Storm surges can give rise to extreme floods in coastal areas. The Norwegian Meteorological Institute (MET Norway) produces 120 h regional operational storm surge forecasts along the coast of Norway based on the Regional Ocean Modeling System (ROMS), using a model setup called Nordic4-SS. Despite advances in the development of models and computational capabilities, forecast errors remain large enough to impact response measures and issued alerts, in particular, during the strongest storm events. Reducing these errors will positively impact the efficiency of the warning systems while minimizing efforts and resources. Here, we investigate how forecasts can be improved with residual learning, i.e., training data-driven models to predict the residuals in forecasts from Nordic4-SS. A simple error mapping technique and a more sophisticated Neural Network (NN) method are tested. The simple error mapping technique provides a reduction in the Root Mean Square Error (RMSE) of less than 4%. Using the NN residual correction method, the RMSE in the Oslo Fjord is reduced by 36% for lead times of one hour, 9% for 24 h, and 5% for 60 h. Therefore, the residual NN method is a promising direction for correcting storm surge forecasts, especially on short timescales. Moreover, it is well adapted to being deployed operationally, as (i) the correction is applied on top of the existing model and requires no changes to it, (ii) all predictors used for NN inference are already available operationally, (iii) prediction by the NNs is very fast, typically a few seconds per station, and (iv) the NN correction can be provided to a human expert who may inspect it, compare it with the model output, and see how much correction is brought by the NN, allowing to capitalize on human expertise as a quality validation of the NN output. While no changes to the hydrodynamic model are necessary to calibrate the neural networks, they are specific to a given model and must be recalibrated when the numerical models are updated.

中文翻译：

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使用神经网络对业务风暴潮预报进行偏差校正

风暴潮可能会在沿海地区引发极端洪水。挪威气象研究所 (MET挪威) 基于区域海洋建模系统 (ROMS)，使用名为 Nordic4-SS 的模型设置，生成挪威沿海 120 小时区域业务风暴潮预报。尽管模型和计算能力的发展取得了进步，但预报误差仍然很大，足以影响响应措施和发布警报，特别是在最强的风暴事件期间。减少这些错误将对预警系统的效率产生积极影响，同时最大限度地减少工作量和资源。在这里，我们研究如何通过残差学习来改进预测，即训练数据驱动模型来预测 Nordic4-SS 预测中的残差。测试了简单的误差映射技术和更复杂的神经网络（NN）方法。简单的误差映射技术可将均方根误差 (RMSE) 降低至 4% 以下。使用NN残差校正方法，奥斯陆峡湾的RMSE对于1小时的交付周期降低了36%，对于24小时的周期降低了9%，对于60小时的周期降低了5%。因此，残差神经网络方法是修正风暴潮预报的一个有前途的方向，特别是在短时间尺度上。此外，它非常适合在操作中部署，因为 (i) 校正应用于现有模型之上并且不需要对其进行更改，(ii) 用于 NN 推理的所有预测器都已可操作使用，(iii) 预测神经网络的速度非常快，通常每个站几秒钟，并且（iv）可以将神经网络校正提供给人类专家，专家可以检查它，将其与模型输出进行比较，并查看神经网络带来了多少校正，允许利用人类专业知识作为神经网络输出的质量验证。虽然校准神经网络不需要改变流体动力学模型，但它们特定于给定模型，并且在更新数值模型时必须重新校准。