Tropical Cyclone Research and Review最新文献

{"title":"Evaluation of Rainfall Characteristics and Intensity of Tropical Cyclones from Global and Regional Reanalysis Products Over the North Indian Ocean","authors":"Satya Prakash,&nbsp;M. Mohapatra","doi":"10.1016/j.tcrr.2026.07.003","DOIUrl":"10.1016/j.tcrr.2026.07.003","url":null,"abstract":"<div><div>Accurate estimation and prediction of rainfall during the tropical cyclones (TCs) are vital for disaster preparedness and inundation modeling. Reanalysis products provide an opportunity to understand the physical mechanism of rainfall variations associated with TCs through several surface, sub-surface and environmental parameters. This study evaluates mean rainfall characteristics from one of the best global reanalysis products – ERA5 and one of the best regional reanalysis products for the South Asian monsoon region – Indian Monsoon Data Assimilation and Analysis (IMDAA) for different intensity categories of TCs over both basins of the North Indian Ocean (NIO) during pre- and post-monsoon seasons against a merged satellite-gauge rainfall product. This analysis has been done for 41 TCs formed over the NIO between October 2015 and December 2023 due to availability of the reference rainfall data. Although mean TC rainfall patterns are captured by both reanalysis products, they underestimate intense mean rainfall over both basins of the NIO for all TC intensity categories. Both reanalysis products underestimated mean rainfall by about 50% in TC inner core region during intensification stage. IMDAA showed 5–15% improvement in error than ERA5 in daily peak rainfall estimation over both basins of the NIO, except for the post-monsoon TCs over the Arabian Sea. As TC intensity is one of the major factors determining rainfall characteristics and associated hazards, TC intensity computed through the maximum sustained wind (MSW) and estimated central pressure (ECP) estimates from both reanalyses has also been assessed for these 41 TCs against the best-track data of India Meteorological Department (IMD). IMDAA showed improvement over ERA5 in bias of ECP by 1–3 hPa over the Bay of Bengal, while ERA5 showed better performance than IMDAA for pre-monsoon TCs over the Arabian Sea. The largest error in ECP of 19–20 hPa was observed for post-monsoon TCs over the Arabian Sea by both reanalysis products. Furthermore, IMDAA showed notable improvement in bias and errors over ERA5 in MSW estimation for TCs over both basins of the NIO. Results of this study would be vital for historical analysis of TCs over the NIO using global and regional reanalysis products and also provide a framework to assess projected TC rainfall and associated hazards for disaster risk reduction planning.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 3","pages":"Pages 381-399"},"PeriodicalIF":5.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033141","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":"On the Onset Timing of Tropical Cyclone Rapid Intensification in the HWRF Model: Role of Upper Atmospheric Evolution","authors":"N.D. Manikanta,&nbsp;Sudheer Joseph,&nbsp;A.K. Das,&nbsp;Akhil Srivastava,&nbsp;Arun Sharma,&nbsp;T.M. Balakrishnan Nair,&nbsp;M. Mohapatra,&nbsp;M. Ravichandran,&nbsp;C.V. Naidu","doi":"10.1016/j.tcrr.2026.07.007","DOIUrl":"10.1016/j.tcrr.2026.07.007","url":null,"abstract":"<div><div>The Hurricane Weather Research and Forecasting (HWRF) model has shown significant advancements in predicting tropical cyclone (TC) intensity and intensity changes over the past decades. However, forecasting the rapid intensification (RI) of TC remains challenging, with notable inconsistencies in HWRF cyclic runs. This study investigates the causative factors of RI prediction errors in the HWRF cyclical simulations of two consecutive intense TCs i.e., Super Cyclonic Storm (SuCS) Kyarr and Extremely Severe Cyclonic Storm (ESCS) Maha that occurred over the Arabian Sea (AS) in 2019. Most HWRF cyclic runs predicted delayed RI for Kyarr and early RI for Maha. For Kyarr, RI began at a higher intensity and with a weaker magnitude than observed. For Maha, RI began at a lower intensity and predicted a stronger magnitude than observed. These contrasting performances are attributed to differences in the evolution of upper-level winds, which affected Kyarr and Maha differently. For Kyarr, enhanced upper-level winds increased vertical wind shear, tilting the vortex and disrupting