Assessing the performance of data assimilation algorithms which employ linear error feedback

Mallia-Parfitt, N. and Bröcker, J. (2016) Assessing the performance of data assimilation algorithms which employ linear error feedback. Chaos, 26 (10). 103109. ISSN 1089-7682 doi: 10.1063/1.4965029

Abstract/Summary

Data assimilation means to and an (approximate) trajectory of a dynamical model that (approximately) matches a given set of observations. A direct evaluation of the trajectory against the available observations is likely to yield a too optimistic view of performance, since the observations were already used to find the solution. A possible remedy is presented which simply consists of estimating that optimism, thereby giving a more realistic picture of the `out of sample' performance. Our approach is inspired by methods from statistical learning employed for model selection and assessment purposes in statistics. Applying similar ideas to data assimilation algorithms yields an operationally viable means of assessment. The approach can be used to improve the performance of models or the data assimilation itself. This is illustrated by optimising the feedback gain for data assimilation employing linear feedback.

Additional Information	(accepted)
Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/67574
Identification Number/DOI	10.1063/1.4965029
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Additional Information	(accepted)
Publisher	American Institute of Physics
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