[论文解读] Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers

Zied Ben-Bouallegue, Jonathan A. Weyn+4 more|arXiv (Cornell University)|Mar 30, 2023

Meteorological Phenomena and Simulations被引用 9

一句话总结

PoET 使用分层变换器对集合预测进行后处理，以校准每个成员，在全球范围内将2m温度预报技能提升约20%，降水提升约2%，超越MBM基准并在跨模型周期与数据集上展现出强泛化。

ABSTRACT

Statistical post-processing of global ensemble weather forecasts is revisited by leveraging recent developments in machine learning. Verification of past forecasts is exploited to learn systematic deficiencies of numerical weather predictions in order to boost post-processed forecast performance. Here, we introduce PoET, a post-processing approach based on hierarchical transformers. PoET has 2 major characteristics: 1) the post-processing is applied directly to the ensemble members rather than to a predictive distribution or a functional of it, and 2) the method is ensemble-size agnostic in the sense that the number of ensemble members in training and inference mode can differ. The PoET output is a set of calibrated members that has the same size as the original ensemble but with improved reliability. Performance assessments show that PoET can bring up to 20% improvement in skill globally for 2m temperature and 2% for precipitation forecasts and outperforms the simpler statistical member-by-member method, used here as a competitive benchmark. PoET is also applied to the ENS10 benchmark dataset for ensemble post-processing and provides better results when compared to other deep learning solutions that are evaluated for most parameters. Furthermore, because each ensemble member is calibrated separately, downstream applications should directly benefit from the improvement made on the ensemble forecast with post-processing.

研究动机与目标

• 动机：在NWP集合中纠正系统性预报误差的统计后处理。
• 开发一种基于变换器的后处理方法，直接作用于集合成员。
• 确保该方法对集合规模无关，且可扩展到不同的空间分辨率。
• 将PoET与MBM和ENS-10进行基准比较，以评估各变量上的相对性能。

提出的方法

• 引入PoET，一种基于分层变换器的后处理方法。
• 沿集合维度应用变换器对每个集合成员进行重新校准。
• 将PoET嵌入到U-net架构中，以管理内存并支持高分辨率全球网格。
• 使用带层归一化的残差变换器块来纠正预报误差。
• 在ECMWF再预报和再分析（ERA5）上进行训练，预报时效最高至96小时；在2021年运营数据上进行测试。
• 将PoET与MBM进行比较，并评估在2m温度和降水方面的性能；对损失函数进行调整（降水采用kCRPS）。

Figure 1: Schematic of the transformer U-net architecture of PoET. The inset on the lower right is adapted from Finn ( 2021 ) , used with permission.

实验结果

研究问题

• RQ1 PoET是否能够在相对于原始集合的情况下显著提升全球2m温度和降水预报的可靠性和技能？
• RQ2 在到达96小时的预报时效内，PoET与MBM统计后处理方法的比较如何？
• RQ3 PoET是否对集合规模无关，当训练与推理集合大小不同时仍能保持性能？
• RQ4 PoET是否能跨不同模型周期以及ENS-10基准数据集泛化？
• RQ5 PoET改进的空间分布模式是什么，是否存在区域性退化？

主要发现

• PoET在全球层面使2m温度的技能提升高达约20%，降水提升约2%，相对于原始集合。
• 在2m温度方面，PoET在不同预报时效和地区通常超过MBM。
• 在ENS-10上，PoET对先前基线有显著改进，特别是在2m温度方面。
• 对降水，增益较小且区域性变化，热带地区表现更好。
• PoET可以在训练和推理之间使用不同的集合规模，验证了对集合规模的无关性。
• 通过PoET分别校准每个集合成员可以更好地与ERA5对齐，并支持下游应用。

Figure 2: Relative changes by PoET to a single date of the 2m temperature forecast at day 2, valid on 6 January 2021. The left column shows the first 3 raw ensemble members. The first row shows the first 3 PoET-corrected ensemble members. Other entries show the differences between raw and PoET-corre