arXiv subjects

Tracy Hertneky

Publications and source records attributed to Tracy Hertneky.

2 recordsLinked to original sources

arXiv2026

AI-Based Regional Emulation for Kilometer-Scale Dynamical Downscaling

An AI-based Limited-Area Model (LAM) is developed for dynamical downscaling over the Southern Great Plains and the southeastern United States, with strong generalization abilities under diverse boundary conditions. The model is trained using 0.25-degree, 3-hourly ERA5 as forcings and CONUS404 as targets in 1980--2019, producing 4-km, hourly dynamical downscaling outputs; it is also connected to a post-processing model to derive additional diagnostic variables. The model is evaluated across multiple forcing datasets, time periods, and climate regimes. For present-day downscaling in the 2021--2024 water years, the model produces stable multi-year simulations with no unrealistic drift; its deterministic verification scores are comparable to other weather-forecasting-oriented AI models. The model also generalizes robustly to a 1.0-degree, 6-hourly non-ERA5 forcing dataset, yielding only minor performance changes. Frontal cyclone and hurricane case studies further demonstrate that the model reconstructs realistic, interpretable weather-scale dynamical and thermodynamic structure from coarse boundary information. The AI-based LAM is further tested by downscaling 30-year global climate model runs in 1980--2010 and 2070--2100, and climate model ensembles in 2025-2027. In this application, the model remains stable at hourly downscaling frequencies for all 30 years and effectively captures future climate-change signals, indicating meaningful generalization across different climate regimes. When downscaling ensembles, the model produces well-posed ensemble distributions without collapsing the ensemble spread. Overall, the AI-based LAM of this study offers good downscaling performance and generalization abilities. It provides a practical and transferable example of adapting AI weather prediction models for regional climate applications.

physics.ao-ph↗

arXiv2025

Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model

Recent advances in AI-based weather prediction have led to the development of artificial intelligence weather prediction (AIWP) models with competitive forecast skill compared to traditional NWP models, but with substantially reduced computational cost. There is a strong need for appropriate methods to evaluate their ability to predict extreme weather events, particularly when spatial coherence is important, and grid resolutions differ between models. We introduce a verification framework that combines spatial verification methods and weighted proper scoring rules. Specifically, the framework extends the High-Resolution Assessment (HiRA) approach with threshold-weighted scoring rules. It enables user-oriented evaluation consistent with how forecasts may be interpreted by operational meteorologists or used in simple post-processing systems. The method supports targeted evaluation of extreme events by allowing flexible weighting of the relative importance of different decision thresholds. We demonstrate this framework by evaluating 32 months of precipitation forecasts from an AIWP model and a high-resolution NWP model. Our results show that model rankings are sensitive to the choice of neighbourhood size. Increasing the neighbourhood size has a greater impact on scores evaluating extreme-event performance for the high-resolution NWP model than for the AIWP model. At near equivalent neighbourhood sizes, the empirical CDF of the high-resolution NWP model only outperformed the empirical CDF of the AIWP model in predicting extreme precipitation events at short lead times. We also demonstrate how this approach can be extended to evaluate discrimination ability in predicting heavy precipitation. We find that the high-resolution NWP model had superior discrimination ability at short lead times.

physics.ao-ph↗