arXiv subjects

Yun Am Seo

Publications and source records attributed to Yun Am Seo.

3 recordsLinked to original sources

arXiv2026

Early warning of road icing from antecedent meteorological conditions without consistent critical slowing down

Critical slowing down (CSD), the slowing of recovery from perturbations as a system approaches a critical transition, is widely used for early warning, but whether such signals appear in environmental transitions subject to rapidly changing external forcing has rarely been tested with observations. Here we show, using 2,427 icing-onset events in 1-min observations from 121 South Korean road weather stations, that such signals do not consistently precede road icing. Icing began from different prior surface states, but conditions at onset varied continuously with weak separation among pathways. Cold dose, deposition dose and freeze-thaw accumulation at onset exceeded those at non-icing control times, with cold-dose discrimination highest at decay timescales of 30 min or less. However, the increases in variance and lag-1 autocorrelation expected under CSD did not distinguish icing from control times; the same procedure recovered these increases in synthetic series with a declining recovery rate, and the null result was replicated for 3,091 events at 127 independent stations. Predictive information was nevertheless present: adding recent changes and cumulative antecedent conditions to current conditions improved discrimination, and surrounding meteorological fields increased the area under the ROC curve by 0.030-0.032 for forecast windows of 1 to 12 h and reduced false alarms by 62-67% at 3 and 6 h. These results suggest that road icing is more consistent with recent meteorological forcing moving the road surface into icing-favourable conditions than with a progressive decline in internal stability, and they indicate where early warning should look when a transition is externally forced.

stat.AP↗

arXiv2026

Quantum mutual information statistics for detecting dependence-structure change points in time series

Detecting when the dependence between two components of a multivariate time series changes, while the marginals drift freely, requires a dependence-specific statistic. We take the inferential object to be a density operator -- the trace-normalised second moment of unit-norm random Fourier features of ranks -- rather than a probability distribution. Partial traces recover the marginal operators exactly, so von Neumann entropies yield a quantum mutual information (QMI) statistic computed from prefix sums of small matrices, without density estimation, matrix inversion, or a tuned parameter. We develop the inference it needs: a segment-separable cost that drives penalised optimal partitioning, its split gain a Holevo information; finite-sample exact calibration by joint pair permutation, a block-permutation form for serially dependent series, and an exact, provably consistent exchangeability diagnostic that selects between them. We also prove a weighted chi-square boundary law, at the segment length and not its square root, for the rank-based statistic exactly as computed. In 500 replicates QMI detects nonlinear, correlation-free dependence changes with more power than the Hilbert-Schmidt independence criterion, distance correlation, Spearman, and empirical-copula statistics on the same ranks, by at least 15 percentage points wherever any statistic detects the change. Its false-alarm rate stays near nominal under marginal drift, where the empirical-copula statistic reaches 0.87. On eight years of hourly Korean weather observations, a two-stage segment-and-certify procedure finds dependence-change candidates above chance (five of 27 at p $\le$ 0.05 against 1.4 expected); stage two certifies one as a pure coupling change and reclassifies eight as marginal-driven.

stat.ME↗

arXiv2020

Iterative Method for Tuning Complex Simulation Code

Tuning a complex simulation code refers to the process of improving the agreement of a code calculation with respect to a set of experimental data by adjusting parameters implemented in the code. This process belongs to the class of inverse problems or model calibration. For this problem, the approximated nonlinear least squares (ANLS) method based on a Gaussian process (GP) metamodel has been employed by some researchers. A potential drawback of the ANLS method is that the metamodel is built only once and not updated thereafter. To address this difficulty, we propose an iterative algorithm in this study. In the proposed algorithm, the parameters of the simulation code and GP metamodel are alternatively re-estimated and updated by maximum likelihood estimation and the ANLS method. This algorithm uses both computer and experimental data repeatedly until convergence. A study using toy-models including inexact computer code with bias terms reveals that the proposed algorithm performs better than the ANLS method and the conditional-likelihood-based approach. Finally, an application to a nuclear fusion simulation code is illustrated.

stat.CO↗