Tropical Cyclone Research and Review最新文献

{"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":"2025-04-12","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}

{"title":"Assessment of the impacts of Super Typhoon Saola and the record-breaking rainstorm due to the remnant of Severe Typhoon Haikui on Hong Kong in September 2023","authors":"Yuk Sing Lui,&nbsp;The Hong Kong Federation of Insurers,&nbsp;Andy Wang-chun Lai,&nbsp;Chun-wing Choy,&nbsp;Tsz-cheung Lee","doi":"10.1016/j.tcrr.2025.04.002","DOIUrl":"10.1016/j.tcrr.2025.04.002","url":null,"abstract":"<div><div>In early September 2023, Hong Kong was severely impacted by the ferocious strike of Super Typhoon Saola on 1–2 September and the phenomenal rainstorm on 7–8 September triggered by the remnant of TC Haikui. Given the rarity of these two successive extreme weather events which wreaked havoc in Hong Kong within 10 days, impact assessment on the damage and economic loss in Hong Kong due to these two extreme events was conducted. Utilizing available data from government reports, media, surveys, and insurance claims, the direct economic losses incurred by Super Typhoon Saola on 1–2 September and the record-breaking rainstorm on 7–8 September were estimated to be around HK$0.48 billion and HK$1.74 billion respectively. Moreover, the impacts of Saola and the record-breaking rainstorm in September 2023 are compared with other super typhoons and Black Rainstorm events in Hong Kong mainly in the last decade for reference. It is noted that, when compared with the Super Typhoons Hato and Mangkhut which also necessitated the issuance of Hurricane Signal No. 10 in Hong Kong respectively in 2017 and 2018, the overall impact of Saola in 2023 was less than those of Hato and Mangkhut. In terms of rainstorm events, the impact of the Black Rainstorm event on 7–8 September 2023 was significantly higher than those of the Black Rainstorm events in March 2014 and June 2020. The possible attributing factors related to the differences in the impact of these super typhoon and rainstorm events were also briefly discussed.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 2","pages":"Pages 158-169"},"PeriodicalIF":2.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138233","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":"Tropical cyclone activities in the western North Pacific in 2023","authors":"Xin Huang ,&nbsp;Johnny C.L. Chan ,&nbsp;Lina Bai ,&nbsp;Zifeng Yu ,&nbsp;Tingting Sun","doi":"10.1016/j.tcrr.2025.04.001","DOIUrl":"10.1016/j.tcrr.2025.04.001","url":null,"abstract":"<div><div>Using the best-track dataset from the Shanghai Typhoon Institute/China Meteorological Administration, the paper presents a detailed summary and analysis of tropical cyclone (TC) activities in the Western North Pacific (WNP) and the South China Sea (SCS) during 2023. Based on historical records from 1951 to 2020 as the climatology benchmark, we examine anomalies in TC frequency, origin locations, tracks, intensity, and duration, as well as landfall events across the Asia-Pacific region. TC frequency in 2023 is found to be lower than climatology, with a marked decrease during the autumn months. Origin locations of TCs, which mark the starting points of their paths, are generally consistent with climatology, although there is a noticeable northwestward shift in the origins of the intense TCs. Track density of named TCs is anomalously high within the 0–20°N and 110°E to 125°E longitude box, and offshore areas covering northwestern to southern Japan and around the Korean Peninsula. Comparisons of the means, medians, upper and lower quartiles all indicate that TC intensity is generally stronger than usual, with 8 out of 17 named TCs reaching super typhoon status. The duration of TCs maintaining tropical storm intensity or above also surpasses climatological norms. In terms of landfall, 6 TCs made landfall in China, totaling 11 events, while 11 TCs accounted for 20 landfall instances across the Asia-Pacific. The key anomalous annual TC activities are influenced by atmospheric and oceanic conditions modulated by a concurrent El Niño event, a positive North Pacific Mode, a negative Pacific Meridional Mode on the interannual scale, and the negative Pacific Decadal Oscillation phase and positive Atlantic Multidecadal Oscillation phase on the interdecadal scale.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 2","pages":"Pages 145-157"},"PeriodicalIF":2.4,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138232","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":"Can one reconcile the classical theories and the WISHE theories of tropical cyclone intensification?","authors":"Roger K. Smith ,&nbsp;Michael T. Montgomery ,&nbsp;Shanghong Wang","doi":"10.1016/j.tcrr.2025.02.002","DOIUrl":"10.1016/j.tcrr.2025.02.002","url":null,"abstract":"<div><div>An effort is made to reconcile the classical balance theories of tropical cyclone intensification by Shapiro and Willoughby and Schubert and Hack and the various prognostic (or WISHE-) theories of Emanuel. As a start, it proves insightful to extend the classical theories to account for explicit latent heat release in slantwise ascending air. While such an effort uncovers enroute a range of old modelling issues concerning the representation of deep convection in a balance framework, the analysis provides a new perspective on these issues. The bottom line is that the two theories cannot be reconciled.</div><div>The behaviour of the classical model with explicit latent heat release included is illustrated by a particular calculation starting with an axisymmetric vortex in a conditionally-unstable atmosphere. As soon as condensation occurs aloft, the moist Eliassen equation for the overturning circulation becomes hyperbolic in the convectively-unstable region and the model cannot be advanced forwards beyond this time unless the Eliassen equation is suitably regularized to remove these hyperbolic regions. However, regularization suppresses deep moist convection, leaving no mechanism to reverse the frictionally-induced outflow in the lower troposphere required to concentrate absolute angular momentum there. For this reason, the initial vortex spins down, even following the formation of elevated cloud with the accompanying latent heat release.</div><div>The fact that the flow configuration in the explicit moist version of the classical theories is similar to that in the WISHE theories raises several fundamental questions concerning the physics of vortex spin up in the WISHE theories, calling into question the utility of these theories for understanding tropical cyclone intensification in nature.