Deep learning-based forecasting of sea surface temperature in the interim future: application over the Aegean, Ionian, and Cretan Seas (NE Mediterranean Sea)

IF 2.2 3区 地球科学 Q2 OCEANOGRAPHY Ocean Dynamics Pub Date : 2024-01-13 DOI:10.1007/s10236-023-01595-3
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Abstract

Sea surface temperature (SST) is a key indicator of the global climate system and is directly related to marine and coastal ecosystems, weather conditions, and atmospheric events. Marine heat waves (MHWs), characterized by prolonged periods of high SST, affect significantly the oceanic water quality and thus, the local ecosystem, and marine and coastal activities. Given the anticipated increase of MHWs occurrences due to climate change, developing targeted strategies is needed to mitigate their impact. Accurate SST forecasting can significantly contribute to this cause and thus it comprises a crucial, yet challenging, task for the scientific community. Despite the wide variety of existing methods in the literature, the majority of them focus either on providing near-future SST forecasts (a few days until 1 month) or long-term predictions (decades to century) in climate scales based on hypothetical scenarios that need to be proven. In this work, we introduce a robust deep learning-based method for efficient SST forecasting of the interim future (1 year ahead) using high-resolution satellite-derived SST data. Our approach processes daily SST sequences lasting 1 year, along with five other relevant atmospheric variables, to predict the corresponding daily SST timeseries for the subsequent year. The novel method was deployed to accurately forecast SST over the northeastern Mediterranean Seas (Aegean, Ionian, Cretan Seas: AICS). Utilizing the effectiveness of well-established deep learning architectures, our method can provide accurate spatiotemporal predictions for multiple areas at once, without the need to be deployed separately at each sub-region. The modular design of the framework allows customization for different spatial and temporal resolutions according to use case requirements. The proposed model was trained and evaluated using available data from the AICS region over a 15-year time period (2008–2022). The results demonstrate the efficiency of our method in predicting SST variability, even for previously unseen data that are over 2 years in advance, in respect to the training set. The proposed methodology is a valuable tool that also can contribute to MHWs prediction.

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基于深度学习的未来中期海面温度预报:在爱琴海、爱奥尼亚海和克里特海(地中海东北部)的应用
摘要 海洋表面温度(SST)是全球气候系统的一个关键指标,与海洋和沿海生态系统、天气状况和大气事件直接相关。海洋热浪(MHWs)的特点是长时间的高 SST,对海洋水质有很大影响,从而影响当地生态系统以及海洋和沿海活动。鉴于气候变化预计会导致热浪发生率上升,因此需要制定有针对性的策略来减轻热浪的影响。准确的 SST 预报可以在很大程度上促进这一目标的实现,因此是科学界的一项重要而又具有挑战性的任务。尽管文献中现有的方法种类繁多,但大多数方法要么侧重于提供近期(几天到一个月)的 SST 预报,要么侧重于根据需要证明的假设情景在气候尺度上进行长期预测(几十年到一个世纪)。在这项工作中,我们介绍了一种基于深度学习的稳健方法,利用高分辨率卫星衍生 SST 数据对未来中期(未来 1 年)进行高效 SST 预测。我们的方法处理持续 1 年的每日 SST 序列以及其他五个相关大气变量,以预测随后一年的相应每日 SST 时间序列。这种新方法被用于准确预报地中海东北部海域(爱琴海、爱奥尼亚海、克里特海:AICS)的海温。利用成熟的深度学习架构的有效性,我们的方法可以同时为多个区域提供准确的时空预测,而无需在每个子区域单独部署。该框架的模块化设计允许根据用例要求定制不同的空间和时间分辨率。我们使用 AICS 地区 15 年(2008-2022 年)的可用数据对所提出的模型进行了训练和评估。结果表明,相对于训练集而言,我们的方法在预测 SST 变率方面非常有效,即使是对以前未见过的提前两年以上的数据也是如此。所提出的方法是一种有价值的工具,也有助于预测多年平均海温。
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来源期刊
Ocean Dynamics
Ocean Dynamics 地学-海洋学
CiteScore
5.40
自引率
0.00%
发文量
37
审稿时长
6-12 weeks
期刊介绍: Ocean Dynamics is an international journal that aims to publish high-quality peer-reviewed articles in the following areas of research: Theoretical oceanography (new theoretical concepts that further system understanding with a strong view to applicability for operational or monitoring purposes); Computational oceanography (all aspects of ocean modeling and data analysis); Observational oceanography (new techniques or systematic approaches in measuring oceanic variables, including all aspects of monitoring the state of the ocean); Articles with an interdisciplinary character that encompass research in the fields of biological, chemical and physical oceanography are especially encouraged.
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