Abstract/Summary

Tropical cyclones tend to result in distinctive spatial and temporal characteristics in the upper ocean, which suggests that traditional, parametrisation-based background-error covariances in oceanic data assimilation (DA) may not be suitable. Using the case study of Cyclone Titli, which affected the Bay of Bengal in October 2018, we explore hybrid methods that combine the traditional covariance modelling strategy used in variational methods with flow-dependent estimates of the ocean's error covariance structures based on a short-range ensemble forecast. This hybrid approach is investigated in the UK Met Office’s state-of-the-art system. Single-observation experiments in the ocean reveal that the hybrid approach is capable of producing analysis increments that are time-varying, more anisotropic and vertically less uniform. When the hybrid oceanic covariances are incorporated into a weakly coupled DA system, the sea-surface temperature (SST) in the path of the cyclone is changed, not only through the different specifications of background-error covariances used in the SST assimilation, but also through the propagation of sub-surface temperature differences to the surface as a result of vertical mixing associated with the cyclone's strong winds. The coupling with the atmosphere then leads to a discrepancy in the cyclone's central pressure, which brings forth further SST differences due to the different representations of the cyclone's emerging cold wake.