用边缘分布训练出更精准的联合天气预报，能捕捉空间结构且预测台风路径更准。
Skillful joint probabilistic weather forecasting from marginals
- 通过学习模型扰动生成集合，仅用单点数据训练却能建模联合分布。
- 在多种指标上超越现有最先进模型，对热带气旋路径预测有显著提升。
- 适合需要高精度概率天气预报的气象机构和气候研究者使用。
基于机器学习的天气模型因精度和速度优于传统的数值天气预报（NWP）而迅速崛起，近期在全局概率天气预报中已超越传统集合预报。本文提出FGN，一种简单、可扩展且灵活的建模方法，显著优于当前最先进模型。FGN通过学习模型扰动生成集合，采用受约束的模型集合进行预测，并直接以最小化各位置预报的连续排名概率分数（CRPS）为目标进行训练。该方法在多种确定性和概率性指标上达到最先进的集合预报效果，能做出有技巧的热带气旋路径预测，并且尽管仅基于边缘分布训练，仍能捕捉到联合空间结构。
原文摘要 · Abstract (English)
Machine learning (ML)-based weather models have rapidly risen to prominence due to their greater accuracy and speed than traditional forecasts based on numerical weather prediction (NWP), recently outperforming traditional ensembles in global probabilistic weather forecasting. This paper presents FGN, a simple, scalable and flexible modeling approach which significantly outperforms the current state-of-the-art models. FGN generates ensembles via learned model-perturbations with an ensemble of appropriately constrained models. It is trained directly to minimize the continuous rank probability score (CRPS) of per-location forecasts. It produces state-of-the-art ensemble forecasts as measured by a range of deterministic and probabilistic metrics, makes skillful ensemble tropical cyclone track predictions, and captures joint spatial structure despite being trained only on marginals.
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