Tropical Cyclone Research and Review最新文献

{"title":"From vulnerability to resilience: Addressing the causes, impacts, and solutions for recurrent flash floods in the Philippines","authors":"Alice T. Rivera ,&nbsp;Jim Boy G. Dela Vega","doi":"10.1016/j.tcrr.2025.08.005","DOIUrl":"10.1016/j.tcrr.2025.08.005","url":null,"abstract":"<div><div>The Philippines is among the most disaster-prone nations. Its islands are frequently hit by floods, typhoons, landslides, earthquakes, volcanic eruptions, and droughts due to their location along major tectonic plates and in a typhoon belt .The country is blessed with oceans, rivers, lakes, and streams, but weather events can release massive amounts of water, causing flooding. This study investigates Philippine flash floods, their sources, effects, and solutions. It focuses on major floods, including those caused by both typhoons and monsoons, as well as susceptible locations and ASEAN prevention methods.</div><div>Employing a descriptive-evaluative research design, this study aligns with Ary's notion that descriptive research seeks to capture the current state of affairs. It entails data mining from reliable, pertinent sources to inform the study's outcomes. Analysis reveals that recurrent flash floods pose a considerable risk across all Philippine regions, with Region III standing out as particularly susceptible. The catastrophic flooding induced by Super Typhoon Yolanda marked the most severe flooding event in the Philippines between 2010 and 2020. However, monsoon-induced floods also significantly contributed to annual flooding, particularly in highly urbanized and coastal areas.</div><div>Attention and resources should be prioritized for Northern Luzon, notably Regions I, III, IV-A, IV-B, and CAR, which exhibit a high frequency of flash flood recurrences. Implementing actionable flood risk information and robust flood warning systems, reinforcing drainage infrastructure, allocating budgets for flood prevention initiatives, promoting tree planting, and adopting Cambodia's HYDRA Floods approach represent viable flood prevention measures tailored for the Philippines' flood-prone regions.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 301-310"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges in forecasting super typhoon Yagi (2024)","authors":"Yumei Li ,&nbsp;Johnny CL. Chan ,&nbsp;Xun Li ,&nbsp;Wen Feng ,&nbsp;Yu Zhang","doi":"10.1016/j.tcrr.2025.08.007","DOIUrl":"10.1016/j.tcrr.2025.08.007","url":null,"abstract":"<div><div>Yagi (2024) is another super typhoon that made landfall in 2024 in Wenchang, Hainan, following Rammasun (2014), with its destructive power setting a new historical record for typhoon disasters in Hainan. Based on multi-source observational data and numerical model forecast results, we present the distinctive characteristics of Supertyphoon Yagi and discuss the huge challenges in the operational forecasting of its track and intensity. Yagi underwent explosive intensification over the northeastern South China Sea, with wind speeds increasing by 28 m s⁻¹ within 24 h—meeting the criteria for extreme rapid intensification (ERI). It maintained supertyphoon intensity for 67 h, with hurricane-force winds (&gt;32.7 m s⁻¹) persistently affecting Hainan's land area for approximately 10 h. These characteristics exceeded those of Supertyphoon Rammasun and pose great challenges in operational forecasting. Operational numerical weather prediction (NWP) models show marked disagreements in predicting the track of Yagi in the early stage. In the later period, while the ECMWF and Pangu model predictions suggest landfall over northeastern Hainan, those of the NCEP-GFS and three CMA models maintain a landfall over the Leizhou Peninsula. Given the fact that historically, the landfall probability in Guangdong is much higher than that in Hainan, such discrepancies considerably increased the difficulty in determining the landfall location. The forecasting of Yagi’s intensity also poses a substantial challenge because of the rare occurrence of supertyphoon landfall cases in Hainan and the underpredicted intensities from the NWP models.