Deep Learning-Derived Long-Term Dataset of Internal Waves in the Northern South China Sea from Satellite Imagery

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Xudong Zhang, Xiaofeng Li
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引用次数: 0

Abstract

Abstract. Internal waves (IWs) are an important ocean process in transmitting energy between multiscale ocean dynamics, making them a crucial oceanic phenomenon. The South China Sea (SCS) is renowned for its frequent large-amplitude IW activities, emphasizing the importance of collecting and analyzing extensive observational data. In this study, we present a comprehensive IW dataset covering the northern SCS covering 112.40–121.32° E and 18.32–23.19° N, spanning from 2000 to 2022 with a 250 m spatial resolution. The IW dataset comprises 3085 high-resolution MODIS true-color IW images paired with precise IW position information extracted from 15830 MODIS images using advanced deep learning techniques. IWs in the northern SCS are divided into four regions based on extracted IW spatial distributions, facilitating detailed analyses of IW characteristics, including spatial and temporal distributions across both the entire northern SCS and its sub-regions. Notably, we uncover typical "double-peak" distributions corresponding to the lunar day, underscoring IWs' close relationship with tides. Furthermore, we identify two IW-free silence regions attributed to underwater topography influences, indicating varied IW characteristics across regions and suggesting underlying mechanisms warrant further investigation. The constructed dataset holds significant potential for applications in studying IW-environment interactions, developing monitoring and prediction models, validating and enhancing numerical simulations, and serving as an educational resource to foster awareness and interest in IW research.
通过卫星图像深度学习得出的南海北部内波长期数据集
摘要内波是多尺度海洋动力学之间传递能量的重要海洋过程,是一种重要的海洋现象。中国南海(SCS)以其频繁的大振幅内波活动而闻名,这强调了收集和分析大量观测数据的重要性。在本研究中,我们提出了一个覆盖南中国海北部的综合 IW 数据集,其覆盖范围为东经 112.40-121.32 度和北纬 18.32-23.19 度,时间跨度为 2000 年至 2022 年,空间分辨率为 250 米。IW数据集包括3085幅高分辨率MODIS真彩IW图像,以及利用先进的深度学习技术从15830幅MODIS图像中提取的精确IW位置信息。根据提取的 IW 空间分布情况,将 SCS 北部的 IW 划分为四个区域,以便于详细分析 IW 的特征,包括整个 SCS 北部及其子区域的空间和时间分布情况。值得注意的是,我们发现了与阴历日相对应的典型 "双峰 "分布,凸显了 IW 与潮汐的密切关系。此外,我们还发现了两个由于水下地形影响而导致的无IW静默区,这表明不同区域的IW特征各不相同,其潜在机制值得进一步研究。所构建的数据集在研究 IW 与环境的相互作用、开发监测和预测模型、验证和增强数值模拟以及作为教育资源促进对 IW 研究的认识和兴趣等方面具有巨大的应用潜力。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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