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New method calibrates ocean models with uncertainty quantification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for calibrating single-column ocean models using simulation-based inference (SBI). This approach addresses the limitation of previous methods by quantifying the uncertainty associated with parameter estimates, which is crucial when inverse problems are ill-posed. The study applied SBI to the JAX-based `tunax` ocean model to calibrate coefficients for its k-epsilon closure, utilizing a blockwise principal component analysis to compress simulator output and make inference tractable. AI

IMPACT This research introduces a novel simulation-based inference technique that could improve the accuracy and reliability of climate and oceanographic models.

RANK_REASON The cluster contains an academic paper detailing a new simulation-based inference method for calibrating ocean models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method calibrates ocean models with uncertainty quantification

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The cluster contains an academic paper detailing a new simulation-based inference method for calibrating ocean models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Luben M. C. Cabezas, Sacha Wendling, Aur\`ele Gallard, Gabriel Mouttapa, Julien Le Sommer, Pedro L. C. Rodrigues ·

    Calibrating subgrid parametrizations of single-column ocean models via simulation-based inference

    arXiv:2609.13242v1 Announce Type: cross Abstract: Subgrid parametrizations of vertical mixing in ocean models depend on free coefficients that cannot be measured directly and must be calibrated against high-fidelity references such as large-eddy simulations (LES). Existing approa…

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