Talea L. Mayo | Searcharxiv

arXiv2026

Kilometer-Scale AI Downscaling of Atlantic Hurricanes with Generative Ensembles

This study presents an AI-based dynamical downscaling system for Tropical Cyclones (TCs). The system incorporates an AI-based limited-area model that downscales 3-hourly low-resolution boundary forcings into hourly high-resolution fields autoregressively, and a diffusion model that converts the outputs into ensembles of hazard-relevant variables. The system is trained on the regridded CONUS404 data with ERA5 forcings, and is evaluated on 20 TCs in 2020--2024. Verification shows stable downscaling performance across Atlantic hurricane seasons, with energy spectra closely matching the CONUS404 reference. The system is also verified to produce skillful TC-relevant weather extremes, largely improved over a deterministic AI baseline. The system performs well with forcing data from other models (GDAS/FNL) and can produce detailed eyewall, rainband, and landfall structures in TC case studies. The study provides a good example of how AI-based dynamical downscaling systems can be designed to resolve small-scale extreme weather events.

physics.ao-ph↗

arXiv2026

The Ice Sheet State and Parameter Estimator (ICESEE) Library (v1.0.0): Ensemble Kalman Filtering for Ice Sheet Models

ICESEE (ICE Sheet statE and parameter Estimator) is a Python-based, open-source data assimilation framework designed for seamless integration with ice sheet and Earth system models. It implements a parallel Ensemble Kalman Filter (EnKF) with full MPI support for scalable assimilation in state and parameter spaces. ICESEE uses a matrix-free update scheme from Evensen (2003), which avoids explicit forecast error covariance construction and eliminates the need for localization in high-dimensional, nonlinear systems. ICESEE also supports four EnKF variants, including a localized version for methodological testing. It enables indirect inference of unobserved model parameters through a hybrid assimilation-inversion strategy. The framework features modular coupling interfaces, adaptive state indexing, and efficient parallel I/O, making it extensible to a variety of modeling environments. ICESEE has been successfully coupled with ISSM, Icepack, and other models. In this study, we focus on applications with ISSM and Icepack, demonstrating ICESEE's interoperability, performance, scalability, and ability to improve state estimates and infer uncertain parameters. Performance benchmarks show strong and weak scaling, highlighting ICESEE's potential for large-scale, observation-constrained ice sheet reanalyses.

cs.CC↗