Feature-Relative Deployment Optimisation of Ice-Tethered Buoy Arrays in the Southern Ocean
Abstract
The Southern Ocean marginal ice zone is critical for climate dynamics and sea-ice model validation, yet remains severely undersampled due to costly deployment and short buoy lifetimes. The sea-ice edge shifts by hundreds of kilometres between years, so fixed-coordinate optimisation fails when future ice conditions are unknown. This paper presents a robust multi-objective framework that encodes buoy positions relative to the dynamic ice-edge feature, so one genotype adapts to inter-annual variability. NSGA-II optimises spatial coverage and ship-operating cost. Training uses 39 years of Polar Pathfinder data (1979--2017). Blind validation covers seven held-out years (2018--2024), and cross-product transfer uses two independent drift products (DPGSIV AMSR2 and OSI-405-c). The feature-relative strategy retains 99.4\% coverage on unseen years and outperforms fixed-coordinate, grid, random and historical fan baselines at lower cost. It transfers to alternative products, giving a quantified decision-support tool for pre-cruise planning.
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BibTeX
@misc{hepworth2026featurerelative,
title = {{Feature-Relative Deployment Optimisation of Ice-Tethered Buoy Arrays in the Southern Ocean}},
author = {James Hepworth and Edward Boje and Amit Kumar Mishra},
howpublished = {EarthArXiv},
year = {2026},
month = oct,
doi = {10.31223/x5x81x},
url = {https://eartharxiv.org/repository/view/15492/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Feature-Relative Deployment Optimisation of Ice-Tethered Buoy Arrays in the Southern Ocean
AU - James Hepworth
AU - Edward Boje
AU - Amit Kumar Mishra
PY - 2026
DA - 2026/10/14
PB - EarthArXiv
DO - 10.31223/x5x81x
UR - https://eartharxiv.org/repository/view/15492/
AB - The Southern Ocean marginal ice zone is critical for climate dynamics and sea-ice model validation, yet remains severely undersampled due to costly deployment and short buoy lifetimes. The sea-ice edge shifts by hundreds of kilometres between years, so fixed-coordinate optimisation fails when future ice conditions are unknown. This paper presents a robust multi-objective framework that encodes buoy positions relative to the dynamic ice-edge feature, so one genotype adapts to inter-annual variability. NSGA-II optimises spatial coverage and ship-operating cost. Training uses 39 years of Polar Pathfinder data (1979--2017). Blind validation covers seven held-out years (2018--2024), and cross-product transfer uses two independent drift products (DPGSIV AMSR2 and OSI-405-c). The feature-relative strategy retains 99.4\% coverage on unseen years and outperforms fixed-coordinate, grid, random and historical fan baselines at lower cost. It transfers to alternative products, giving a quantified decision-support tool for pre-cruise planning.
ER -