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Atmosphere
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Forecasting and Modeling of Tropical Cyclones and Their Induced Wind...
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Forecasting and Modeling of Tropical Cyclones and Their Induced Wind and Precipitation

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

Deadline for manuscript submissions: 3 June 2024 | Viewed by 7845

Special Issue Editors

Dr. Qinglan Li

E-Mail Website
Guest Editor
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: tropical-cyclone-induced wind and rainfall forecast and modeling; forecasting and modeling the intensity of tropical cyclones; multiple operational models consensus forecast; climate change; environmental engineering
Prof. Dr. Corene Matyas

E-Mail Website
Guest Editor
Department of Geography, University of Florida, Gainesville, FL 32611-7315, USA
Interests: spatial analysis of precipitation; tropical cyclones; geographic information systems; spatial metrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Tropical cyclones (TCs) are often accompanied by strong winds and torrential rains, with a wide-ranging influence and great destructive power, especially in coastal areas, making them a disastrous weather system. However, TCs can also bring rain to some areas suffering from drought. In addition, a significant portion of the annual rainfall in many areas comes from TCs. TCs can also provide energy for wind-power generation in coastal areas and maintain the balance between global heat and momentum. Therefore, studying the evolution and mechanism of the winds and precipitation induced by TCs is very important for regional disaster prevention and mitigation, efficient energy use, as well as regional sustainable development.

This Special Issue is devoted to forecasting and modeling the wind, rainfall, and storm surges caused by TCs: three sources of damage for human beings. The intensity, spatial distribution, and duration of TC-induced disasters are related to the intensity, location, structure, and external environmental background of the TCs. Exploring and clarifying the mechanism of these factors will help to improve the accurate forecasting of TC-induced disasters. Furthermore, it will help wind-energy generation and avoid the risk associated with offshore wind farms.

Potential contributions to this Special Issue include TC studies focusing on climatology and meteorology, both regionally and globally, ranging from synoptic scales to small physics scales. Analyses may include global or mesoscale numerical weather prediction systems; field campaign studies; satellite, air, sea, or ground-base observations; and/or other idealized, statistical, or historical data. Modeling can apply to mathematical algorithms, statistical methods, numerical model simulation, and artificial intelligence models. Manuscripts in this collection should provide scientific insight into some aspects of TCs’ structure and involvement, and the induced wind, rainfall, and storm surges, providing a better understanding of how and why these natural events occur.

Dr. Qinglan Li
Prof. Dr. Corene Matyas
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

  • tropical cyclones
  • wind
  • precipitation
  • storm surge
  • intensity
  • forecast
  • modeling
  • spatiotemporal characteristics
  • satellite meteorology

Published Papers (6 papers)

