Reconstruction of Typhoon-Induced Ocean Thermal Structures Using Deep Learning and Multi-Source Satellite Data with News Impact Analysis
Source abstract
Abstract
Reconstructing the three-dimensional thermal structure of the ocean under typhoon conditions presents significant challenges due to the scarcity of observational data, particularly in subsurface regions, and the limitations of existing observation methods in terms of spatial and temporal resolution. Accurate reconstruction of these structures is crucial for understanding the energy exchange between the ocean and typhoons, as this exchange directly influences typhoon intensity and trajectory. To address these challenges, this study introduces a fully connected transformer network (FCT), which integrates fully connected layers with a transformer model. The FCT model leverages the attention mechanisms inherent in the transformer architecture to effectively extract and integrate multi-scale ocean dynamical features. Using data from Typhoon Lekima in 2019, this study reconstructs ocean thermal structures at various depths and achieves an RMSE of 1.03 °C and an MAE of 0.83 °C when validated against Argo data. Furthermore, the model’s robustness was demonstrated through five-fold cross-validation, with the validation loss exhibiting minor fluctuations across folds but remaining stable overall, with an average validation loss of 0.986 °C, indicating the model’s generalizability. Sensitivity analysis also revealed the model’s resilience to variations in key input variables, showing minimal impact on output even with perturbations of up to 10% in input data. In addition, the study incorporates content analysis of typhoon-related news reports from 2011 to 2020, revealing a predominance of political topics, which underscores the central role of government in disaster response, with economic and ecological topics following. This integrated approach not only enhances our understanding of the interactions between ocean thermal structures and typhoon dynamics but also provides valuable insights into the societal impacts of typhoons, as reflected in media coverage, contributing to improved disaster management strategies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Tropical and Extratropical Cyclones Research · Ocean Waves and Remote Sensing · Flood Risk Assessment and Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Literature and Journalism, Ocean University of China, Qingdao 266100, China
Faculty of Information Science and Engineering, College of Marine Technology, Ocean University of China, Qingdao 266100, China
Laoshan Laboratory, Qingdao 266016, China
Funding
Laoshan Laboratory · Marine Science and Technology Innovation Demonstration Project of Qingdao · Key Research and Development Program of Shandong Provinc · the Taishan Scholar Foundation of Shandong Province
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