[论文解读] Evaluation of Tropical Cyclone Track and Intensity Forecasts from Artificial Intelligence Weather Prediction (AIWP) Models

Mark DeMaria, James L. Franklin+5 more|arXiv (Cornell University)|Sep 8, 2024

Tropical and Extratropical Cyclones Research被引用 4

一句话总结

本研究采用国家飓风中心（NHC）验证标准，评估了四种开源AI天气预测模型——FourCastNetv1、FourCastNetv2-small、GraphCast-operational和Pangu-Weather——在热带气旋（TC）路径与强度预测方面的表现。尽管AIWP模型在路径预测精度上可与顶尖业务模型相媲美，但其在强度预测方面存在显著偏差，尤其在前24小时内系统性地低估风暴强度，限制了其在未经偏差校正的情况下实际应用的价值。

ABSTRACT

In just the past few years multiple data-driven Artificial Intelligence Weather Prediction (AIWP) models have been developed, with new versions appearing almost monthly. Given this rapid development, the applicability of these models to operational forecasting has yet to be adequately explored and documented. To assess their utility for operational tropical cyclone (TC) forecasting, the NHC verification procedure is used to evaluate seven-day track and intensity predictions for northern hemisphere TCs from May-November 2023. Four open-source AIWP models are considered (FourCastNetv1, FourCastNetv2-small, GraphCast-operational and Pangu-Weather). The AIWP track forecast errors and detection rates are comparable to those from the best-performing operational forecast models. However, the AIWP intensity forecast errors are larger than those of even the simplest intensity forecasts based on climatology and persistence. The AIWP models almost always reduce the TC intensity, especially within the first 24 h of the forecast, resulting in a substantial low bias. The contribution of the AIWP models to the NHC model consensus was also evaluated. The consensus track errors are reduced by up to 11% at the longer time periods. The five-day NHC official track forecasts have improved by about 2% per year since 2001, so this represents more than a five-year gain in accuracy. Despite substantial negative intensity biases, the AIWP models have a neutral impact on the intensity consensus. These results show that the current formulation of the AIWP models have promise for operational TC track forecasts, but improved bias corrections or model reformulations will be needed for accurate intensity forecasts.

研究动机与目标

• 评估新兴AI天气预测（AIWP）模型在热带气旋（TC）预测中的业务应用潜力。
• 基于NHC验证标准，评估AIWP模型在TC路径与强度预测方面的表现。
• 确定AIWP模型对NHC官方预报共识的影响，尤其在路径与强度预测方面。
• 识别主要局限性，特别是强度预测方面的不足，并提出改进方案以实现业务集成。

提出的方法

• 本研究采用NHC验证流程，评估2023年5月至11月北半球热带气旋的七天路径与强度预测。
• 使用历史大气与海洋再分析数据作为输入，对四种开源AIWP模型——FourCastNetv1、FourCastNetv2-small、GraphCast-operational和Pangu-Weather——进行评估。
• 计算并比较路径预测误差与检测率，与业务模型及气候-持久性（C-P）基线进行对比。
• 量化并分析强度预测误差中的偏差，尤其关注预测前24小时内的表现。
• 通过测量共识预报误差随时间的变化，评估AIWP模型对NHC模型共识的贡献。
• 通过分析预报值与观测强度趋势的偏离，隐式评估偏差校正技术的效果。

实验结果

研究问题

• RQ1 AIWP模型在七天预测期内，与业务模型相比，在预测热带气旋路径与强度方面表现如何？
• RQ2 AIWP模型在强度预测中存在多大的偏差？其偏差在时间上的演变特征如何，特别是在前24小时内？
• RQ3 AIWP模型在多大程度上改善了NHC官方预报共识，尤其是在路径预测方面？
• RQ4 为何AIWP模型尽管路径预测表现优异，却持续低估热带气旋强度？
• RQ5 当前AIWP模型的构型是否可在无需显著偏差校正的情况下实现业务集成？

主要发现

• AIWP模型在路径预测误差方面与最佳业务模型相当，且在较长预报时效下，共识路径误差最高可降低11%。
• 自2001年以来，五日NHC官方路径预报准确率每年约提升2%，而AIWP模型的贡献相当于路径准确率超过五年的进步。
• AIWP模型表现出显著的负向强度偏差，尤其在前24小时内系统性低估风暴强度，导致其误差甚至超过简单的气候-持久性（C-P）预报。
• 尽管存在显著强度偏差，AIWP模型对NHC强度共识的影响为中性，表明其误差未对共识输出造成负面影响。
• 研究结论认为，尽管AIWP模型在路径预测方面展现出巨大潜力，但若要实现强度预测的业务可用性，仍需改进偏差校正或重构模型架构。
• 结果表明，当前AIWP模型构型尚不适合在未经显著后处理或架构优化的情况下直接用于强度预测。