Significant wave height (SWH) 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 European Centre for Medium-Range Weather Forecasting System Integrated Forecast System Global Model (ECMWF-IFS). This method was built on the trajectory gated recurrent unit deep neural network, and it conducts real-time rolling correction for the 0–240-h 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.

中文翻译：

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西北太平洋有效波高预报的深度学习实时偏差修正方法

有义波高（SWH）是表征海浪的最重要参数之一，准确的海浪数值预报对于海岸保护和航运至关重要。然而，由于产生海浪的风场的随机性和非线性以及波浪与风场之间复杂的相互作用，目前对海浪的数值预报存在偏差。在这项研究中，采用时空深度学习方法来修正欧洲中期天气预报中心综合预报系统全球模型（ECMWF-IFS）的网格化SWH预报。该方法建立在轨迹门控循环单元深度神经网络的基础上，对ECMWF-IFS的0-240小时SWH预测进行实时滚动修正。该修正模型由波浪场和风场共同驱动，比单独基于波浪场的修正模型提供了更好的结果。开发了一种新颖的像素开关损失函数。像素开关损失函数可以动态微调预训练的校正模型，重点关注 SWH 预测中偏差较大的像素。根据SWH的季节特征，分别构建了春、夏、秋、冬4个校正模型。实验结果表明，与原来的ECMWF SWH预测相比，春季修正最为有效，平均绝对误差下降了12.972%~46.237%。尽管冬季表现最差，但平均绝对误差下降了 13.794-38.953%。校正结果改善了正常和极端天气条件下原始 ECMWF SWH 预测，表明我们的 SWH 校正模型具有鲁棒性和通用性。