Preprints
https://doi.org/10.5194/essd-2026-232
https://doi.org/10.5194/essd-2026-232
10 Apr 2026
 | 10 Apr 2026
Status: this preprint is currently under review for the journal ESSD.

OceanTACO: A Multi-Sensor Global Ocean Sea Surface State Dataset

Nils Lehmann, Cesar Aybar, Ando Shah, Marcello Passaro, Jonathan L. Bamber, and Xiao Xiang Zhu

Abstract. We present OceanTACO, a harmonised global collection of sea surface state datasets designed to support reproducible Earth system research. The collection integrates satellite altimetry, sea surface temperature, salinity, surface winds, reanalysis fields, and Argo in situ observations within a unified cloud-optimised specification based on Transparent Access to Cloud-optimised datasets (TACO). It includes Level-3 observations, Level-4 gap-filled products, and reanalysis outputs while preserving native spatial and temporal resolution. The core dataset spans 29 March 2023 to 1 August 2025, covering the Surface Water and Ocean Topography (SWOT) mission, with an extended record from 1 January 2015 until 29 March 2023 for non-SWOT sources.

Datasets are harmonised through standardised metadata, spatial referencing, and temporal indexing, enabling consistent spatiotemporal queries across sensors and processing levels. A uniform internal structure reduces product-specific preprocessing and allows the same data-access routines to be applied across regions, sensors, and studies. This supports Earth systems analyses workflows such as validation against in situ observations, comparisons between observation and mapped products, observation system experiments, and multivariate sensor analyses.

Example applications demonstrate cross-product collocation with Argo, analysis of sea surface height variability during extreme events, and relationships between surface variables relevant for data-driven reconstruction. OceanTACO improves accessibility to coordinated multi-source analyses while preserving data provenance and native observation characteristics, and can be extended with new missions without restructuring the dataset. The core and extended dataset are available at https://doi.org/10.57967/hf/8171 (Lehmann and Aybar, 2026a) and https://doi.org/10.57967/hf/8172 (Lehmann and Aybar, 2026b) respectively.

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Nils Lehmann, Cesar Aybar, Ando Shah, Marcello Passaro, Jonathan L. Bamber, and Xiao Xiang Zhu

Status: open (until 17 May 2026)

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Nils Lehmann, Cesar Aybar, Ando Shah, Marcello Passaro, Jonathan L. Bamber, and Xiao Xiang Zhu

Data sets

OceanTACO Core Dataset Nils Lehmann https://doi.org/10.57967/hf/8171

Model code and software

Data Generation Code Nils Lehmann and Cesar Aybar https://github.com/nilsleh/oceanTACO

Interactive computing environment

ReadTheDocs Documentation Page Nils Lehmann https://oceantaco.readthedocs.io/en/latest/index.html

Nils Lehmann, Cesar Aybar, Ando Shah, Marcello Passaro, Jonathan L. Bamber, and Xiao Xiang Zhu
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Latest update: 11 Apr 2026
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Short summary
We created a global ocean dataset that brings together satellite measurements, model outputs, and observations into one consistent system. We did this to reduce the time and effort needed to combine different data sources, to improve reproducibility, and enable new analyses. The result makes it easier to study ocean changes, compare methods, and support better understanding of climate processes and extreme events.
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