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Atmosphere
Special Issues
Sea-Level Rise and Associated Potential Storm Surge Vulnerability
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Sea-Level Rise and Associated Potential Storm Surge Vulnerability

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 4444

Special Issue Editors

Prof. Dr. Seung-Won Suh

E-Mail Website
Guest Editor
Department of Coastal Construction Engineering, Kunsan National University, 558 Daehak-ro, Kunsan, Jeonbuk 54150, Korea
Interests: hydrodynamic modeling; coastal engineering; storm surge; sea-level rise
Special Issues, Collections and Topics in MDPI journals
Dr. Sooyoul Kim

E-Mail Website
Guest Editor
Center for Water Cycle, Marine Environment and Disaster Management, Kumamoto University, Kumamoto, Japan
Interests: storm surge; multiple hazard; climate change; coupling modeling, flood risk

Special Issue Information

Dear Colleagues,

Rapidly increasing global warming, induced by meteorological change, acted as a primary factor in the acceleration of sea-level rise (SLR) and has continuously caused diverse coastal hazards. Not only the non-linear increase in the mean SLR, but also the summertime SLR should be emphasized in the analyses of natural disasters on the coast, such as storm surges, wave overtopping, and coastal erosion/deposition due to repeated changes in the weather forces. Along with the rise in sea levels, people around the world should seek effective countermeasures against coastal vulnerabilities that appear as the final change in climate factors.

We invite the submission of original research articles and reviews on any aspect of atmosphere–sea interactions in the coastal zone, including (but not limited to) SLR, storm surges, wave overtopping, extreme waves, erosion, deposition, and so on. We welcome studies using the most recent technology, such as big data analyses using AI, extreme wave predictions, real-time forecasting of storm surges and/or wave overtopping, and numerical modeling focusing on the coastal zone. We also welcome articles that observe and reanalyze data to address long-term variations of sea level due to atmospheric changes.

Prof. Dr. Seung-Won Suh
Dr. Sooyoul Kim
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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.

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Published Papers (2 papers)

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Research

17 pages, 3678 KiB  
Article
Simulation of Storm Surge Heights Based on Reconstructed Historical Typhoon Best Tracks Using Expanded Wind Field Information
by Seung-Won Suh
Atmosphere 2023, 14(9), 1461; https://doi.org/10.3390/atmos14091461 - 20 Sep 2023
Viewed by 1444
Abstract
A numerical model integrating tides, waves, and surges can accurately evaluate the surge height (SH) risks of tropical cyclones. Furthermore, incorporating the external forces exerted by the storm’s wind field can help to accurately reproduce the SH. However, the lack of long-term typhoon [...] Read more.
A numerical model integrating tides, waves, and surges can accurately evaluate the surge height (SH) risks of tropical cyclones. Furthermore, incorporating the external forces exerted by the storm’s wind field can help to accurately reproduce the SH. However, the lack of long-term typhoon best track (BT) data degrades the SH evaluations of past events. Moreover, archived BT data (BTD) for older typhoons contain less information than recent typhoon BTD. Thus, herein, the wind field structure, specifically its relationship with the central air pressure, maximum wind speed, and wind radius, are augmented. Wind formulae are formulated with empirically adjusted radii and the maximum gradient wind speed is correlated with the central pressure. Furthermore, the process is expanded to four quadrants through regression analyses using historical asymmetric typhoon advisory data. The final old typhoon BTs are converted to a pseudo automated tropical cyclone forecasting format for consistency. Validation tests of the SH employing recent BT and reconstructed BT (rBT) indicate the importance of the nonlinear interactions of tides, waves, and surges for the macrotidal west and microtidal south coasts of Korea. The expanded wind fields—rBT—based on the historical old BT successfully assess the return periods of the SH. The proposed process effectively increases typhoon population data by incorporating actual storm tracks. Full article
(This article belongs to the Special Issue Sea-Level Rise and Associated Potential Storm Surge Vulnerability)
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25 pages, 11552 KiB  
Article
Bridging the Gap: Enhancing Storm Surge Prediction and Decision Support with Bidirectional Attention-Based LSTM
by Vai-Kei Ian, Rita Tse, Su-Kit Tang and Giovanni Pau
Atmosphere 2023, 14(7), 1082; https://doi.org/10.3390/atmos14071082 - 27 Jun 2023
Cited by 11 | Viewed by 2388
Abstract
Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast [...] Read more.
Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA’s superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities. Full article
(This article belongs to the Special Issue Sea-Level Rise and Associated Potential Storm Surge Vulnerability)
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