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Linking Upper Ocean Dynamics with Extreme Weather and Climate Events over the Ocean (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 2790

Special Issue Editors


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Guest Editor
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Interests: tropical cyclone–ocean interaction; upper ocean dynamics; ocean modelling; deep learning; wave–current interaction
NorthWest Research Associates, Seattle, WA 98105, USA
Interests: remote sensing; weather and climate prediction and modeling; meteorology; climatology; ocean–atmosphere and air–sea interactions
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Special Issue Information

Dear Colleagues,

Extreme weather and climate events have a huge impact on the earth system and on human society. These occurrences often lead to natural hazards, underscoring the significance of studying such extreme weather and climate phenomena.

In the coupled earth system, extreme weather events and climate patterns trigger responses evident in the upper ocean. The feedback, whether positive or negative, plays a crucial role in the development of extreme weather and climate events. Furthermore, the complexities of ocean dynamics, spanning multiple scales, further emphasize the importance of understanding the interplay between ocean and extreme weather and climate events. New methodological and technological developments, including but not limited to, remote sensing, in situ observation, numerical modelling and artificial intelligence, have facilitated our understanding of these extreme events.

This Special Issue aims to publish research articles addressing the role the ocean plays in the occurrence, development, and prediction of extreme events. Field work, satellite remote sensing, theoretical derivation, and numerical modelling studies, aimed at better understanding the phenomena and processes of coupled atmosphere–ice–ocean system and their related interactions are all welcome. As a new path, deep learning or other new artificial intelligence technologies are particularly welcome.

Dr. Hailun He
Dr. Gad Levy
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • extreme weather and climate
  • air–sea interaction
  • tropical cyclone
  • extratropical cyclone
  • ocean dynamics
  • biological response
  • in situ observation
  • remote sensing observation
  • ocean modelling
  • operational forecast

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Related Special Issue

Published Papers (2 papers)