symmetric convection in the inner core. This hindered the formation of an upper-level warm core, ultimately delaying the RI onset in model cyclic runs. In the case of Maha, large-scale deformation steering flows over the Arabian Sea played a key role. Differences in the evolution of upper-level anticyclones resulted in diametrically opposite forecasted tracks. Some initial model cyclic runs for Maha incorrectly predicted a westward track, while later runs successfully captured the sharp eastward recurvature. Kyarr and Maha traveled along the same path with a three-day interval during their intensification phases. The forecasted westward trajectories kept the Maha away from the region of upper ocean cooling (3.5 °C) caused by Kyarr. Consequently, these cyclical runs predicted earlier RI, resulting in higher intensity. This research highlights the necessity of accurately depicting upper-atmospheric dynamics to enhance RI forecasts in coupled models.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 3","pages":"Pages 438-457"},"PeriodicalIF":5.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033159","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":"Operational Experience of Using Artificial Intelligence Global Models for Tropical Cyclone Forecasting in the Western North Pacific and the South China Sea","authors":"C.W. Choy,&nbsp;Y.H. He,&nbsp;W.K. Wong,&nbsp;T.C. Lee,&nbsp;C.K. Ho,&nbsp;H.F. Law,&nbsp;Y.S. Lui,&nbsp;P.W. Chan","doi":"10.1016/j.tcrr.2026.05.001","DOIUrl":"10.1016/j.tcrr.2026.05.001","url":null,"abstract":"<div><div>In 2025, the Hong Kong Observatory introduced, for the first time in operation, artificial intelligence (AI) global weather prediction models for operational forecasting of tropical cyclones (TCs) over its area of responsibility, including the South China Sea and parts of the western north Pacific. This paper documents the first-year operational experience of using such AI models and provides verification results for their track forecasting, temporal consistency and genesis. The AI models are found to have reduced forecast track errors, converge towards the actual tracks earlier, and have higher temporal consistency among successive forecast runs, relative to the selected traditional global numerical weather prediction (NWP) models. When combined the traditional global NWP models with AI models, a grand ensemble is possible to enhance the robustness of tropical cyclone warning service for Hong Kong, supporting earlier and more consistent forecast to facilitate early preparation work of the public and the emergency preparedness parties. While AI models are expected to be an indispensable tool for operational TC warning, their limitations on TC intensity and wind-structure prediction should be acknowledged. It should also be noted that present evaluation covers a single TC season, over a regional subset of TCs and a selected set of AI models; multi-year and multi-basin verification is needed to assess generality.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 3","pages":"Pages 339-347"},"PeriodicalIF":5.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149033164","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":"Coordinate Attention Based Wide & Deep Fusion Network for Typhoon Track Prediction","authors":"Guangxin He ,&nbsp;Jiadian Wang ,&nbsp;Jie Feng ,&nbsp;Jingjia Luo ,&nbsp;Xun Li","doi":"10.1016/j.tcrr.2026.04.009","DOIUrl":"10.1016/j.tcrr.2026.04.009","url":null,"abstract":"<div><div>Accurate tropical cyclone track forecasting is of crucial importance for effective disaster risk management in coastal regions. These storms are recognized as one of nature's most hazardous weather phenomena. However, traditional typhoon track prediction methods face certain limitations in handling complex meteorological data and multi-dimensional features. To improve prediction accuracy, this paper proposes an improved Wide &amp; Deep framework. This framework utilizes a Stacked Two-layer Long Short-Term Memory (S2-LSTM) encoder in the Wide branch to model the sequential features of typhoon tracks, and incorporates a Multi-ConvGRU in the Deep branch to handle three-dimensional meteorological data. Following a dynamic/static feature decomposition, the S2-LSTM encoder processes a subset of dynamic features, effectively integrating typhoon position, wind speed, and rate-of-change information from multiple time steps to better capture the temporal