Evaluation of nonidentical versus identical twin approaches for observation impact assessments: an ensemble-Kalman-filter-based ocean assimilation application for the Gulf of Mexico

Liuqian Yu, Katja Fennel, Bin Wang, Arnaud Laurent, Keith R. Thompson, Lynn K. Shay

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1801-1814
Number of pages14
JournalOcean Science
Volume15
Issue number6
DOIs
StatePublished - Dec 20 2019

ASJC Scopus subject areas

  • Oceanography
  • Palaeontology

Fingerprint Dive into the research topics of 'Evaluation of nonidentical versus identical twin approaches for observation impact assessments: an ensemble-Kalman-filter-based ocean assimilation application for the Gulf of Mexico'. Together they form a unique fingerprint.

Cite this