[论文解读] A Deep-learning Real-time Bias Correction Method for Significant Wave Height Forecasts in the Western North Pacific

Wei Zhang, Yu Sun+6 more|arXiv (Cornell University)|Nov 25, 2023

Hydrological Forecasting Using AI参考文献 42被引用 5

一句话总结

本研究提出一种基于轨迹门控循环单元的时空深度学习模型，用于实时校正西太平洋区域欧洲中期天气预报中心-集成预报系统（ECMWF-IFS）的显著波高（SWH）预报。该方法结合波浪场与风场输入，并采用一种新型像素切换损失函数，使春季的平均绝对误差降低最高达46.237%，冬季降低最高达38.953%，在不同季节和天气条件下均表现出强鲁棒性。

ABSTRACT

Significant wave height is one of the most important parameters characterizing ocean waves, and accurate numerical ocean wave forecasting is crucial for coastal protection and shipping. However, due to the randomness and nonlinearity of the wind fields that generate ocean waves and the complex interaction between wave and wind fields, current forecasts of numerical ocean waves have biases. In this study, a spatiotemporal deep-learning method was employed to correct gridded SWH forecasts from the ECMWF-IFS. This method was built on the trajectory gated recurrent unit deep neural network,and it conducts real-time rolling correction for the 0-240h SWH forecasts from ECMWF-IFS. The correction model is co-driven by wave and wind fields, providing better results than those based on wave fields alone. A novel pixel-switch loss function was developed. The pixel-switch loss function can dynamically fine-tune the pre-trained correction model, focusing on pixels with large biases in SWH forecasts. According to the seasonal characteristics of SWH, four correction models were constructed separately, for spring, summer, autumn, and winter. The experimental results show that, compared with the original ECMWF SWH predictions, the correction was most effective in spring, when the mean absolute error decreased by 12.972~46.237%. Although winter had the worst performance, the mean absolute error decreased by 13.794~38.953%. The corrected results improved the original ECMWF SWH forecasts under both normal and extreme weather conditions, indicating that our SWH correction model is robust and generalizable.

研究动机与目标

• 解决由非线性风场和波浪-风相互作用引起的数值显著波高（SWH）预报中持续存在的偏差问题。
• 开发一种实时、时空深度学习模型，用于校正西太平洋区域ECMWF-IFS的网格化SWH预报。
• 通过数据驱动的偏差校正方法，在正常与极端天气条件下提升预报精度。
• 通过构建春季、夏季、秋季和冬季的季节性专用校正模型，提升模型泛化能力。

提出的方法

• 采用轨迹门控循环单元（tGRU）深度神经网络，以建模SWH与风场数据中的时空依赖性。
• 校正模型由波浪场与风场共同驱动，性能优于仅使用波浪场的模型。
• 提出一种新型像素切换损失函数，动态聚焦于偏差较大的像素，实现针对性的误差降低。
• 为每个季节分别训练四个独立的校正模型，以应对波浪气候与预报误差模式的季节性差异。
• 模型可对0–240小时预报实施实时滚动校正，支持业务化部署。
• 利用像素切换损失对预训练模型进行微调，以适应不断演变的偏差分布。

实验结果

研究问题

• RQ1 深度学习模型能否有效降低ECMWF-IFS在西太平洋区域显著波高预报中的系统性偏差？
• RQ2 与仅使用波浪场的模型相比，同时引入风场信息对SWH偏差校正精度有何影响？
• RQ3 像素切换损失函数在高偏差预报像素上的校正性能提升程度如何？
• RQ4 波浪气候的季节性变化如何影响校正模型的泛化能力与性能表现？
• RQ5 所提出的校正方法在极端天气条件及正常条件下是否均具备鲁棒性？

主要发现

• 在春季，校正模型使平均绝对误差降低12.972%至46.237%，为偏差校正效果最佳的季节。
• 即使在表现最差的冬季，平均绝对误差也降低了13.794%至38.953%。
• 模型在正常与极端天气条件下均表现出强鲁棒性与良好泛化能力。
• 将风场信息引入模型后，校正精度优于仅使用波浪场的基线模型。
• 像素切换损失函数有效聚焦并降低了高偏差预报像素的误差，提升了模型精度。
• 季节性专用模型优于单一通用模型，证实了在偏差校正中进行季节性适配的重要性。