ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

feed icon rss

Ihre E-Mail wurde erfolgreich gesendet. Bitte prüfen Sie Ihren Maileingang.

Leider ist ein Fehler beim E-Mail-Versand aufgetreten. Bitte versuchen Sie es erneut.

Vorgang fortführen?

Exportieren
  • 1
    facet.materialart.
    Unbekannt
    In:  EPIC3AGU Joint Meeting, Acapulco, Mexico, May 22-25, 2007 p.
    Publikationsdatum: 2019-07-17
    Beschreibung: 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
    Materialart: Conference , notRev
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 2
    facet.materialart.
    Unbekannt
    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.
    Publikationsdatum: 2019-07-17
    Repository-Name: EPIC Alfred Wegener Institut
    Materialart: Conference , notRev
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 3
    facet.materialart.
    Unbekannt
    In:  EPIC3Ocean Sciences Meeting, Orlando, FL, USA, March, pp. 3-7
    Publikationsdatum: 2019-07-17
    Beschreibung: 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
    Materialart: Conference , notRev
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 4
    facet.materialart.
    Unbekannt
    In:  EPIC3Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, USA, March 10, 2008 p.
    Publikationsdatum: 2019-07-17
    Beschreibung: 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
    Materialart: Conference , notRev
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 5
    facet.materialart.
    Unbekannt
    In:  EPIC3Ocean Sciences Meeting, February 20-24, 2006, Honolulu, USA.
    Publikationsdatum: 2019-07-17
    Repository-Name: EPIC Alfred Wegener Institut
    Materialart: Conference , notRev
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 6
    facet.materialart.
    Unbekannt
    In:  EPIC3AGU Fall Meeting, San Francisco, CA, USA, December 11-15, 2006 p.
    Publikationsdatum: 2019-07-17
    Beschreibung: 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
    Materialart: Conference , notRev
    Format: application/pdf
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 7
    facet.materialart.
    Unbekannt
    In:  EPIC3Journal of marine systems, 68, pp. 237-254
    Publikationsdatum: 2019-07-17
    Repository-Name: EPIC Alfred Wegener Institut
    Materialart: Article , isiRev
    Format: application/pdf
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 8
    facet.materialart.
    Unbekannt
    In:  EPIC3NASA MAP Science Team Meeting, Adelphi, MD, USA, March 7-9, 2007 p.
    Publikationsdatum: 2019-07-17
    Repository-Name: EPIC Alfred Wegener Institut
    Materialart: Conference , notRev
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 9
    facet.materialart.
    Unbekannt
    In:  EPIC3Workshop ``Data Assimilation in Support of Coastal Ocean Observing Systems'', Oregon State University, Corvallis, OR, April 3--5, 2007 p.
    Publikationsdatum: 2019-07-17
    Beschreibung: 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
    Materialart: Conference , notRev
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
  • 10
    facet.materialart.
    Unbekannt
    In:  EPIC3Journal of marine systems, 73(2008), pp. 87-102
    Publikationsdatum: 2019-07-17
    Repository-Name: EPIC Alfred Wegener Institut
    Materialart: Article , isiRev
    Format: application/pdf
    Standort Signatur Erwartet Verfügbarkeit
    BibTip Andere fanden auch interessant ...
Schließen ⊗
Diese Webseite nutzt Cookies und das Analyse-Tool Matomo. Weitere Informationen finden Sie hier...