arXiv subjects

Ethan YoungIn Shin

Publications and source records attributed to Ethan YoungIn Shin.

2 recordsLinked to original sources

arXiv2026

Connecting the forward problem to the inverse problem in uncertainty quantification of Earth system models using fast emulators

Quantifying and reducing uncertainty in Earth system model parameterizations is essential to improving their reliability in decision-making. Forward uncertainty propagation is used to derive parameter sensitivity but requires physically plausible parameter distributions first be learned from observations. Bayesian inference offers a principled approach but can become ill-posed when observations weakly constrain parameters--a condition difficult to know prior to inference. Addressing this gap, we show that parameter sensitivity results from forward uncertainty quantification can guide a non-iterative strategy for identifying observations informative to Bayesian calibration. We explore both forward and inverse uncertainty quantification for parameterizations of atmospheric turbulence in the Weather Research and Forecasting (WRF) model. To overcome the computational bottleneck of $\mathcal{O}(10^5)$ model evaluations required for both analyses, we leverage Gaussian process emulators trained on several hundred WRF simulations. Using these emulators, we conduct a global sensitivity analysis across observation space, investigating how parameter contributions to output variance depend on quantity of interest, atmospheric stability, time-averaging length, and spatial location. We then introduce nondimensional diagnostic measures that systematically identify regions where a parameter's contribution to output variance exceeds observational noise and its independent effect exceeds interaction effects. We demonstrate that observations from these regions serve as a strong proxy for accurate Bayesian calibration and reduced posterior uncertainty. Through emulator-aided Bayesian inversion with synthetic observations, we show how parameter uncertainty can be systematically reduced by leveraging sensitivity information.

physics.ao-ph↗

arXiv2026

Probabilistic inference of surface parameters for Monin-Obukhov similarity theory

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 $20$-$30\%$.

physics.ao-ph↗