Published 2015
| Reprint
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Data Assimilation: New Challenges in Random and Stochastic Dynamical Systems
- Creators
- Reich, Sebastian
-
Stuart, Andrew M.
Chicago
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Abstract
The seamless integration of large data sets into sophisticated computational models provides one of the central challenges for the mathematical sciences in the 21st century. When the computational model is based on dynamical systems, and the data set is time ordered, the process of combining models and data is called data assimilation. The assimilation of data into computational models serves a wide spectrum of purposes ranging from model calibration and model comparison, all the way to the validation of novel model design principles.
Additional Information
© 2015 SIAM. MS is grateful to EPSRC, ERC, and ONR for financial support that led to the research underpinning this article. SR acknowledges support under the DFG Collaborative Research Center SFB1114: Scaling Cascades in Complex Systems. The authors also thank Roland Potthast (Head of Data Assimilation, German Meteorological Service & Professor for Applied Mathematics, University of Reading) for providing the images in Figures 1 and 2. This article is based, in part, on a lecture delivered by Stuart at the 2015 SIAM Conference on Applications of Dynamical Systems, held in Snowbird, Utah.Attached Files
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Additional details
- Alternative title
- Applying Mathematics to Data Assimilation Methods
- Alternative title
- Data Assimilation in Numerical Weather Prediction
- Eprint ID
- 71940
- Resolver ID
- CaltechAUTHORS:20161111-104214792
- Engineering and Physical Sciences Research Council (EPSRC)
- European Research Council (ERC)
- Office of Naval Research (ONR)
- Deutsche Forschungsgemeinschaft (DFG)
- SFB1114
- Created
-
2016-11-15Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Other Numbering System Name
- Andrew Stuart
- Other Numbering System Identifier
- C21