A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting
A Hybrid Multivariate Deep Learning Network for Multistep Ahead Sea Level Anomaly Forecasting
复制标题
用于多步提前海平面异常预测的混合多元深度学习网络
DOI：
10.1175/jtech-d-21-0043.1
复制
发表时间：
2022
影响因子：
2.2
通讯作者：
Hongli Fu
中科院分区：
文献类型：
--
作者：
Guosong Wang;Xidong Wang;Xinrong Wu;Kexiu Liu;Yiquan Qi;Chunjian Sun;Hongli Fu
摘要：高度计和散射计积累的遥感数据为海洋状况预报提供了新的机会，并提高了我们对海洋-大气交换的认识。然而，对于不同模态的海平面异常（SLA）的多变量、多步骤、时空序列预报的研究仍然存在问题。在本文中，我们提出了一种新的混合和多变量深度神经网络，命名为HMnet 3，可用于中国南海（SCS）的SLA预测。首先，时空序列预测网络由改进的卷积长短期记忆（ConvLSTM）网络使用通道注意机制和1993年至2015年的多变量数据进行训练。然后利用改进的长短期记忆（LSTM）网络训练时间序列预测网络，该网络通过集成经验模式分解（EEMD）实现。最后，这两个网络相结合的一个连续的校正方法，以产生SLA预测的前置时间长达15天，特别关注的公海和沿海地区的南海。在2016-18年的测试期间，HMnet 3与海表温度异常（SSTA），风速异常（SPDA）和SLA数据的性能远远优于最先进的动态和统计（ConvLSTM，持久性和气候学）预测模型。研究了用于实时数据集的试验模拟实验的涡轮机试验台，其中HMnet 3的涡流分类指标对所有属性都有利，特别是对于小尺度涡流。
Abstract.The accumulated remote sensing data of altimeters and scatterometers have provided new opportunities for ocean state forecasting and have improved our knowledge of ocean–atmosphere exchanges. Studies on multivariate, multistep, spatiotemporal sequence forecasts of sea level anomalies (SLA) for different modalities, however, remain problematic. In this paper, we present a novel hybrid and multivariate deep neural network, named HMnet3, which can be used for SLA forecasting in the South China Sea (SCS). First, a spatiotemporal sequence forecasting network is trained by an improved convolutional long short-term memory (ConvLSTM) network using a channelwise attention mechanism and multivariate data from 1993 to 2015. Then a time series forecasting network is trained by an improved long short-term memory (LSTM) network, which is realized by ensemble empirical mode decomposition (EEMD). Finally, the two networks are combined by a successive correction method to produce SLA forecasts for lead times of up to 15 days, with a special focus on the open sea and coastal regions of the SCS. During the testing period of 2016–18, the performance of HMnet3 with sea surface temperature anomaly (SSTA), wind speed anomaly (SPDA), and SLA data is much better than those of state-of-the-art dynamic and statistical (ConvLSTM, persistence, and climatology) forecast models. Stricter testbeds for trial simulation experiments with real-time datasets are investigated, where the eddy classification metrics of HMnet3 are favorable for all properties, especially for those of small-scale eddies.