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Research

18 pages, 3091 KiB  
Article
A Deep Learning-Based Downscaling Method Considering the Impact on Typhoons to Future Precipitation in Taiwan
by Shiu-Shin Lin, Kai-Yang Zhu and Chen-Yu Wang
Atmosphere 2024, 15(3), 371; https://doi.org/10.3390/atmos15030371 - 19 Mar 2024
Viewed by 636
Abstract
This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon [...] Read more.
This study proposes a deep neural network (DNN)-based downscaling model incorporating kernel principal component analysis (KPCA) to investigate the precipitation uncertainty influenced by typhoons in Taiwan, which has a complex island topography. The best tracking data of tropical cyclones from the Joint Typhoon Warning Center (JTWC) are utilized to calculate typhoon and non-typhoon precipitation. KPCA is applied to extract nonlinear features of the BCC-CSM1-1 (Beijing Climate Center Climate System Model version 1.1) and CanESM2 (second-generation Canadian Earth System Model) GCM models. The length of the data used in the two GCM models span from January 1950 to December 2005 (historical data) and from January 2006 to December 2099 (scenario out data). The rainfall data are collected from the weather stations in Taichung and Hualien (cities of Taiwan) operated by the Central Weather Administration (CWA), Taiwan. The period of rainfall data in Taichung and in Hualien spans from January 1950 to December 2005. The proposed model is constructed with features extracted from the GCMs and historical monthly precipitation from Taichung and Hualien. The model we have built is used to estimate monthly precipitation and uncertainty in both Taichung and Hualien for future scenarios (rcp 4.5 and 8.5) of the GCMs. The results suggest that, in Taichung and Hualien, the summer precipitation is mostly within the normal range. The rainfall in the long term (January 2071 to December 2080) for both Taichung and Hualien typically fall between 100 mm and 200 mm. In the long term, the dry season (January to April, November, and December) precipitation for Taichung and that in the wet season (May to October) for Hualien are less and more affected by typhoons, respectively. The dry season precipitation is more affected by typhoons in Taichung than Hualien. In both Taichung and Hualien, the long-term probability of rainfall exceeding the historical average in the dry season is higher than that in the wet season. Full article
(This article belongs to the Special Issue Forecasting and Modeling of Tropical Cyclones and Their Induced Wind and Precipitation)
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24 pages, 9690 KiB  
Article
Comparative Analysis of BALSSA and Conventional NWP Methods: A Case Study in Extreme Storm Surge Prediction in Macao
by Vai-Kei Ian, Su-Kit Tang and Giovanni Pau
Atmosphere 2023, 14(11), 1597; https://doi.org/10.3390/atmos14111597 - 25 Oct 2023
Cited by 1 | Viewed by 804
Abstract
In coastal regions, accurate storm surge prediction is crucial for effective disaster management and risk mitigation. This study presents a comparative analysis between BALSSA (Bidirectional Attention-based LSTM for Storm Surge Architecture) and the Japan Meteorological Agency (JMA) numerical storm surge model, focusing on [...] Read more.
In coastal regions, accurate storm surge prediction is crucial for effective disaster management and risk mitigation. This study presents a comparative analysis between BALSSA (Bidirectional Attention-based LSTM for Storm Surge Architecture) and the Japan Meteorological Agency (JMA) numerical storm surge model, focusing on the Saola-induced storm surge in Macao, September 2023. To train and assess the model, we leverage an extensive dataset comprising meteorological and tide level information from more than 80 typhoon occurrences in Macao spanning the period from 2017 to 2023. The results provide evidence of BALSSA’s effectiveness in capturing the complex spatio-temporal dynamics of storm surges, with a lead time of up to 72 h, as reflected by its MAE of 0.019 and RMSE of 0.024. It demonstrates reliable accuracy in predicting storm surge magnitude, timing, and spatial extent, potentially contributing to more precise and timely warnings for coastal communities. Furthermore, the real-time data assimilation feature of BALSSA ensures up-to-date information, aligned with the latest observations, which is essential for effective emergency preparedness and response. The high-resolution grids enhance risk assessment, highlighting BALSSA’s potential for early warnings, emergency planning, and coastal risk management. This study contributes valuable insights to the broader field of storm surge prediction, guiding decision-making processes and supporting the development of effective strategies to enhance coastal resilience. Full article
(This article belongs to the Special Issue Forecasting and Modeling of Tropical Cyclones and Their Induced Wind and Precipitation)
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20 pages, 5225 KiB  
