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- Title
Evaluation of nonidentical versus identical twin approaches for observation impact assessments: an ensemble-Kalman-filter-based ocean assimilation application for the Gulf of Mexico.
- Authors
Yu, Liuqian; Fennel, Katja; Wang, Bin; Laurent, Arnaud; Thompson, Keith R.; Shay, Lynn K.
- Abstract
Assessments of ocean data assimilation (DA) systems and observing system design experiments typically rely on identical or nonidentical twin experiments. The identical twin approach has been recognized as yielding biased impact assessments in atmospheric predictions, but these shortcomings are not sufficiently appreciated for oceanic DA applications. Here we present the first direct comparison of the nonidentical and identical twin approaches in an ocean DA application. We assess the assimilation impact for both approaches in a DA system for the Gulf of Mexico that uses the ensemble Kalman filter. Our comparisons show that, despite a reasonable error growth rate in both approaches, the identical twin produces a biased skill assessment, overestimating the improvement from assimilating sea surface height and sea surface temperature observations while underestimating the value of assimilating temperature and salinity profiles. Such biases can lead to an undervaluation of some observing assets (in this case profilers) and thus a misguided distribution of observing system investments.
- Subjects
GULF of Mexico; KALMAN filtering; OCEAN temperature; OCEAN; ERROR rates
- Publication
Ocean Science, 2019, Vol 15, Issue 6, p1801
- ISSN
1812-0784
- Publication type
Article
- DOI
10.5194/os-15-1801-2019