Tropical Cyclone Research and Review最新文献

{"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}

{"title":"Impact of twin typhoons on the upper ocean environment across the Northwest Pacific ocean","authors":"M.V. Subrahmanyam","doi":"10.1016/j.tcrr.2025.02.008","DOIUrl":"10.1016/j.tcrr.2025.02.008","url":null,"abstract":"<div><div>Two typhoons, Saola and Damrey, moved across the Northwest Pacific Ocean (NPO) between July 27, 2012, and August 4, 2012. During this period, the oceanographic response was studied. The study examined variations in Sea Surface Temperature (SST) and Mix Layer Depth (MLD) in response to twin typhoons using satellite data from QuickSCAT wind, reanalysis data from OISST, and Argo data. On August 1, 2012, typhoon Damrey's right side experienced the greatest SST dip of 3.6 °C because of mixing. Typhoon Damrey had an influence on typhoon Saola, which caused a weaker SST cooling of 2.5 °C. During the passage of the twin typhoons, the area around typhoon Saola observed the most noticeable change in MLD, which went from 15 m to 85 m. The Ekman pumping effect led to modifications in the subsurface layer, which improved SST cooling and caused MLD deepening.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 1","pages":"Pages 71-81"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792179","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":"Comparative analysis of kinetic energy budget of Typhoons Yagi and Rumbia","authors":"Guanbo Zhou ,&nbsp;Longsheng Liu ,&nbsp;Bin Huang","doi":"10.1016/j.tcrr.2025.02.004","DOIUrl":"10.1016/j.tcrr.2025.02.004","url":null,"abstract":"<div><div>In this paper, the ERA-Interim reanalysis data at six-hourly intervals (with a horizontal resolution of 0.25° × 0.25°) and typhoon best track datasets are used to conduct a comparative kinetic energy budget analysis of Typhoon Yagi and Rumbia during their lifecycles in 2018. At the same time, the contributions of divergent wind and rotating wind to the kinetic energy budget in different quadrants are analyzed, and the relative importance of the components of rotating wind energy and divergent wind energy represented by Kr and Kd in the kinetic energy budget is studied. Different from the previous kinetic energy budget analysis of the whole target area, this paper studies Kr and Kd in the four quadrants around the typhoon center to reveal their respective contributions to the development of the typhoon. The results show that: (1) On the whole, the rotational wind energy Kr is the largest, and the distribution is relatively consistent with the total kinetic energy K0. (2) The variation trend of divergent wind energy Kd in the lower layer can better reflect the intensity change of TCs. (3) From the comparative analysis of the deviation term vr∗vd, \"Rumbia\" weakens to the lowest in the northwest, while the corresponding northeast direction is the maximum at this time, which corresponds to the beginning of the Northeast turning of \"Rumbia\". (4) Through further analysis and comparison, it is found that T1 and T3 are mainly positive in the low layer, and their contributions mainly come from T1d and T3d.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 1","pages":"Pages 13-26"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792175","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":"The mean and extreme tropical cyclone wave climate throughout the Coral Sea, from 1979 to 2020","authors":"John G. Miller,&nbsp;Guilherme Vieira da Silva,&nbsp;Darrell Strauss","doi":"10.1016/j.tcrr.2025.03.001","DOIUrl":"10.1016/j.tcrr.2025.03.001","url":null,"abstract":"<div><div>Here we present a mean and extreme tropical cyclone (TC)-generated wave climate for the Coral Sea (the oceanic basin east of Australia), for 1979–2020. An available WAVEWATCH III® hindcast model dataset with surface wind forcing from the National Centres for Environmental Prediction supplied Climate Forecasts System Version 2 was used. The resolution of this wind field is 0.3°, increasing to 0.2° from 2011, among the highest available to better represent TC vortices. The spatial and temporal resolution of the wave model was sufficient to produce TC wave climates, although a limitation in representing TCs at both ends of the intensity scale was apparent. Model performance was validated using wave buoy data at three coastal locations. The area near the Tropic of Capricorn, around 155° E, experienced the largest TC-generated mean waves with the locations of the primary swell height maxima shifted slightly north-west, in comparison with combined waves. There was an interdecadal increase (decrease) in TC significant wave height with positive (negative) IPO phase in three of the four decades. TC extreme wave maxima were situated further east, compared to the TC generated mean waves. The 50 and 100-year average return intervals indicated high extreme waves near the northeastern tip of Australia and northwest of New Caledonia. For the east Australian coast, extreme waves from TCs showed a decreasing trend in the south only. This study presents a unique mapping of TC wave characteristics over the entire Coral Sea and validates the use of a globally applicable method, for such applications.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 1","pages":"Pages 40-59"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792176","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":"Assessment of an Extreme Heavy Rainfall over Meghalaya, India on 16th& 17th June 2022: A case study using Meteorological and Remote Sensing observations","authors":"Sunil Kumar ,&nbsp;Shashi Kant ,&nbsp;Rizwan Ahmed","doi":"10.1016/j.tcrr.2025.02.007","DOIUrl":"10.1016/j.tcrr.2025.02.007","url":null,"abstract":"<div><div>The investigation of extreme weather phenomena is an important scientific field that incorporates multiple disciplines due to their significant impacts on various sectors and society as a whole. There is evidence to support the occurrence of severe weather events in different regions of the world. In India, major precipitation events, such as the Mumbai floods in 2005 and the Kerala floods in 2018, often occur during the south-west monsoon season, leading to significant impacts. This statement is also applicable to states like Arunachal Pradesh, Nagaland, and Manipur in the northeast region. Intense precipitation events caused significant impacts in Meghalaya from June 16th to June 18th, 2022.These events resulted in a significant socio-economic and human impact, including infrastructure damage, displacement of communities, and around 90 reported fatalities in Assam and Meghalaya. These two states are susceptible to flooding and erosion, and they consistently face intense and periodic floods on an annual basis. Analysis of the 2022 rainfall data reveals that over 4.8 million individuals were adversely impacted in the majority of districts in Assam and Meghalaya. Specifically, 79 % of the total 43 districts were affected.