8.5A Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance

Thursday, 14 January 2016: 9:30 AM
Room 345 ( New Orleans Ernest N. Morial Convention Center)
Feiyu Lu, University of Wisconsin, Madison, WI; and Z. Liu, S. Zhang, Y. Liu, and R. Jacob

A new leading averaged coupled covariance (LACC) method is proposed to improve the performance of the strongly coupled data assimilation (SCDA). The SCDA not only uses the coupled model to generate the forecast and assimilates observations into multiple model components like the weakly coupled version (WCDA), but also applies cross update using the coupled covariance between variables from different model components. The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice because of different time scales between model components. In a typical extra-tropical coupled system, the ocean–atmosphere correlation shows a strong asymmetry with the maximum correlation occurring when the atmosphere leads the ocean by about the decorrelation time of the atmosphere. The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore, increasing the coupled correlation and enhancing the signal-to-noise ratio in calculating the coupled covariance.

The LACC method is first tested in a simple coupled model with the ensemble Kalman filter (EnKF). With the LACC method, the SCDA reduces the analysis error of the oceanic variable by over 20% compared to the WCDA and 10% compared to the regular SCDA using simultaneous coupled covariance (SimCC). More sensitivity experiments show that the advantage of the LACC method is more notable when the system contains larger errors, such as in the cases with smaller ensemble size, bigger time-scale difference, or model biases.

The LACC method is then applied to a perfect-model SCDA system in a fully coupled general circulation model (CGCM). By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the error of SST analysis compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in the deep tropics. The improvement in SST analysis is a result of the enhanced signal-to-noise ratio from the LACC method, especially in the extra-tropical regions. The improved SST analysis also benefits the analyses of subsurface ocean temperature and low-level atmosphere temperature through dynamic and statistical processes.

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