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Atmosphere
Special Issues
Extreme Events and Risk of Disasters
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Extreme Events and Risk of Disasters

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 2836

Special Issue Editors

Dr. Helber Barros Gomes

E-Mail Website
Guest Editor
Institute of Atmospheric Sciences, Federal University of Alagoas, Maceio 57072-900, Brazil
Interests: climate variability and change; natural hazards and impacts; extreme events; numerical modeling; climate dynamics; atmosphere circulations; renewable energies
Prof. Dr. Jose A. Marengo

grade E-Mail Website
Guest Editor
National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), São Jose dos Campos, Brazil
Interests: climatology; hydrology; climate changes; natural disasters; natural disaster risk reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change is an imminent threat for most humans in most areas around the world. As the Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) states, the human influence on the climate system is unequivocal, and various measures including frequency and intensity of weather extremes have shown an increasing trend in the present climate with about 1 degree surface warming. Understanding and quantifying the uncertainties associated with global and regional climate models regarding the projections of extreme weather and climate events is a challenge. In this sense, this Special Issue aims to invite potential contributors to submit novel and original papers outlining important scientific investigations based on studies on the changes of the frequency and the intensity of extreme events, including compound extreme events in present climate and future climate projections. The focus of this Special Issue also includes work on the monitoring and early warning of natural disasters or hydro-geo-meteorological origin for disaster risk reduction.

This Special Issue is now open for submissions of novel and original papers outlining important scientific investigations. Modeling, reanalysis, and observational studies on the changes of the frequency and the intensity of extreme events are also welcomed.

Dr. Helber Barros Gomes
Prof. Dr. Jose A. Marengo
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.

Keywords

  • weather and climate extremes
  • climate variability and change
  • land use and land cover
  • droughts
  • floods
  • heatwaves
  • landslides
  • multihazard early warning systems
  • risks, vulnerability, and impacts

Published Papers (2 papers)

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Research

30 pages, 13666 KiB  
Article
Assessing the Risk of Extreme Storm Surges from Tropical Cyclones under Climate Change Using Bidirectional Attention-Based LSTM for Improved Prediction
by Vai-Kei Ian, Su-Kit Tang and Giovanni Pau
Atmosphere 2023, 14(12), 1749; https://doi.org/10.3390/atmos14121749 - 28 Nov 2023
Cited by 1 | Viewed by 917
Abstract
Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical [...] Read more.
Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change. Full article
(This article belongs to the Special Issue Extreme Events and Risk of Disasters)
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18 pages, 7668 KiB  
Article
A Study of Drought and Flood Cycles in Xinyang, China, Using the Wavelet Transform and M-K Test
by Xinchen Gu, Pei Zhang, Wenjia Zhang, Yang Liu, Pan Jiang, Shijie Wang, Xiaoying Lai and Aihua Long
Atmosphere 2023, 14(8), 1196; https://doi.org/10.3390/atmos14081196 - 25 Jul 2023
Cited by 3 | Viewed by 1128
Abstract
Accurately identifying and predicting droughts can provide local managers with a basis for decision-making. The Xinyang region is prone to droughts and floods, which have a large impact on local agriculture and socio-economics. This paper employs precipitation data from the Xinyang region to [...] Read more.
Accurately identifying and predicting droughts can provide local managers with a basis for decision-making. The Xinyang region is prone to droughts and floods, which have a large impact on local agriculture and socio-economics. This paper employs precipitation data from the Xinyang region to provide a scientific basis for drought and flood control measures in this region. The data are first treated with standardized precipitation indices (SPIs) on three-month, six-month, and nine-month time scales. Subsequently, a Morlet wavelet analysis is performed for each of the three time scales analyzed for the SPI. The results show multiple time scales of drought and flood disasters in the Xinyang region. The cycles of drought and flood disasters in the Xinyang region show different fluctuations on different SPI scales. The SPI time series reflect a strong fluctuation period of 17a for drought and flood disasters in the Xinyang region. An analysis of the variance of the wavelet coefficients showed that the first main cycle of drought and flood disasters in the Xinyang region is 7a, and the second and third sub-cycles are 4a and 13a, respectively. We conclude that floods are more frequent than droughts in Xinyang and are more likely to occur from 2017 to 2021, with a subsequent shift to droughts. Local managers should put drought prevention measures in place to deal with droughts after 2021. Full article
(This article belongs to the Special Issue Extreme Events and Risk of Disasters)
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