date: 2019-07-17
    Description: Chlorophyll concentration estimates by ocean-biogeochemical models showtypically significant errors. Data assimilation algorithms based onthe Kalman filter can be applied to improve the model state. However,these algorithms do usually not account for possible biases in themodel prediction. Taking model bias explicitly into account canimprove the assimilation estimates.Here, the effect of bias estimation is studied with the assimilationof chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) into the NASA Ocean Biogeochemical Model (NOBM). Theensemble-based SEIK filter has been combined with an online biascorrection scheme. A static error covariance matrix is used for simplicity. The performance of the filter algorithm is assessed by comparisonwith independent in situ data over the 7-year period 1998--2004.Compared to the assimilation without bias estimation, the biascorrection results in significant improvements of the surfacechlorophyll. With bias estimation, the daily surface chlorophyllestimates from the assimilation show about 3.3\% lower error thanSeaWiFS data. In contrast, the error in the global surfacechlorophyll estimate without bias estimation is 10.9\%. Next to theimprovement of the estimated chlorophyll concentrations, the estimatedbiases indicate regions with systemic errors in the model-represenedchlorophyll.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 4
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    Improving Assimilation of SeaWiFS Data by the Application of Bias Correction with a Local SEIK Filter (2008)
    In:  EPIC3Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, USA, March 10, 2008 p.
    Details
    Publication Date: 2019-07-17
    Description: hlorophyll concentration estimates by ocean-biogeochemical models showtypically significant errors. Data assimilation algorithms based onthe Kalman filter can be applied to improve the model state. However,these algorithms do usually not account for possible biases in themodel prediction. Taking model bias explicitly into account canimprove the assimilation estimates.Here, the effect of bias estimation is studied with the assimilationof chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) into the NASA Ocean Biogeochemical Model (NOBM). Theensemble-based SEIK filter has been combined with an online biascorrection scheme. A static error covariance matrix is used for simplicity. The performance of the filter algorithm is assessed by comparisonwith independent in situ data over the 7-year period 1998--2004.Compared to the assimilation without bias estimation, the biascorrection results in significant improvements of the surfacechlorophyll. With bias estimation, the daily surface chlorophyllestimates from the assimilation show about 3.3\% lower error thanSeaWiFS data. In contrast, the error in the global surfacechlorophyll estimate without bias estimation is 10.9\%.
    Repository Name: EPIC Alfred Wegener Institut
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  • 5
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    Constraining a global three-dimensional ocean biogeochemical model by SeaWiFS ocean Chlorophyll data using a local SEIK Filter (2006)
    In:  EPIC3Ocean Sciences Meeting, February 20-24, 2006, Honolulu, USA.
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    Publication Date: 2019-07-17
    Repository Name: EPIC Alfred Wegener Institut
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  • 6
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    Multivariate Assimilation of Satellite Ocean Chlorophyll Data Into a Global Model - Prospects and Challenges (2006)
    In:  EPIC3AGU Fall Meeting, San Francisco, CA, USA, December 11-15, 2006 p.
    Details
    Publication Date: 2019-07-17
    Description: Chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) is assimilated into the three-dimensional global NASA OceanBiogeochemical Model (NOBM) for the period 1998-2004 using amultivariate configuration of the SEIK filter which is anensemble Kalman filter scheme. The SEIK filter is applied here with alocalized analysis and simplified by the use of a constant covariancematrix. The multivariate assimilation is applied to update thefour phytoplankton groups of the model as well as the simulatednutrient fields. While the chlorophyll estimates of the model can besignificantly improved by the assimilation, the results are less clearfor the nutrients. We discuss the behavior of the multivariateassimilation process and the challenges involved by it.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 7
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    Assimilation of SeaWiFS data into a global ocean-biogeochemical model using a local SEIK Filter (2007)
    In:  EPIC3Journal of marine systems, 68, pp. 237-254
    Details
    Publication Date: 2019-07-17
    Repository Name: EPIC Alfred Wegener Institut
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  • 8
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    Multivariate Assimilation of Satellite Ocean Chlorophyll Data into a Global Model (2007)
    In:  EPIC3NASA MAP Science Team Meeting, Adelphi, MD, USA, March 7-9, 2007 p.
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    Publication Date: 2019-07-17
    Repository Name: EPIC Alfred Wegener Institut
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  • 9
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    State estimation in an ocean-biogeochemical model by assimilation of satellite ocean chlorophyll data (2007)
    In:  EPIC3Workshop ``Data Assimilation in Support of Coastal Ocean Observing Systems'', Oregon State University, Corvallis, OR, April 3--5, 2007 p.
    Details
    Publication Date: 2019-07-17
    Description: Chlorophyll data from the Sea-viewing Wide Field-of-view Sensor(SeaWiFS) is assimilated into the three-dimensional global NASA OceanBiogeochemical Model (NOBM) for the period 1998-2004. The assimilationis performed by a multivariate configuration of the SEIK filter whichis an ensemble-based Kalman filter scheme. The filter is simplified bythe use of a static error covariance matrix. It operates with alocalized analysis and is amended by an online bias correction scheme.The multivariate assimilation is applied to update the fourphytoplankton groups of the model as well as the simulated nutrientfields. The chlorophyll estimates of the model can be improved by theassimilation such that they outperform the assimilated SeaWiFS data.However, the results are less clear for the nutrients where the biasestimation is required for stability but reduces the assimilationimprovements.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 10
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    Improving Assimilation of SeaWiFS Data by the Application of Bias Correction with a Local SEIK Filter (2008)
    In:  EPIC3Journal of marine systems, 73(2008), pp. 87-102
    Details
    Publication Date: 2019-07-17
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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