The authors present an end-to-end locally stationary Gaussian process framework for Argo-based ocean heat content mapping that incorporates time trends, estimates decorrelation scales from data, and uses local conditional simulation for correlated uncertainty quantification validated by cross-valida
Zammit-Mangion, R
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Locally stationary Argo ocean heat content estimates: Modeling, validation and uncertainty quantification
The authors present an end-to-end locally stationary Gaussian process framework for Argo-based ocean heat content mapping that incorporates time trends, estimates decorrelation scales from data, and uses local conditional simulation for correlated uncertainty quantification validated by cross-valida