Article
Prediction of Storm Surge Water Level Based on Machine Learning Methods
by Yun Liu, Qiansheng Zhao, Chunchun Hu and Nianxue Luo
Atmosphere 2023, 14(10), 1568; https://doi.org/10.3390/atmos14101568 - 16 Oct 2023
Cited by 2 | Viewed by 1278
Abstract
Storm surge disasters result in severe casualties and economic losses. Accurate prediction of storm surge water level is crucial for disaster assessment, early warning, and effective disaster management. Machine learning methods are relatively more efficient and straightforward compared to numerical simulation approaches. However, [...] Read more.
Storm surge disasters result in severe casualties and economic losses. Accurate prediction of storm surge water level is crucial for disaster assessment, early warning, and effective disaster management. Machine learning methods are relatively more efficient and straightforward compared to numerical simulation approaches. However, most of the current research on storm surge water level prediction based on machine learning methods is primarily focused on point predictions. In this study, we explore the feasibility of spatial water level prediction using the ConvLSTM model. We focus on the coastal area of Guangdong Province and employ MIKE21(2019) software to simulate historical typhoons that have made landfall in the region from 1991 to 2018. We construct two datasets: one for direct water level prediction and the other for indirect water level prediction based on water level changes. Utilizing the ConvLSTM network, we employ it to forecast storm surges on both datasets, effectively capturing both temporal and spatial characteristics and thus ensuring the production of dependable results. When directly predicting water levels, we achieve an MAE (mean absolute error) of 0.026 m and an MSE (mean squared error) of 0.0038 m2. In contrast, the indirect prediction approach yields even more promising results, with an MAE of 0.014 m and an MSE of 0.0007 m2. Compared to traditional numerical simulation methods, the ConvLSTM-based approach is simpler, faster, and able to predict water levels accurately without boundary conditions or topographies. Furthermore, we consider worst-case scenarios by predicting the maximum water increase value using the random forest model. Our results indicate that the random forest model can serve as a valuable reference for forecasting the maximum water increase value of typhoon storm surges, supporting effective emergency responses to disasters. Full article
(This article belongs to the Special Issue Forecasting and Modeling of Tropical Cyclones and Their Induced Wind and Precipitation)
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15 pages, 3124 KiB  
Article
Study of Landfalling Typhoon Potential Maximum Gale Forecasting in South China
by Zhizhong Su, Lifang Li, Fumin Ren, Jing Zhu, Chunxia Liu, Qilin Wan, Qiongbo Sun and Li Jia
Atmosphere 2023, 14(5), 888; https://doi.org/10.3390/atmos14050888 - 19 May 2023
Viewed by 1084
Abstract
Based on historical tropical cyclone (TC) tracking data and wind data from observation stations, four comparison experiments were designed that considered TC translation speed similarity and five new ensemble schemes in an improved Dynamical-Statistical-Analog Ensemble Forecast (DSAEF) model for Landfalling Typhoon Gale (LTG), [...] Read more.
Based on historical tropical cyclone (TC) tracking data and wind data from observation stations, four comparison experiments were designed that considered TC translation speed similarity and five new ensemble schemes in an improved Dynamical-Statistical-Analog Ensemble Forecast (DSAEF) model for Landfalling Typhoon Gale (LTG), which was tested in terms of forecast capability in South China. The results showed that the improved DSAEF_LTG model with the incorporation of TC translation speed and a new ensemble scheme could improve the forecast threat score (TS) and reduce both the false alarm ratio and the missing ratio in comparison with corresponding values attained before the improvement. The TS of the new ensemble scheme model (DLTG_3) was 0.34 at threshold above Beaufort Scale 7, which was 31% better than that of the unimproved model (DLTG_1). At a threshold above Beaufort Scale 10, the TS of DLTG_3 indicated even greater improvement, reaching 0.25, i.e., 127% higher than that of DLTG_1. The results of the experiments illustrated the marked improvement achievable when using the new ensemble scheme. The reasons for the differences in the DSAEF_LTG model forecasts before and after the introduction of TC translation speed and the new ensemble scheme were analyzed for the cases of Typhoon Haima and Typhoon Hato. Full article
(This article belongs to the Special Issue Forecasting and Modeling of Tropical Cyclones and Their Induced Wind and Precipitation)
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22 pages, 10334 KiB  
Article
Response of Extratropical Transitioning Tropical Cyclone Size to Ocean Warming: A Case Study for Typhoon Songda in 2016