evolution patterns of typhoon movement. Furthermore, a Coordinate Attention (CA) module is introduced, which captures positional dependencies along the vertical and horizontal dimensions within the feature maps through a factorized average pooling operation. This enhances the model's ability in identifying key areas within atmospheric fields, thus ensuring improved spatial precision in typhoon track forecasting. The model's efficacy was assessed with the 2020–2024 typhoon track dataset published by the China Meteorological Administration (CMA) and the ERA5 reanalysis product maintained by the European Centre for Medium-Range Weather Forecasts (ECMWF). The findings indicate how the two introduced enhancements to the model lead to higher precision for deep learning-based forecasts to a certain extent.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 2","pages":"Pages 307-321"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208494","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":"Disaster Risk Reduction and Preparedness for the Increasing Tropical Cyclone Risk – Hong Kong Perspective","authors":"Tsz-cheung Lee,&nbsp;Shuk-mei Tse,&nbsp;Ping Cheung,&nbsp;Tze-shun Tsoi,&nbsp;Yuk-sing Lui,&nbsp;Kai-hou Yip","doi":"10.1016/j.tcrr.2026.04.003","DOIUrl":"10.1016/j.tcrr.2026.04.003","url":null,"abstract":"<div><div>Tropical cyclones (TCs) are one of the most destructive weather systems. Various hazards associated with TCs, such as extreme winds, heavy rain, storm surge and flooding, can incur multiple casualties and significant damages to property and infrastructure of the affected areas. Against the background of climate change, the latest assessments by the Expert Teams of the World Meteorological Organization and Typhoon Committee suggested that the TC intensity, precipitation rate and proportion of intense TCs in the western North Pacific (WNP) are expected to increase in a warming climate. Moreover, the sea level rises together with the projected increase in TC intensity will exacerbate storm surge and coastal inundation risks. Review of some recent studies further supports the findings of increasing TC risk in the WNP under climate change and provides more insights into various related topics, such as poleward migration, landfall intensity, rapid intensification and TC induced precipitation. In the context of extreme weather warnings, the United Nations launched the “Early Warnings for All (EW4All)” initiative in 2022 which is built on four pillars with a view to delivering effective and inclusive multi-hazard early warning systems for everyone on Earth. In Hong Kong, the Hong Kong Observatory (HKO) has implemented various weather and climate services over the past century with a number of the ongoing and recent services echoing with the four pillars of the EW4All initiative, especially for TC and rainstorm related hazards. Some of HKO's exemplary services with a focus on disaster risk reduction and preparedness for TCs are concisely presented in this paper for reference.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 2","pages":"Pages 203-214"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208297","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":"Role of Oceanic Mixed Layer Processes over Meso-Scale Eddies in Impacting the Tropical Cyclones in the Bay of Bengal","authors":"Gopi Krishna Podapati ,&nbsp;Pushpalatha Thadivalasa ,&nbsp;Sreenivas Pentakota ,&nbsp;Anuj Gupta ,&nbsp;K. Debasis ,&nbsp;K. Maneesha ,&nbsp;Stella Jes Varghese","doi":"10.1016/j.tcrr.2026.04.010","DOIUrl":"10.1016/j.tcrr.2026.04.010","url":null,"abstract":"<div><div>Tropical cyclones dynamically interact with mesoscale oceanic features, like warm-core and cold-core eddies, which play a key role in their track and intensification. Warm (cold) core eddies, which are elevated (lowered) sea surface height structures with enhanced (diminished) heat content, respectively, can intensify (weaken) tropical cyclones by serving as a source of warm (cold) ocean water in the path of a storm. Oceanic anti-cyclone (cyclonic) The process of downwelling (upwelling) allows oceanic eddies to increase (reduce) heat content. In this study, we have conducted an Ocean Mixed Layer Heat budget analysis for distinct TCs over Bay of Bengal. Further we have examined the coherence between distinct ocean mixed layer budget terms and TC Genesis Potential Parameter (GPP) especially over warm and cold core