Probabilistic modelling of atmosphere-surface coupling with a copula Bayesian network
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914038526509056 |
|---|---|
| author | Mack, Laura Kähnert, Marvin Pirk, Norbert |
| author_facet | Mack, Laura Kähnert, Marvin Pirk, Norbert |
| contents | Land-atmosphere coupling is an important process for correctly modelling near-surface temperature profiles, but it involves various uncertainties due to subgrid-scale processes, such as turbulent fluxes or unresolved surface heterogeneities, suggesting a probabilistic modelling approach. We develop a copula Bayesian network (CBN) to interpolate temperature profiles, acting as alternative to T2m-diagnostics used in numerical weather prediction (NWP) systems. The new CBN results in (1) a reduction of the warm bias inherent to NWP predictions of wintertime stable boundary layers allowing cold temperature extremes to be better represented, and (2) consideration of uncertainty associated with subgrid-scale spatial variability. The use of CBNs combines the advantages of uncertainty propagation inherent to Bayesian networks with the ability to model complex dependence structures between random variables through copulas. By combining insights from copula modelling and information entropy, criteria for the applicability of CBNs in the further development of parameterizations in NWP models are derived. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11975 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic modelling of atmosphere-surface coupling with a copula Bayesian network Mack, Laura Kähnert, Marvin Pirk, Norbert Atmospheric and Oceanic Physics Applications Land-atmosphere coupling is an important process for correctly modelling near-surface temperature profiles, but it involves various uncertainties due to subgrid-scale processes, such as turbulent fluxes or unresolved surface heterogeneities, suggesting a probabilistic modelling approach. We develop a copula Bayesian network (CBN) to interpolate temperature profiles, acting as alternative to T2m-diagnostics used in numerical weather prediction (NWP) systems. The new CBN results in (1) a reduction of the warm bias inherent to NWP predictions of wintertime stable boundary layers allowing cold temperature extremes to be better represented, and (2) consideration of uncertainty associated with subgrid-scale spatial variability. The use of CBNs combines the advantages of uncertainty propagation inherent to Bayesian networks with the ability to model complex dependence structures between random variables through copulas. By combining insights from copula modelling and information entropy, criteria for the applicability of CBNs in the further development of parameterizations in NWP models are derived. |
| title | Probabilistic modelling of atmosphere-surface coupling with a copula Bayesian network |
| topic | Atmospheric and Oceanic Physics Applications |
| url | https://arxiv.org/abs/2509.11975 |