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This is a Quantitative Study study.

October 13, 2025Open Access

Probabilistic modelling of atmosphere-surface coupling with a copula Bayesian network

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The copula Bayesian network reduces wintertime warm bias in temperature predictions, improving extreme temperature accuracy.
CBN effectively represents uncertainties in subgrid-scale processes affecting land-atmosphere interactions.
By modeling dependence structures, CBN enhances interpretation of complex temperature profiles beyond traditional methods.
Criteria derived for CBN application offer insights for advancing numerical weather prediction parameterizations.

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Implication

This research develops a copula Bayesian network to improve temperature profiling in numerical weather prediction, addressing uncertainties in land-atmosphere coupling.

Cite This Study

Mack et al. (2025) studied this question.

synapsesocial.com/papers/68ed1896f29694dd1da78a8d https://doi.org/10.48550/arxiv.2509.11975

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Implication

This research develops a copula Bayesian network to improve temperature profiling in numerical weather prediction, addressing uncertainties in land-atmosphere coupling.

Probabilistic modelling of atmosphere-surface coupling with a copula Bayesian network

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Implication

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Probabilistic modelling of atmosphere-surface coupling with a copula Bayesian network

Key Points

Discussion

Implication

Cite This Study

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