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 317-322"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of artificial intelligence application in typhoon forecasting","authors":"Xinyuan Bi ,&nbsp;Jinping Liu ,&nbsp;Yihong Duan","doi":"10.1016/j.tcrr.2025.07.005","DOIUrl":"10.1016/j.tcrr.2025.07.005","url":null,"abstract":"<div><div>As global climate warming intensifies, the frequency and intensity of typhoon (tropical cyclone) have become increasingly uncertain, posing significant challenges to human society. Traditional typhoon forecasting methods, while having made remarkable progress over the past few decades, still face numerous limitations in handling complex meteorological data and providing accurate predictions. In recent years, the rapid development of artificial intelligence (AI) technologies has brought new opportunities to the field of typhoon forecasting and is revolutionizing typhoon forecasting by improving the accuracy of track and intensity predictions. This paper reviews the applications of AI models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in typhoon forecasting and analyzes the performance of AI models in 2024 by comparing them with traditional numerical models like the European Centre for Medium-Range Weather Forecasts (ECMWF, TC). A case study of Typhoon Gaemi demonstrates AI’s capabilities and limitations. The study highlights AI’s advantages, challenges, and future recommendations for enhancing typhoon prediction system.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 230-236"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response of aquatic organisms as an eco-biotic indicator with response to cyclonic intervention in the large river system: A case study of river Ganga, India, during cyclone YAAS","authors":"Basanta Kumar Das ,&nbsp;Nitish Kumar Tiwari ,&nbsp;Trupti Rani Mohanty ,&nbsp;Shreya Roy ,&nbsp;Archisman Ray ,&nbsp;Supriti Bayen ,&nbsp;Subhadeep Das Gupta ,&nbsp;Kausik Mondal ,&nbsp;Himanshu Sekhar Swain ,&nbsp;Raju Baitha ,&nbsp;Mitesh Hiradas Ramteke ,&nbsp;Canciyal Johnson ,&nbsp;Thangjam Nirupada Chanu ,&nbsp;Manisha Bhor","doi":"10.1016/j.tcrr.2025.08.001","DOIUrl":"10.1016/j.tcrr.2025.08.001","url":null,"abstract":"<div><div>Cyclonic interferences can adversely affect the riverine ecology and ecological niche of many aquatic organisms. The present study evaluates the impact of the cyclonic storm “Yaas” on the different abiotic as well as biotic variables (Plankton, Fish, and Benthos) of the river Ganga. In the study, it was observed that cyclones have affected the riverine water quality, as prior to Yaas the calculated “National Sanitation Foundation” - Water Quality Index was 70.52 and during the Yaas period, it reduced to 52.8, while, the observed value during the post-Yaas period was 68.2. The phytoplankton density varied from pre-Yaas period (6284 cell⁻¹) to Yass (670 cell⁻¹) and finally during post-Yaas period (196 cell⁻¹). Contrary to phytoplankton, zooplankton responded favorably as its density increased from pre-Yaas period (196 cell⁻¹) to Yaas period (370 cell⁻¹), and during the post-Yaas (24 cell⁻¹). The Fish and Benthic organisms also showed similar responses as to zooplankton.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 249-269"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the physics of a new time-dependent theory of tropical cyclone intensification","authors":"Roger K. Smith ,&nbsp;Michael T. Montgomery","doi":"10.1016/j.tcrr.2025.08.004","DOIUrl":"10.1016/j.tcrr.2025.08.004","url":null,"abstract":"<div><div>Recent studies (Wang et al. 2021; Li et al. 2024) propose a new time-dependent theory for tropical cyclone intensification. Here, we examine the physics of this new theory and point out that intensification in the model has to be the result of an unspecified source of absolute angular momentum. For this reason, we are led to question the physical integrity of the theory. We question also the methodology seeking to tune the unknown parameters introduced in the theory.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 297-300"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting the frequency and magnitude of hurricanes in the Yucatan Peninsula, Mexico, in the period from 2025 to 2034 using convolutional neural networks (CNNs), Long Short-Term Memory networks (LSTMs) and statistical models","authors":"Hermes De Gracia ,&nbsp;Jorge Celeron ,&nbsp;Consuelo Diaz ,&nbsp;Aristeo Hernandez ,&nbsp;Victoria Serrano","doi":"10.1016/j.tcrr.2025.07.006","DOIUrl":"10.1016/j.tcrr.2025.07.006","url":null,"abstract":"<div><div>Climate change has significantly increased the frequency and severity of extreme weather events, a trend recognized under the United Nations Sustainable Development Goal 13: Climate Action. This study forecasts hurricane activity in the Yucatan Peninsula, Mexico, for the period 2025–2034 using advanced computational models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Autoregressive Integrated Moving Average models (ARIMA), and Linear Regression (LR). Historical hurricane data were extracted from the HURDAT2 database kept by the National Hurricane Center (NHC) and spatially analyzed in QGIS to assess storm trajectories and wind intensities.