基于深度学习的大风订正预报研究-海洋预报

基于深度学习的大风订正预报研究

单位：1. 青岛市气象服务中心, 山东青岛 266003;
2. 青岛市黄岛区气象局, 山东青岛 266400;
3. 青岛天洋气象科技有限公司, 山东青岛 266400

分类号：P457.5

出版年·卷·期（页码）：2024·41·第六期（23-31）

摘要：

基于数值预报模式产品的风速预报集成学习误差订正方法，通过长短期记忆网络（LSTM）和残差神经网络（ResNet）构建新的风速预测混合模型 ResNet-LSTM。采用 2019—2020年欧洲中期天气预报中心39种数值天气预报模式产品训练深度学习模型，对格点预报产品插值到站点后的预报结果进行订正。结果表明：与ECMWF的原始预报相比，ResNet-LSTM模型在预测6级以上阵风时的 TS评分整体可以提高 50% 以上，预报精度提升。寒潮大风和台风大风的个例分析也表明，ResNet-LSTM可以有效解决大风漏报问题，对站点风速的预报订正效果显著。

A novel wind speed prediction model, the ResNet-LSTM model, is proposed combining the Long Short-Term Memory (LSTM) model and Residual Network (ResNet) model. By using 39 kinds of numerical weather forecasting products from the European Center for Medium Range Weather Forecasting (ECMWF), a deep learning model is trained to correct wind speed forecasts. The results show that compared with the ECMWF results, the TS score of the ResNet-LSTM model for gusts above level 6 has been increased by over 50%. Further analysis shows that the ResNet-LSTM model can effectively solve the fail report problem and improve wind speed forecasting corrections.

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