by Ziwei Miao and Xiaodong Tang
Atmosphere 2023, 14(4), 639; https://doi.org/10.3390/atmos14040639 - 28 Mar 2023
Viewed by 1624
Abstract
This study attempts to investigate how future sea surface temperature increases will affect the size (radius of gale-force [17 m s−1] wind at 10 m height; i.e., R17) evolution of tropical cyclones that undergo extratropical transition (ET) through sensitivity [...] Read more.
This study attempts to investigate how future sea surface temperature increases will affect the size (radius of gale-force [17 m s−1] wind at 10 m height; i.e., R17) evolution of tropical cyclones that undergo extratropical transition (ET) through sensitivity experiments of sea surface temperature (SST) for Typhoon Songda (2016) in the northwestern Pacific. Two numerical experiments were carried out, including a control simulation (control) and a sensitivity experiment (SST4.5) with SST increased by 4.5 degrees in the entire domain. The results showed that Songda tended to be stronger and larger with projected higher SSTs. Moreover, the momentum equation for tangential wind was utilized to study the mechanism of R17 evolution in different SST scenarios, in which the radial absolute vorticity flux term played a dominant role in generating a positive tendency of tangential wind. The results indicate that before ET, higher SSTs in the entire domain led to more active rainbands in both inner-core and outer-core regions. As a result, stronger secondary circulation and low-level inflow extended outward, and the absolute angular momentum (AAM) importing from the outer region increased, which led to a larger R17 in SST4.5. During the ET, the peripheral baroclinically driven frontal convection induced extensive boundary layer inflow, which accelerated the tangential flow in the outer frontal region through strong inward AAM transport. However, due to the lower latitude of the cyclone and the strong frontolysis at the outer side of the cold pool in SST4.5, the peripheral frontal convection reached the location of R17 later; thus, the increase in the cyclone size lagged behind that in the control. Full article
(This article belongs to the Special Issue Forecasting and Modeling of Tropical Cyclones and Their Induced Wind and Precipitation)
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12 pages, 3631 KiB  
Article
Comprehensive Analysis of Typhoon Nangka Based on the Satellite Data from the GPM, CloudSat and Himawari-8
by Xiaolin Ma, Ju Wang, Hong Huang, Xuezhong Wang, Zhen Wang and Banghui Hu
Atmosphere 2023, 14(3), 440; https://doi.org/10.3390/atmos14030440 - 23 Feb 2023
Cited by 1 | Viewed by 1272
Abstract
A typhoon or hurricane is one of the most destructive high-impact weather events. In this study, the genesis and development processes of Typhoon Nangka (2015), which occurred over the Western Pacific in 2015, were investigated based on the comprehensive observation data from three [...] Read more.
A typhoon or hurricane is one of the most destructive high-impact weather events. In this study, the genesis and development processes of Typhoon Nangka (2015), which occurred over the Western Pacific in 2015, were investigated based on the comprehensive observation data from three satellites, i.e., the Himawari-8 satellite, the CloudSat satellite and the Global Precipitation Measurement mission satellite (GPM), focusing on the characteristics of typhoon structure, precipitation and cloud. The results (Results) show that during the developing stage of Typhoon Nangka, the cloud system was relatively complex and changed significantly, with large raindrops dominating the precipitation around the eyewall in the first quadrant, and the convection in the eyewall and outer rainband burst upward to 17 km. In addition, three features were obvious: stratiform precipitation was dominant in the inner rainband, both the precipitation type (stratiform or convective) and intensity were distributed unevenly in the outer rainband, and large water content was located in the warm layer of clouds. Moreover, the collision growth and breakup of water droplets tended to be stable. The precipitation in the typhoon eyewall, inner rainband and outer rainband was significantly different; stratiform precipitation mainly occurred in the inner rainband, while convective precipitation mainly appeared in the eyewall and outer rainband. The cloud system was distributed asymmetrically, and the upper-layer and lower-layer clouds were closely related, dominated by single-layer clouds. There were deep convective clouds in the eyewall, and cirrus clouds with the broadest range across the eyewall. The coverage range of cirrus clouds was close to the radius of the typhoon. There were stratocumulus, altostratus and cumulus in the low levels. Full article
(This article belongs to the Special Issue Forecasting and Modeling of Tropical Cyclones and Their Induced Wind and Precipitation)
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