Ocean Science (Dec 2024)

Assessing the impact of future altimeter constellations in the Met Office global ocean forecasting system

  • R. R. King,
  • M. J. Martin,
  • L. Gaultier,
  • J. Waters,
  • C. Ubelmann,
  • C. Donlon

DOI
https://doi.org/10.5194/os-20-1657-2024
Journal volume & issue
Vol. 20
pp. 1657 – 1676

Abstract

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Satellite altimeter measurements of sea surface height (SSH) are a crucial component of current operational ocean forecasting systems. The launch of the Surface Water and Ocean Topography (SWOT) wide-swath altimeter (WiSA) mission is bringing a step change in our observing capacity with 2D mesoscale structures now able to be observed over the global ocean. Proposals are now being considered for the make-up of the future altimeter constellation. In this study we use Observing System Simulation Experiments (OSSEs) to compare the impact of additional altimeter observations from two proposed future satellite constellations. We focus on the expected impact on the Met Office operational ocean analysis and forecasting system of assimilating an observation network including either 12 nadir altimeters or 2 wide-swath altimeters. Here we show that an altimeter constellation of 12 nadir altimeters produces greater reductions in the errors for SSH, surface currents, temperature, and salinity fields compared to a constellation of 2 wide-swath altimeters. The impact is greatest in the dynamic western boundary current (WBC) regions where the nadir altimeters can reduce the SSH RMS (root-mean-square) error by half, while the wide-swath altimeter only reduces this by one-quarter. A comparison of the spatial scales resolved in daily SSH fields also highlights the superiority of the nadir constellation in our forecasting system. We also highlight the detrimental impact spatially correlated errors could have on the immediate use of wide-swath altimeter observations. However, we still achieve promising impacts from the assimilation of wide-swath altimetry, and work is ongoing to develop improved methods to account for spatially correlated observation errors within our data assimilation scheme.