</div><div>This study analyzed the meteorological aspects of exceptional heavy rainfall events in Meghalaya from June 16th to 18th, 2022, to understand their significant impact on the environment and society. The extreme weather event in Meghalaya was a result of a well-marked low pressure area, a significant influx of moisture from the Bay of Bengal (BoB), favourable dynamics and thermodynamics conditions, and a supportive cloud top temperature (CTT) that collectively intensified the heavy rainfall. The study's findings can provide valuable insights for disaster managers and forecasters, enabling them to better prepare for and respond to extreme rainfall events in the northeast region of the country.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 1","pages":"Pages 60-70"},"PeriodicalIF":2.4,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792178","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":"A method to automatically ascertain the identities of tropical cyclones in tropical cyclone warning messages","authors":"Rijin Wan ,&nbsp;Mengqi Yang","doi":"10.1016/j.tcrr.2024.11.003","DOIUrl":"10.1016/j.tcrr.2024.11.003","url":null,"abstract":"<div><div>In the operational forecasting of tropical cyclones (TCs), decoding TC warning messages from global centers, along with extracting, organizing, and storing useful track observations and forecasts, are fundamental tasks. The technical core lies in accurately identifying distinct TC individuals through automated programming methods. Based on the statistical characteristics of historical distances between TC individuals, this study designs a novel method for automatic identification of TC individuals and establishes a database of TC track observations and forecasts by integrating the persistent features from various elements in TC warning messages. This method accurately identifies each TC individual and assigns it a unique database number through a two-step process: initially, through the 'Same Center same Number Comparison(SCNC)' identification method, followed by the 'Spatio-Temeporal Distance Comparison(STDC)' identification method.On this basis, we obtain a well-organized and comprehensive dataset that covers entire TC life time. Over the past decade,the operational practice has demonstrated that this method is accurate and efficient, providing solid data support for the TC forecasting operation, the assessment of TC forecasting accuracy, the compilation of TC yearbook, and TC-related research.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"13 4","pages":"Pages 286-292"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307919","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":"Assessing global ensemble systems’ forecasts of tropical cyclone genesis in differing environmental flow regimes in the western North Pacific","authors":"Yasuhiro Kawabata ,&nbsp;Munehiko Yamaguchi ,&nbsp;Hironori Fudeyasu ,&nbsp;Ryuji Yoshida","doi":"10.1016/j.tcrr.2024.11.007","DOIUrl":"10.1016/j.tcrr.2024.11.007","url":null,"abstract":"<div><div>The forecast probability of tropical cyclone (TC) genesis in the western North Pacific from 2017 to 2020 was investigated using global ensembles from the Japan Meteorological Agency (JMA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the U.S. National Centers for Environmental Prediction (NCEP), and the Met Office in the United Kingdom (UKMO). The time of TC genesis was defined as the time the TCs were first recorded in the best-track data (Case 1) and as the time they reached the intensity of a Tropical Storm (Case 2). The results in Case 1 showed that differences between the forecast probability based on each global ensemble were large, even for a 1-day forecast, and that mean probability were from 18 % to 74 %. The forecasts based on the NCEP had a large frequency bias and overpredicted TC genesis events. The results indicated that the representation of genesis events differed greatly between global ensembles. The effectiveness of multiple ensembles was investigated. The results from the threat score and the false alarm ratio indicated that multiple ensembles had skillful forecasts. When the forecast probability was examined for environmental patterns of synoptic low-level flow, the mean 5-day forecast probability was highest for the pattern in the confluence region. The results also showed that the forecast probability was much larger in Case 2 than in Case 1. In all global ensembles, the mean probability with a lead time of up to 1-week was below 10 % for both Case 1 and 2. This result indicates that even with today's operational forecasting systems, it is difficult to regularly predict TC genesis events with a 1-week lead time with high confidence. These results provide a better understanding of TC genesis forecast products in each global ensemble and will be useful information when multiple-ensemble products are created.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"13 4","pages":"Pages 344-355"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143355039","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":"Development of strong asymmetric convection leading to rapid intensification of tropical cyclones","authors":"Jeff Callaghan","doi":"10.1016/j.tcrr.2024.11.006","DOIUrl":"10.1016/j.tcrr.2024.11.006","url":null,"abstract":"<div><div>Recent intensifying tropical cyclones around the globe are analysed to examine the observed winds structure in their inner core. The winds in sectors with strong bands of thunderstorms were observed from analysed vector winds in weather forecasting computer models to turn in an anticyclonic fashion from the 850 hPa level up to the 500 hPa level. This wind structure resembles Quasi-Geostrophic warm air advection and from Hysplit the trajectory analysis was in areas of ascending air currents suitable for the initiation of thunderstorms. The rapid intensification occurred as the cyclonic circulation extends up to at least 200 hPa.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"13 4","pages":"Pages 239-260"},"PeriodicalIF":2.4,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143307916","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}