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Estimating forecast error covariances for strongly coupled atmosphere-ocean 4D-Var data assimilation

Smith, P. J., Lawless, A. S. and Nichols, N. K. (2017) Estimating forecast error covariances for strongly coupled atmosphere-ocean 4D-Var data assimilation. Monthly Weather Review, 145 (10). pp. 4011-4035. ISSN 0027-0644

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To link to this item DOI: 10.1175/MWR-D-16-0284.1

Abstract/Summary

Strongly coupled data assimilation emulates the real world pairing of the atmosphere and ocean by solving the assimilation problem in terms of a single combined atmosphere-ocean state. A significant challenge in strongly coupled variational atmosphere-ocean data assimilation is a priori specification of the cross-covariances between the errors in the atmosphere and ocean model forecasts. These covariances must capture the correct physical structure of interactions across the air-sea interface as well as the different scales of evolution in the atmosphere and ocean; if prescribed correctly, they will allow observations in one medium to improve the analysis in the other. Here we investigate the nature and structure of atmosphere-ocean forecast error cross-correlations using an idealised strongly coupled single-column atmosphere-ocean 4D-Var assimilation system. We present results from a set of identical twin experiments that use an ensemble of coupled 4D-Var assimilations to derive estimates of the atmosphere-ocean error cross-correlations. Our results show significant variation in the strength and structure of cross-correlations in the atmosphere-ocean boundary layer between summer and winter and between day and night. These differences provide a valuable insight into the nature of coupled atmosphere-ocean correlations for different seasons and points in the diurnal cycle.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > National Centre for Earth Observation (NCEO)
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:71324
Publisher:American Meteorological Society

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