This research aims to explore the observation impacts in coupled data assimilation (CDA) and improve the predictability of coupled systems by advanced DA approaches. Three topics are discussed in this dissertation: (1) An enhanced application of the correlation cutoff method (Yoshida and Kalnay, 2018) as a spatial localization is introduced. We investigated the feasibility and characteristics of the traditional distance-dependent (Gaspari and Cohn, 1999) and the correlation-dependent localizations preliminary on the Lorenz (1996) model with the local ensemble transform Kalman filter (LETKF). We further discussed the potential of integrative localization strategies and the application of the correlation cutoff method on Mars DA.
(2) We found that the surface sea temperature (SST) relaxation operationally used in the Climate Forecast System version 2 (CFSv2) is not effective in reducing existing SST biases. To address this issue, we replaced the SST relaxation with the weakly coupled data assimilation (WCDA) of satellite-retrieved SST products. A series of experiments with real observations were conducted on the CFSv2-LETKF (Sluka et al., 2018) to investigate the impacts of SST WCDA on the CFSv2 analysis and the forecasts.
(3) The Ensemble Forecast Sensitivity to Observations (EFSO, Kalnay et al., 2012) is a powerful tool to identify the beneficial or detrimental impact of every observation and has been widely used in atmospheric ensemble-based DA. However, EFSO has not yet been applied to any ocean or coupled DA due to the lack of a proper error norm for oceanic variables. This study first introduces a novel density-based error norm that simultaneously includes sea temperature and salinity forecast errors, by which EFSO becomes available to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2-LETKF for quantifying the individual impact of ocean observations and explored the great potential of EFSO to be extended as a data selection criterion to improve the CFSv2 forecasts.