</div><div>The data were processed using Python, and each model was trained to predict hurricane frequency within three wind speed categories: &lt;50 knots, 50–100 knots, and &gt;100 knots. Results reveal divergent performance among the models. CNN exhibited high variability for low-speed events, peaking at 4.21 events in 2027 and dropping to 1.27 by 2034. In contrast, LSTM and ARIMA maintained stable forecasts: LSTM fluctuated between 2.7 and 3.0, and ARIMA ranged from 1.5 to 1.8. For the 50–100 knot range, CNN reached an anomalous high of 8.14 events in 2032, while LSTM and ARIMA remained within narrower bands (1.85–2.01 and 1.32–1.99, respectively). At the &gt;100 knot level, ARIMA showed a rising trend from 0.21 in 2025 to 0.57 in 2034, suggesting a potential increase in high-intensity cyclones.</div><div>These findings emphasize the need for adaptive forecasting systems that account for nonlinear behavior under climate change conditions.</div><div>The model outputs offer valuable insights for risk management, contingency planning, and infrastructure resilience in the hurricane-prone Yucatan Peninsula.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 237-248"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The influence of El Niño-Southern Oscillation (ENSO) on the characteristics of tropical cyclones in Indonesia waters","authors":"Kadek Krisna Yulianti ,&nbsp;Nining Sari Ningsih ,&nbsp;Rima Rachmayani ,&nbsp;Eko Prasetyo","doi":"10.1016/j.tcrr.2025.08.002","DOIUrl":"10.1016/j.tcrr.2025.08.002","url":null,"abstract":"<div><div>Indonesia, bordered by the Indian and Pacific Oceans, is influenced by tropical cyclone (TC) activity and phenomena such as the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). This study examines TC variations across four Indonesian regions, focusing on ENSO events when the IOD is neutral. Over the past 50 years (1973–2022), 2,885 tropical cyclones (TCs) have passed through Indonesian waters, with the most active area being Region 2. During El Niño, TC occurrences across Indonesia increase, while La Niña sees fewer TCs overall but with regional variations. Region 2 experiences a 12.5 % monthly decrease in TCs during La Niña due to less favourable environmental factors like vertical wind shear (VWS) and vorticity. Conversely, Regions 3 and 4 show increases of 38.1 % and 45.7 %, respectively, attributed to supportive conditions such as sea surface temperature and humidity. Accumulated Cyclone Energy (ACE) analysis reveals significant changes, with increases of 65.4 % in region 1 and 12.4 % in region 2 during El Niño, while region 2 decreases by 35.3 % during La Niña. Kernel Density Estimation (KDE) highlights seasonal and ENSO-driven spatial shifts, with density centres generally moving eastward during La Niña, except for region 2, which shifts westward. The largest shift, 632.1 km, occurred in region 4 during La Niña, moving TC formations from near West Nusa Tenggara to the Timor Sea. Analysis of Significant Wave Height (SWH) during ENSO periods for each tropical cyclone event in different regions shows that shifts in density centers during El Niño and La Niña also influence SWH variability in Indonesian waters. These findings underscore the impact of ENSO on TC activity in Indonesian waters, providing valuable insights for improving preparedness and marine safety.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 270-286"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A microwave-based machine learning approach for predicting eyewall replacement cycles","authors":"Lorenzo Pulmano","doi":"10.1016/j.tcrr.2025.07.001","DOIUrl":"10.1016/j.tcrr.2025.07.001","url":null,"abstract":"<div><div>Eyewall replacement cycles (ERCs) greatly increase the destructive potential of tropical cyclones (TCs) by affecting the maximum wind speed, wind field size, and storm surge severity while simultaneously reducing confidence in TC forecasts, most prominently in intensity forecasting. Machine learning (ML) presents new opportunities to improve current forecasting and predictive capabilities, and its application will benefit forecasters and ultimately the public. The objective of this project was to create a proof-of-concept ML convolutional neural network (CNN) to predict ERCs using the 89 GHz microwave band for training and testing. The training set was comprised of North Atlantic basin (NATL) storms from 1999 to 2009. The testing set included NATL storms from 2019 to 2022. Twelve models were created, together known as the CNN Ensemble for Predicting Eyewall Replacement Cycles (CE-PERCY), with each individual member achieving at least 80 % in-training accuracy. Two versions were created: versions A and B. Using synthetic aperture radar, land-based radar, aircraft reconnaissance, Microwave-based Probability of ERC (M-PERC), National Hurricane Center reports, and microwave imagery, ERC analysis was conducted on the testing set. 28 ERCs were identified throughout 14 hurricanes from 2019 to 2022. CE-PERCY performs well for a proof-of-concept, with versions A and B predicting 21 and 23 ERCs, respectively. This project successfully introduces a foundation for using ML CNNs in ERC prediction, demonstrates the viability of the technique, and proves that a large enough dataset of microwave imagery can be used in this specific application.