Probabilistic inference of surface parameters for Monin-Obukhov similarity theory

[2608.21549] Probabilistic inference of surface parameters for Monin-Obukhov similarity theory Skip to main content Search Submit Donate Log in Physics > Atmospheric and Oceanic Physics arXiv:2608.21549 (physics) [Submitted on 21 Aug 2026 ( v1 ), last revised 26 Aug 2026 (this version, v2)] Title: Probabilistic inference of surface parameters for Monin-Obukhov similarity theory Authors: Ethan YoungIn Shin , Michael F. Howland View PDF HTML (experimental) Abstract: In simulations of atmospheric flow, the grid spacing typically exceeds the size of the roughness elements at the surface by an order of magnitude. The unresolved effects of surface morphology and roughness on the flow are represented by effective surface parameters and specified as a surface flux boundary condition, most often through a formulation based on Monin-Obukhov similarity theory (MOST). These surface parameters are known to depend on both surface and flow properties, yet they are generally estimated as deterministic quantities with no characterization of associated uncertainty. In this study, we use a Bayesian approach to infer surface parameters for MOST and quantify their uncertainties. For the aerodynamic roughness length $z_0$ inferred in isolation, a normal-normal conjugate update yields the posterior and posterior predictive distributions in closed form. We first demonstrate our method on idealized conventionally neutral boundary layers generated by large-eddy simulation, where $z_0$ is prescribed, and quantify how prior- and observation-related choices shape the inferred posterior. We then apply the method to field observations from the Atmospheric Radiation Measurement Southern Great Plains observatory, from which we infer $z_0$ distributions conditioned on month, on wind direction, and on both jointly. By leveraging a prior informed by sample statistics of all near-neutral observations in the training years, we demonstrate the advantage of the Bayesian inference method for $z_0$ relative to the state-of-practice least-squares profile-fitting method in conditions of data sparsity. Predictions for unseen observations-evaluated at the posterior mean-reduce root mean squared error and mean absolute error in data-sparse wind directions, while posterior predictive distributions consistently reduce the continuous ranked probability score by approximately $20$-$30\%$. Comments: 19 pages, 9 figures, 1 table Subjects: Atmospheric and Oceanic Physics (physics.ao-ph) ; Data Analysis, Statistics and Probability (physics.data-an) MSC classes: 86 ACM classes: J.2.5; J.2.8 Cite as: arXiv:2608.21549 [physics.ao-ph] (or arXiv:2608.21549v2 [physics.ao-ph] for this version) https://doi.org/10.48550/arXiv.2608.21549 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ethan YoungIn Shin [ view email ] [v1] Fri, 21 Aug 2026 18:37:44 UTC (8,650 KB) [v2] Wed, 26 Aug 2026 21:12:55 UTC (8,650 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: physics.ao-ph < prev | next > new | recent | 2026-08 Change to browse by: physics physics.data-an References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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