[论文解读] The rise of data-driven weather forecasting

Zied Ben-Bouallegue, Mariana Clare+15 more|arXiv (Cornell University)|Jul 19, 2023

Meteorological Phenomena and Simulations被引用 43

一句话总结

论文评估数据驱动的、基于ML的天气预报在接近操作的设置中与传统NWP预报（ECMWF IFS）的比较，显示出具竞争力的技能并识别当前ML的缺点。

ABSTRACT

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the 'quiet revolution' of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable skill for both global metrics and extreme events, when verified against both the operational analysis and synoptic observations. Increasing forecast smoothness and bias drift with forecast lead time are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

研究动机与目标

• 将数据驱动建模作为潜在加速天气预报、超越传统NWP的动力。
• 在相同初始条件下，使用高质量重分析数据训练的ML-based forecasts对比领先的全球NWP系统。
• 在全球指标和极端事件方面分析ML预报的优点与局限。
• 强调一种新范式，其中ML推断与最先进的分析驱动预报初始化与训练。

提出的方法

• 将ML生成的预报（PanguWeather）与以相同初始条件初始化的ECMWF IFS预报进行比较。
• 使用标准预报验证工具对确定性预报进行验证。
• 使用来自高质量重分析（ERA5）的训练数据进行ML模型开发。
• 评估相对于操作分析与同化观测的表现。
• 识别如前瞻时间的预测平滑性和偏差漂移等问题。

实验结果

研究问题

• RQ1 当从相同条件初始化时，数据驱动的ML预测相对于领先的NWP系统的表现如何？
• RQ2 在全球指标和极端事件方面，ML预测相对于NWP预报的技能有何比较？
• RQ3 ML基于天气预报当前的缺点（如平滑度、偏差漂移）有哪些？
• RQ4 在重分析数据训练的ML模型是否能在显著降低计算成本的前提下实现有竞争力的准确性？
• RQ5 将ML推理与最先进的分析结合起来用于初始化和训练，能产生何种预测范式？

主要发现

• 当与操作分析和synoptic observations对比时，ML预报在全球指标和极端事件上的技能与领先的NWP预测相当。
• ML模型产生的预报可以在较低的计算成本下达到有竞争力的准确性。
• 已识别的缺点包括在ML基于预测中，随前瞻时间的增加，预测的平滑性增强和偏差漂移。
• 结果展示了依赖ML推断与高质量重分析数据用于初始化和训练的新NWP范式的潜力。
• 该研究首次在接近操作的情境下直接比较了ML预测与全球NWP系统。