Abstract The quality of metocean forecasts at longer forecast ranges has a significant impact on maritime safety and offshore operations. A nonlinear ensemble averaging technique is demonstrated using neural networks applied to one year (2017) of Global ocean Wave Ensemble forecast System (GWES) data provided by NCEP. Post-processing algorithms are developed based on multilayer perceptron neural networks (NN) trained with altimeter data to improve the global forecast skill, from nowcast to forecast ranges up to 10 days, including significant wave height (Hs) and wind speed (U10). NNs are applied as an alternative to the typical use of the arithmetic ensemble mean (EM). NN models are constructed using six variables sourced from 21 ensemble members, plus latitude, sin/cos of longitude, sin/cos of time, forecast lead time, and GWES cycle. The NN outputs are the residues of Hs and U10, i.e., the difference from the EM to the observations. One hidden (intermediate) layer is evaluated in terms of the optimum number of neurons (complexity) to map the given problem. The sensitivity test considered 26 different numbers of neurons, 10 seeds for initial conditions, and 3 equally-divided datasets; for a total of 780 NN experiments. Assessments using 2,507,099 paired satellite/GWES fields show that a simple NN model with few neurons is able to reduce the systematic errors for short-range forecasts, while a NN with more neurons is required to minimize the scatter error at longer forecast ranges. The novel method shows that one single NN model with 140 neurons is able to improve the error metrics for the whole globe while covering all forecast ranges analyzed. The bias of the widely used EM of GWES that varies from -10% to 10% for Hs compared to altimeters can be reduced to values within 5%. The RMSE of day-10 forecasts from the NN simulations indicated a gain of two days in predictability when compared to the EM, using a reasonably simple post-processing model with low computational cost.

中文翻译：

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使用神经网络改进 NCEP 的全球尺度波浪系综平均值

摘要 较长预报范围内气象海洋预报的质量对海上安全和海上作业有重大影响。使用应用于 NCEP 提供的一年（2017 年）全球海浪集合预报系统 (GWES) 数据的神经网络，展示了一种非线性集合平均技术。后处理算法是基于使用高度计数据训练的多层感知器神经网络 (NN) 开发的，以提高全球预测技能，从临近预报到长达 10 天的预测范围，包括显着波高 (Hs) 和风速 (U10)。NNs 被用作算术集合平均 (EM) 的典型用途的替代方案。NN 模型是使用来自 21 个集合成员的六个变量构建的，加上纬度、经度的正弦/余弦、时间的正弦/余弦、预测提前期和 GWES 周期。NN 输出是 Hs 和 U10 的残差，即从 EM 到观测值的差异。一个隐藏（中间）层根据神经元的最佳数量（复杂性）进行评估，以映射给定的问题。敏感性测试考虑了 26 个不同数量的神经元、10 个初始条件的种子和 3 个等分的数据集；总共 780 个神经网络实验。使用 2,507,099 个配对卫星/GWES 场进行的评估表明，具有很少神经元的简单 NN 模型能够减少短期预测的系统误差，而需要具有更多神经元的 NN 才能最大限度地减少较长预测范围内的散射误差。这种新颖的方法表明，一个具有 140 个神经元的单个 NN 模型能够在覆盖分析的所有预测范围的同时改善全球的误差指标。与高度计相比，GWES 广泛使用的 EM 偏差从 -10% 到 10% 不等，Hs 可以减少到 5% 以内的值。来自 NN 模拟的第 10 天预测的 RMSE 表明，与 EM 相比，可预测性提高了两天，使用了计算成本低的合理简单的后处理模型。