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Research

20 pages, 3531 KiB  
Article
Sea Surface Temperature Prediction Using ConvLSTM-Based Model with Deformable Attention
by Benyun Shi, Conghui Ge, Hongwang Lin, Yanpeng Xu, Qi Tan, Yue Peng and Hailun He
Remote Sens. 2024, 16(22), 4126; https://doi.org/10.3390/rs16224126 - 5 Nov 2024
Cited by 1 | Viewed by 1274
Abstract
Sea surface temperature (SST) prediction has received increasing attention in recent years due to its paramount importance in the various fields of oceanography. Existing studies have shown that neural networks are particularly effective in making accurate SST predictions by efficiently capturing spatiotemporal dependencies [...] Read more.
Sea surface temperature (SST) prediction has received increasing attention in recent years due to its paramount importance in the various fields of oceanography. Existing studies have shown that neural networks are particularly effective in making accurate SST predictions by efficiently capturing spatiotemporal dependencies in SST data. Among various models, the ConvLSTM framework is notably prominent. This model skillfully combines convolutional neural networks (CNNs) with recurrent neural networks (RNNs), enabling it to simultaneously capture spatiotemporal dependencies within a single computational framework. To overcome the limitation that CNNs primarily capture local spatial information, in this paper we propose a novel model named DatLSTM that integrates a deformable attention transformer (DAT) module into the ConvLSTM framework, thereby enhancing its ability to process more complex spatial relationships effectively. Specifically, the DAT module adaptively focuses on salient features in space, while ConvLSTM further captures the temporal dependencies of spatial correlations in the SST data. In this way, DatLSTM can adaptively capture complex spatiotemporal dependencies between the preceding and current states within ConvLSTM. To evaluate the performance of the DatLSTM model, we conducted short-term SST forecasts in the Bohai Sea region with forecast lead times ranging from 1 to 10 days and compared its efficacy against several benchmark models, including ConvLSTM, PredRNN, TCTN, and SwinLSTM. Our experimental results show that the proposed model outperforms all of these models in terms of multiple evaluation metrics short-term SST prediction. The proposed model offers a new predictive learning method for improving the accuracy of spatiotemporal predictions in various domains, including meteorology, oceanography, and climate science. Full article
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21 pages, 13437 KiB  
Article
The Remote Effects of Typhoons on the Cold Filaments in the Southwestern South China Sea
by Zezheng Zhao, Shengmu Yang, Huipeng Wang, Taikang Yuan and Kaijun Ren
Remote Sens. 2024, 16(17), 3293; https://doi.org/10.3390/rs16173293 - 4 Sep 2024
Cited by 1 | Viewed by 948
Abstract
Cold filaments (CFs) in the southwestern South China Sea (SCS) impact local hydrodynamics and the ecological environment. In this study, the effects of typhoons passing over the northern SCS on CFs are investigated using multi-source observational and reanalysis data. Statistical analysis of CF [...] Read more.
Cold filaments (CFs) in the southwestern South China Sea (SCS) impact local hydrodynamics and the ecological environment. In this study, the effects of typhoons passing over the northern SCS on CFs are investigated using multi-source observational and reanalysis data. Statistical analysis of CF responses to typhoons over the past 24 years shows that during typhoon periods in the northern SCS, the CFs are intensified. We further analyze the remote effect of typhoons on the CF during Typhoon Kalmaegi in 2014, which caused a sea surface temperature (SST) drop of more than 3 °C in the CF region. The strengthened along-coast wind induced strong upwelling off the Vietnam coast. The maximum vertical velocity in the CF reaches three times the usual value. Meanwhile, influenced by the peripheral wind field of Kalmaegi, cold coastal water accumulated at the CF region due to the shafting of the offshore current. Both strong offshore currents and coastal upwellings enhanced the intensity of the CF. These findings demonstrate that typhoons not only directly affect ocean dynamic processes along their path but also present significant remote influences on coastal dynamics in other regions. This study enhances the understanding of CF evolution and sea–air interactions during extreme events. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Remote sensed spatio-temporal study of the Tropical Cyclone Freddy exceptional case
Authors: Giuseppe Ciardullo; Leonardo Primavera; Fabrizio Ferrucci; Fabio Lepreti; Vincenzo Carbone
Affiliation: Dipartimento di Fisica, Università della Calabria, Cubo 31/C, Ponte P. Bucci, Rende, Italy
Abstract: Dynamical processes during the different stages of evolution of tropical cyclones play crucial roles in their development and intensification, making them one of the most powerful natural forces on Earth. Since they can be classified as atmospheric extreme events due to the combination of several factors, it is significant to study their dynamical behavior and the nonlinear effects generated by emerging structures during scales and intensity transitions, correlating them with the surrounding environment. Under this perspective, the present work aims to investigate the extraordinary and record-breaking case study of Tropical Cyclone Freddy (2023 Indian Ocean tropical season) from a purely dynamic point of view, examining the superposition of energetic structures at different spatiotemporal scales. The first and most powerful intensification of Freddy is inspected by considering its characteristic thermal fluctuations over 12 days of its evolution. The tool used for this investigation is the Proper Orthogonal Decomposition (POD), in which a set of empirical basis functions is built up, retaining the maximum energetic content of the turbulent flow. The method is applied on a satellite imagery dataset, acquired from the SEVIRI radiometer onboard the Meteosat Second Generation - 8 (MSG-8) geostationary platform, from which the cloud-top temperature scalar field is remote sensed looking at the cloud’s associated system. For this application, considering Freddy’s very long life period and exceptionally wide path of its evolution, reanalysis and tracking data archives are taken into account in order to create an appropriately dynamic spatial grid. Freddy’s eye is followed after its first shape formation with very high temporal resolution snapshots of the temperature field. The energy content in three different characteristic scale ranges is analyzed through the associated spatial and temporal component spectra, focusing both on the total period and on the transitions between different categories.

Title: Potential Impact of Ocean Optical Properties on the Intensity of Mediterranean Cyclones
Authors: John Karagiorgos (1), Platon Patlakas (1), Vassilios Vervatis (1), and Sarantis Sofianos (1)
Affiliation: (1) Department of Physics, Section of Environmental Physics & Meteorology, National and Kapodistrian University of Athens, Athens, Greece
Abstract: This study investigates the role of ocean optical properties and penetrative radiation in shaping the heat content of the upper ocean and their subsequent impact on the intensity of Mediterranean cyclones. Using a coupled ocean-wave-atmosphere model for the Mediterranean region, we performed sensitivity experiments with different ocean color datasets and solar radiation penetration schemes. The findings aim to advance our understanding on the interactions between the upper ocean conditions and cyclone dynamics, providing new insights into the feedback mechanisms that affect cyclone development and intensity. Apart from the pure scientific interest, the findings can be a step forward in improving cyclone predictability and climate impact assessments.

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