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 171-184"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Typhoon science meets artificial intelligence: A roundtable on bridging physics-based and data-driven paradigms","authors":"Zeyi Niu ,&nbsp;Zhe-Min Tan ,&nbsp;Hui Yu ,&nbsp;Jian-Feng Gu ,&nbsp;Guomin Chen ,&nbsp;Wei Huang","doi":"10.1016/j.tcrr.2025.08.006","DOIUrl":"10.1016/j.tcrr.2025.08.006","url":null,"abstract":"<div><div>This paper summarizes the ‘Artificial Intelligence (AI) + Typhoon’ session and a subordinated roundtable forum at the 21st China National Workshop on Tropical Cyclones (NWTC-XXI, 16–18 April. 2025, Changsha China), highlighting recent advances in AI techniques for typhoon monitoring, forecasting, and impact assessment, as well as the deep integration of state-of-the-art artificial-intelligence weather prediction (AIWP) models with traditional physics-based numerical weather prediction (NWP) models. Key insights from the round-table forum are synthesized, emphasizing the strengths, limitations, and future development directions for AI models in typhoon forecasting. As a forward-looking perspective, we should be ready for embracing the emerging AI for research (AI4R) paradigm to advance typhoon science and technology.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 311-316"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the impact of climate change on land-falling tropical cyclones (LFTCs) over the North Indian Ocean (NIO) and their effects on coastal agriculture in Maharashtra: A case study","authors":"Shahenaz Mulla ,&nbsp;Sudhir Kumar Singh ,&nbsp;Rizwan Ahmed","doi":"10.1016/j.tcrr.2025.04.003","DOIUrl":"10.1016/j.tcrr.2025.04.003","url":null,"abstract":"<div><div>The intensity of tropical cyclonic storms formed over the North Indian Ocean (NIO) has increased over the last two decades. The increasing severity of cyclonic storms has serious socioeconomic and agricultural consequences. Many people are concerned about the impact of global warming caused by climate change on extreme weather events, such as the frequency and intensity of Tropical Cyclones (TCs) that form over global ocean basins. High-intensity cyclones have become more common in the NIO, posing significant risks and vulnerability to coastal communities.</div><div>The World Meteorological Organization (WMO) reported that the warmest year was 2015–21, and the warmest decade was 2011–2020, which could be attributed to increased levels of greenhouse gases. However, few studies on the impact of climate change on various characteristics of Land-falling Tropical Cyclones (LFTCs) between 2001 and 2021 have been conducted. As a result, we performed an analysis to evaluate the impact of climate change on various characteristics of LFTCs, such as TC patterns, eye scenes, over land duration, Land-falling intensity (LFI) of LFTCs formed between the years 2000 and 2021. TCs formed over the NIO (2001–2021) crossed the coast with higher LFI and have shown a significant increasing trend in current intensity. Furthermore, more overland duration, eye-pattern TCs, and eye scenes were observed between 2000 and 2021.</div><div>This study also assessed the impact of Severe Cyclonic Storm (SCS) Nisarga on coastal agriculture of Maharashtra in terms of vegetation, and shoreline dynamics. The Nisarga’s landfall caused huge socioeconomic as well as agricultural damages including torrential rainfall, storm surges, and saltwater intrusion, causing biodiversity loss and prolonged soil degradation. Normalized differential vegetation index (NDVI) and Enhanced Vegetation Index (EVI) indices revealed a sharp decline in vegetation health during post-cyclone with slow recovery in the subsequent months. The findings of this study could be used to improve the accuracy of operational forecasting of TCs over the North Indian Ocean basins. The results also highlight the need for targeted coastal management, including mangrove restoration and adaptive agricultural strategies, to enhance resilience against future LFTCs.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 2","pages":"Pages 132-144"},"PeriodicalIF":2.4,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}