The impact of hybrid oceanic data assimilation in a coupled model: A case study of a tropical cyclone

Abstract

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 subsurface 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.

Marine data assimilation in the UK: the past, the present and the vision for the future

“…This started with studying various aspects of strongly coupled DA algorithms in simplified coupled models (Smith et al, 2015, 2018, 2020, Fowler and Lawless, 2016. Recently there has been collaborations between the University of Reading and the Met Office under the WCSSP-India programme to understand the impact of coupled atmosphere-ocean DA on tropical cyclone prediction in the Bay of Bengal and how to improve the operational coupled DA approach (Leung et al, 2022). There has also been work on understanding the nature of atmosphere-ocean error covariances from the Met Office coupled ensemble (Wright et al, 2024).…”

“…Such future DTO systems should also further optimize the way they integrate deterministic models with ML/AI and datasources across a variety of platforms and scales. , Skakala et al, 2024, Leung et al, 2022Wright et al, 2024, Dong et al, 2021.…”