College of Information Science and Engineering, Ocean University of China, Qingdao 266005, China.
Key Laboratory of Physical Oceanography, Institute for Advanced Ocean Studies, Ocean University of China, Qingdao 266005, China.
Sensors (Basel). 2022 Feb 19;22(4):1636. doi: 10.3390/s22041636.
Impacted by global warming, the global sea surface temperature (SST) has increased, exerting profound effects on local climate and marine ecosystems. So far, investigators have focused on the short-term forecast of a small or medium-sized area of the ocean. It is still an important challenge to obtain accurate large-scale and long-term SST predictions. In this study, we used the reanalysis data sets provided by the National Centers for Environmental Prediction based on the Internet of Things technology and temporal convolutional network (TCN) to predict the monthly SSTs of the Indian Ocean from 2014 to 2018. The results yielded two points: Firstly, the TCN model can accurately predict long-term SSTs. In this paper, we used the Pearson correlation coefficient (hereafter this will be abbreviated as "correlation") to measure TCN model performance. The correlation coefficient between the predicted and true values was 88.23%. Secondly, compared with the CFSv2 model of the American National Oceanic and Atmospheric Administration (NOAA), the TCN model had a longer prediction time and produced better results. In short, TCN can accurately predict the long-term SST and provide a basis for studying large oceanic physical phenomena.
受全球变暖的影响，全球海表温度（SST）不断升高，对当地气候和海洋生态系统产生了深远的影响。到目前为止，研究人员主要关注的是中小区域的短期预测。要获得准确的大尺度、长期 SST 预测仍然是一个重要的挑战。在这项研究中，我们使用基于物联网技术和时间卷积网络（TCN）的美国国家环境预报中心提供的再分析数据集，对 2014 年至 2018 年印度洋的月 SST 进行预测。结果得出两点：首先，TCN 模型可以准确地预测长期 SST。在本文中，我们使用 Pearson 相关系数（以下简称“相关系数”）来衡量 TCN 模型的性能。预测值与真实值之间的相关系数为 88.23%。其次，与美国国家海洋和大气管理局（NOAA）的 CFSv2 模型相比，TCN 模型具有更长的预测时间和更好的结果。总之，TCN 可以准确地预测长期 SST，为研究大型海洋物理现象提供了依据。