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 2","pages":"Pages 105-118"},"PeriodicalIF":2.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138229","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":"Rapid intensification of the Super Cyclone Amphan: Environmental drivers and its future projections","authors":"R.S. Akhila,&nbsp;J. Kuttippurath,&nbsp;A. Chakraborty,&nbsp;N. Sunanda,&nbsp;R. Peter","doi":"10.1016/j.tcrr.2025.02.005","DOIUrl":"10.1016/j.tcrr.2025.02.005","url":null,"abstract":"<div><div>Tropical cyclones are intense weather systems that originate over warm tropical oceans and they alter the dynamical, chemical, and biological state of the oceans. Here, the reasons for the rapid intensification of Super cyclone Amphan that occurred in May 2020 in the Bay of Bengal (BoB) are thoroughly investigated. One of the main causes for the intensification of Amphan into a super cyclone is the rise in sea surface temperature (SST). Additionally, the warm-core eddies present in the track of cyclones also contributed to its rapid intensification. The Tropical Cyclone Heat Potential (TCHP) and Upper Ocean Heat Content (OHC) were consistent and remained high throughout the cyclone period to maintain its high intensity. Although there were greater cyclone-induced cold wakes during the period, the background SST conditions were still higher and were favourable for the cyclone to intensify further. The vertical wind shear in both shallow and deep layers was minimal, which further helped the formation of a stable and strong cyclonic vortex, and thus contributed to its rapid intensification. The behaviour of cyclone Amphan in future scenarios is analysed using a coupled atmosphere-ocean model. Compared to the current scenario, the severity of cyclones is expected to increase in the future (RCP 8.5). Early landfall is observed in the case of RCP 4.5. As a result of elevated UOHC, Amphan attains more strength in the RCP 8.5 than it does in the present scenario. The translational speed increases in the future, which makes the cyclone move faster. Due to the passage of Amphan, there is a reduction in UOHC, which is higher in the case of a future warm climate. This suggests that additional energy from the ocean is transferred to the atmosphere, causing the cyclone to intensify further. According to the results from the coupled atmosphere-ocean model, the future warm atmospheric and oceanic conditions will be more favourable for the genesis and development of stronger cyclones.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 1","pages":"Pages 27-39"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble deep learning models for tropical cyclone intensity prediction using heterogeneous datasets","authors":"Dikshant Gupta,&nbsp;Menaka Pushpa Arthur","doi":"10.1016/j.tcrr.2025.02.001","DOIUrl":"10.1016/j.tcrr.2025.02.001","url":null,"abstract":"<div><div>The prediction of the Tropical Cyclone (TC) intensity helps the government to take proper precautions and disseminate appropriate warnings to civilians. Intensity prediction for TC is a very challenging task due to its dynamically changing internal and external impact factors. We proposed a system to predict TC intensity using CNN-based ensemble deep-learning models that are trained by both satellite images and numerical data of the TC. This paper presents a thorough examination of several deep-learning models such as CNN, Recurrent Neural Networks (RNN) and transfer learning models (AlexNet and VGG) to determine their effectiveness in forecasting TC intensity. Our focus is on four widely recognized models: AlexNet, VGG16, RNN and, a customized CNN-based ensemble model all of which were trained exclusively on image data, as well as an ensemble model that utilized both image and numerical datasets for training. Our analysis evaluates the performance of each model in terms of the loss incurred. The results provide a comparative assessment of the deep learning models selected and offer insights into their respective prediction loss in the form of Mean Square Error (MSE) as 194 in 100 epochs and execution time 1229 s to forecasting TC intensity. We also emphasize the potential benefits of incorporating both image and numerical data into an ensemble model, which can lead to improved prediction accuracy. This research provides valuable knowledge to the field of meteorology and disaster management, paving the way for more resilient and precise TC intensity forecasting models.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 1","pages":"Pages 1-12"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of operational extended range forecast of cyclogenesis over the north Indian Ocean","authors":"M. Sharma ,&nbsp;M. Mohapatra ,&nbsp;P. Suneetha","doi":"10.1016/j.tcrr.2025.02.006","DOIUrl":"10.1016/j.tcrr.2025.02.006","url":null,"abstract":"<div><div>The performance of the operational extended range forecast issued by the India Meteorological Department in the nil, low, moderate and high categories of probability of cyclogenesis has been evaluated based on 868 forecasts issued every Thursday for week 1 and week 2 for the Arabian Sea (AS), Bay of Bengal (BoB) and north Indian Ocean (NIO) as a whole during April 2018 to December 2023. The forecast is biased towards under-warning for low and moderate categories over the NIO, BoB &amp; AS and towards over-warning for high categories over NIO and BoB in week 1. It is biased towards over-warning for moderate &amp; high categories and under-warning for low category forecast over NIO and BoB for week 2. It is biased towards under-warning for low and high categories and over-warning for moderate category forecasts over AS in week 2. The Brier score (Brier skill score) for week 1 and week 2 are 0.051 (48.7 %) and 0.087 (8.6 %) over NIO respectively.</div><div>The association of Madden Julian Oscillation (MJO), equatorial Rossby waves (ERW) and Kelvin waves (KW) with genesis increases and that of low-frequency background waves (LW) and inter-tropical convergence zone (ITCZ) decreases with an increase in the intensity of storms from depression to very severe cyclonic storms (VSCS). About 100 %, 92 %, 92 %, 92 % and 100 % of the cases of the genesis of VSCS &amp; above category storms over the NIO are associated with stronger westerlies to the south, stronger easterlies to the north, convective phase of MJO, ERW and KW over the region of genesis.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 1","pages":"Pages 82-103"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}