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Assimilation of GRACE-derived oceanic mass distributions with a global ocean circulation model

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/persons/resource/saynisch

Saynisch,  J.
1.3 Earth System Modelling, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Publikationen aller GRACE-unterstützten Projekte, Deutsches GeoForschungsZentrum;

/persons/resource/ingab

Bergmann-Wolf,  I.
1.3 Earth System Modelling, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Publikationen aller GRACE-unterstützten Projekte, Deutsches GeoForschungsZentrum;

/persons/resource/mthomas

Thomas,  M.
1.3 Earth System Modelling, 1.0 Geodesy and Remote Sensing, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;
Publikationen aller GRACE-unterstützten Projekte, Deutsches GeoForschungsZentrum;

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676894.pdf
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Zitation

Saynisch, J., Bergmann-Wolf, I., Thomas, M. (2015): Assimilation of GRACE-derived oceanic mass distributions with a global ocean circulation model. - Journal of Geodesy, 89, 2, 121-139.
https://doi.org/10.1007/s00190-014-0766-0


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_676894
Zusammenfassung
To study the sub-seasonal distribution and generation of ocean mass anomalies, Gravity Recovery and Climate Experiment (GRACE) observations of daily and monthly resolution are assimilated into a global ocean circulation model with an ensemble-based Kalman-Filter technique. The satellite gravimetry observations are processed to become time-variable fields of ocean mass distribution. Error budgets for the observations and the ocean model’s initial state are estimated which contain the full covariance information. The consistency of the presented approach is demonstrated by increased agreement between GRACE observations and the ocean model. Furthermore, the simulations are compared with independent observations from 54 bottom pressure recorders. The assimilation improves the agreement to high-latitude recorders by up to 2 hPa. The improvements are caused by assimilation-induced changes in the atmospheric wind forcing, i.e., quantities not directly observed by GRACE. Finally, the use of the developed Kalman-Filter approach as a destriping filter to remove artificial noise contaminating the GRACE observations is presented.