Localized posterior inference may reduce predictive errors in ocean turbulence closure
The paper introduces a new calibration approach that constructs localized posterior distributions by weighting observation likelihoods according to their proximity to a target context. Gaussian localization weights incorporate context uncertainty through covariance inflation, with reflection at domain boundaries and an optional correction for uneven sampling. A conditional normalizing flow…
Key points
- Localized posterior inference uses Gaussian weights and a conditional normalizing flow for context‑aware calibration.
- Twin experiment with Branin‑Hoo model shows reduced predictive errors under skewed contexts.
In a twin experiment with the Branin‑Hoo model, the authors compare global and localized inference under uniform and skewed context distributions. Localization reduces the reported aggregate predictive errors relative to the global posterior, demonstrating the benefit of context‑aware calibration. The twin experiment demonstrates that even with skewed context distributions, localized inference consistently outperforms global methods.
Localized posterior calibration reduces temperature and zonal‑velocity errors compared with deterministic and global‑posterior calibration, illustrating its potential for improving physical model fidelity. These results suggest that incorporating context‑specific uncertainty can enhance the accuracy of climate and ocean models.
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