测试AI模型对飓风初始条件扰动的鲁棒性，发现其轨迹稳定但强度预测偏保守。
Robustness Test for AI Forecasting of Hurricane Florence Using FourCastNetv2 and Random Perturbations of the Initial Condition
- 用高斯噪声和随机初值测试FourCastNetv2对飓风预报的敏感性
- 低中度噪声下轨迹和结构保持良好，高强度噪声时位置精度下降
- 模型对极端天气预报有稳定输出倾向，适合评估其他AI气象模型
理解气象预报模型在输入噪声或不确定性下的鲁棒性，对评估极端天气事件（如飓风）预测可靠性至关重要。本文测试了NVIDIA的AI气象模型FourCastNetv2（FCNv2）对初始条件扰动的敏感性和鲁棒性。实验基于欧洲中期天气预报中心（ECMWF）再分析数据集ERA5（2018年9月13-16日）中的飓风佛罗伦萨初始场，分别注入不同强度的高斯噪声，考察对路径与强度预测的影响；其次，使用完全随机的初始条件启动模型，观察其对非物理输入的响应。结果表明，FCNv2在低至中等噪声下能准确保留飓风特征，即使在高噪声下仍维持整体路径与结构，仅位置精度下降。模型在所有噪声水平下均持续低估风暴强度与持续时间。在完全随机初始条件下，模型经数个时间步后生成平滑且连贯的预报，显示其趋向于稳定、平滑输出的特性。该方法简单可移植，适用于其他数据驱动的AI气象预报模型。
原文摘要 · Abstract (English)
Understanding the robustness of a weather forecasting model with respect to input noise or different uncertainties is important in assessing its output reliability, particularly for extreme weather events like hurricanes. In this paper, we test sensitivity and robustness of an artificial intelligence (AI) weather forecasting model: NVIDIAs FourCastNetv2 (FCNv2). We conduct two experiments designed to assess model output under different levels of injected noise in the models initial condition. First, we perturb the initial condition of Hurricane Florence from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset (September 13-16, 2018) with varying amounts of Gaussian noise and examine the impact on predicted trajectories and forecasted storm intensity. Second, we start FCNv2 with fully random initial conditions and observe how the model responds to nonsensical inputs. Our results indicate that FCNv2 accurately preserves hurricane features under low to moderate noise injection. Even under high levels of noise, the model maintains the general storm trajectory and structure, although positional accuracy begins to degrade. FCNv2 consistently underestimates storm intensity and persistence across all levels of injected noise. With full random initial conditions, the model generates smooth and cohesive forecasts after a few timesteps, implying the models tendency towards stable, smoothed outputs. Our approach is simple and portable to other data-driven AI weather forecasting models.
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