arXiv subjects

James Doss-Gollin

Publications and source records attributed to James Doss-Gollin.

3 recordsLinked to original sources

arXiv2025

Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion

Understanding the risks posed by extreme rainfall events requires analysis of precipitation fields with high resolution (to assess localized hazards) and extensive historical coverage (to capture sufficient examples of rare occurrences). Radar and mesonet networks provide precipitation fields at 1 km resolution but with limited historical and geographical coverage, while gauge-based records and reanalysis products cover decades of time on a global scale, but only at 30-50 km resolution. To help provide high-resolution precipitation estimates over long time scales, this study presents Wasserstein Regularized Diffusion (WassDiff), a diffusion framework to downscale (super-resolve) precipitation fields from low-resolution gauge and reanalysis products. Crucially, unlike related deep generative models, WassDiff integrates a Wasserstein distribution-matching regularizer to the denoising process to reduce empirical biases at extreme intensities. Comprehensive evaluations demonstrate that WassDiff quantitatively outperforms existing state-of-the-art generative downscaling methods at recovering extreme weather phenomena such as tropical storms and cold fronts. Case studies further qualitatively demonstrate WassDiff's ability to reproduce realistic fine-scale weather structures and accurate peak intensities. By unlocking decades of high-resolution rainfall information from globally available coarse records, WassDiff offers a practical pathway toward more accurate flood-risk assessments and climate-adaptation planning.

cs.LG↗

arXiv2025

Bayesian Spatiotemporal Nonstationary Model Quantifies Robust Increases in Daily Extreme Rainfall Across the Western Gulf Coast

Precipitation exceedance probabilities are widely used in engineering design, risk assessment, and floodplain management. While common approaches like NOAA Atlas 14 assume that extreme precipitation characteristics are stationary over time, this assumption may underestimate current and future hazards due to anthropogenic climate change. However, the incorporation of nonstationarity in the statistical modeling of extreme precipitation has faced practical challenges that have restricted its applications. In particular, random sampling variability challenges the reliable estimation of trends and parameters, especially when observational records are limited. To address this methodological gap, we propose the Spatially Varying Covariates Model, a hierarchical Bayesian spatial framework that integrates nonstationarity and regionalization for robust frequency analysis of extreme precipitation. This model draws from extreme value theory, spatial statistics, and Bayesian statistics, and is validated through cross-validation and multiple performance metrics. Applying this framework to a case study of daily rainfall in the Western Gulf Coast, we identify robustly increasing trends in extreme precipitation intensity and variability throughout the study area, with notable spatial heterogeneity. This flexible model accommodates stations with varying observation records, yields smooth return level estimates, and can be straightforwardly adapted to the analysis of precipitation frequencies at different durations and for other regions.

stat.AP↗

arXiv2024

Bayesian Functional Graphical Models with Change-Point Detection

Functional data analysis, which models data as realizations of random functions over a continuum, has emerged as a useful tool for time series data. Often, the goal is to infer the dynamic connections (or time-varying conditional dependencies) among multiple functions or time series. For this task, a dynamic and Bayesian functional graphical model is introduced. The proposed modeling approach prioritizes the careful definition of an appropriate graph to identify both time-invariant and time-varying connectivity patterns. A novel block-structured sparsity prior is paired with a finite basis expansion, which together yield effective shrinkage and graph selection with efficient computations via a Gibbs sampling algorithm. Crucially, the model includes (one or more) graph changepoints, which are learned jointly with all model parameters and incorporate graph dynamics. Simulation studies demonstrate excellent graph selection capabilities, with significant improvements over competing methods. The proposed approach is applied to study of dynamic connectivity patterns of sea surface temperatures in the Pacific Ocean and reveals meaningful edges.

stat.ME↗