Gap-filled sub-surface mooring dataset off Western Australia during 2010–2023

Bui, Toan; Feng, Ming; Chapman, Christopher C.

doi:https://doi.org/10.5194/essd-17-1693-2025

Articles | Volume 17, issue 4

https://doi.org/10.5194/essd-17-1693-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-17-1693-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 4

Data description paper

|

25 Apr 2025

Data description paper |

| 25 Apr 2025

Gap-filled sub-surface mooring dataset off Western Australia during 2010–2023

Toan Bui, Ming Feng, and Christopher C. Chapman

Supplement

https://doi.org/10.5194/essd-17-1693-2025-supplement

Data sets

Gap-filled, gridded subsurface physical oceanography time series dataset derived from selected mooring measurements off the Western Australia coast during 2009-2023 T. Bui and M. Feng https://doi.org/10.25919/myac-yx60

An updated dataset for "A long-term, gridded, subsurface physical oceanography dataset and average annual cycles derived from in situ measurements off the Western Australia coast during 2009--2020" T. Bui et al. https://doi.org/10.25919/9gb1-ne81

Download

Article (3990 KB)
Full-text XML

Short summary

Moored time series data are crucial for detecting changes in the ocean. However, mooring losses or instrument failures often result in data gaps. A gap-filled time series dataset of a shelf mooring array off the Western Australian coast is created using a machine learning tool to fill the data gaps. The gap-filled data show consistency with observations and can be used to characterize marine heat waves and cold spells influenced by ocean boundary currents.