As an extension of wave period inversion models based on altimetry data by an artificial neural network approach, a Deep Neural Network approach (DNN) mean wave period model is developed by introducing new parameters as DNN inputs. In addition to conventional altimeter-derived parameters such as significant wave height (SWH) and the normalized radar cross-section (NRCS) sigma0, the spatial (along-track) SWH gradient and SWH standard deviation (STD) for standard 1-s altimetry data are assumed to be responsible for random wave field dynamics and, thus, for the observed characteristic mean wave period. A comparison with in situ measurements of wave buoys shows higher accuracy of the novel DNN models by using these new parameters. The wave period estimation from DNN model is also consistent with the latest wave reanalysis and indicates less bias when compared to the buoys. The global mean wave steepness distribution from the DNN model shows good agreement with those provided by the wave reanalysis. The sensitivity of input variables sigma0, SWH, SWH gradient, and SWH STD on the results of the DNN model are also investigated. Perspectives on the DNN method for developing universal mission-independent wave period models are discussed.

Objectives of the work

The mean wave period is an important parameter for characterizing wave properties and can be retrieved from altimeter observations. Considering random wave field dynamics, we present a deep neural network (DNN) model for mean wave period retrieval by introducing the SWH gradient and standard deviation as the inputs. The mean wave period estimation of the DNN model shows good agreement with the latest wave reanalysis and less bias compared to buoys. In an attempt to make the DNN mean wave period retrieval model universal, a method of applying DNN model cross-altimetry missions is presented.

中文翻译：

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从高度计中检索波周期：随机波场动力学的深度学习

作为人工神经网络方法对基于测高数据的波浪周期反演模型的扩展，通过引入新参数作为 DNN 输入开发了深度神经网络方法 (DNN) 平均波浪周期模型。除了有效波高 (SWH) 和归一化雷达截面 (NRCS) sigma0 等传统高度计派生参数之外，标准 1 秒高度测量的空间（沿航迹）SWH 梯度和 SWH 标准偏差 (STD)假设数据负责随机波场动力学，从而负责观察到的特征平均波周期。与波浪浮标的原位测量的比较表明，通过使用这些新参数，新型 DNN 模型具有更高的准确性。DNN 模型的波浪周期估计也与最新的波浪再分析一致，与浮标相比偏差较小。DNN 模型的全局平均波陡度分布与波浪再分析提供的分布非常吻合。还研究了输入变量 sigma0、SWH、SWH 梯度和 SWH STD 对 DNN 模型结果的敏感性。讨论了用于开发通用任务独立波周期模型的 DNN 方法的前景。

工作目标

平均波浪周期是表征波浪特性的一个重要参数，可以从高度计观测中获取。考虑到随机波场动力学，我们通过引入 SWH 梯度和标准偏差作为输入，提出了一种用于平均波周期检索的深度神经网络 (DNN) 模型。与浮标相比，DNN 模型的平均波浪周期估计与最新的波浪再分析非常吻合，偏差更小。为了使DNN平均波浪周期反演模型具有普遍性，提出了一种应用DNN模型跨测高任务的方法。