eddies. Through this analysis, we found that the entrainment term has highest correlation with GPP, with correlation coefficient value of magnitude 0.68 with GPP, next to the entrainment the net heat flux term shows significant impact on GPP with correlation coefficient 0.42. Notably, unlike warm-core eddies, the pre-cyclonic relationship (5 days before passage) between mixed layer dynamics and GPP is absent in cold-core eddies. Furthermore, cold-core eddies have a lower association with different terms of mixed layer heat budget analysis and GPP than warm-core eddies. A prominent feature for warm-core eddies is that the net latent heat flux dominates in influencing GPP among the terms that determines net surface heat flux, however, such dominance of net heat flux is not observed for cold core eddies, which infers that the cold core eddy regions are relatively less influential in determining/impacting cyclone life cycle, compared to the warm core eddy regions. The present study offers valuable insights into the asymmetric role of oceanic eddies, particularly highlighting the pre-cyclonic ocean-atmosphere coupling over warm-core eddies, which should be incorporated into coupled forecast systems to enhance tropical cyclone intensity predictions in the Bay of Bengal.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 2","pages":"Pages 322-338"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208492","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":"Dominant Modes of Tropical Cyclone Activity in the Philippines","authors":"Jianne Mae Pamintuan,&nbsp;Joseph Basconcillo","doi":"10.1016/j.tcrr.2026.04.001","DOIUrl":"10.1016/j.tcrr.2026.04.001","url":null,"abstract":"<div><div>Tropical cyclone (TC) activity over the Philippines is influenced by large-scale variability across the western North Pacific (WNP), yet basin-wide analyses may obscure subregional patterns relevant to national risk. This study identifies the dominant spatial modes of TC passage frequency and accumulated cyclone energy (ACE) within the modified PAGASA Tropical Cyclone Advisory domain (4–27°N, 114–140°E) during 1984–2023 using Empirical Orthogonal Function (EOF) analysis. The leading mode (EOF1) explains 35–50% of the variance in TC passage frequency and 43–59% in ACE across seasonal and annual analyses. This mode reveals that the primary pattern of TC activity affecting the Philippines is centered over the Philippine Sea, extending westward into the Philippines and northward toward Taiwan, China. For passage frequency, the spatial structure evolves seasonally with shifts in the monsoon trough (MT) and the Western North Pacific Subtropical High (WNPSH), highlighting the dynamical contrast between boreal summer (JJA) and autumn (SON). For ACE, the dominant pattern emphasizes open-ocean regions east and northeast of the Philippines, where TCs can intensify and persist. ENSO shows the strongest interannual relationships with both TC passage frequency and ACE, particularly during JJA, while the Pacific Decadal Oscillation (PDO) exhibits more seasonally consistent relationships at longer timescales. Significant downward trends during JJA–SON indicate a decline in peak-season TC activity over the study period.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 2","pages":"Pages 177-192"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208295","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":"Performance of Tropical Cyclone Forecasts in the Western North Pacific in 2025. Part Ⅰ: Track and Intensity","authors":"Mengqi Yang ,&nbsp;Guomin Chen ,&nbsp;Gaozhen Nie ,&nbsp;Chen Chen ,&nbsp;Tong Xu","doi":"10.1016/j.tcrr.2026.04.002","DOIUrl":"10.1016/j.tcrr.2026.04.002","url":null,"abstract":"<div><div>This study evaluates the performance of operational tropical cyclone (TC) track and intensity forecasts over the western North Pacific (WNP) during the 2025 season. The 2025 WNP TC season was very active, featuring 27 named TCs. Forecasts from five official typhoon prediction agencies, eight deterministic Numerical Weather Prediction (NWP) models, six deterministic data-driven Artificial Intelligence Weather Prediction (AIWP) models, and five Ensemble Prediction Systems (EPS) are verified at lead times from 12 to 120 h. Results indicate that TC track forecasts achieved reduced mean direct position errors (DPEs) at 24 h compared to 2024, with some reaching historically low error levels. However, the large westward bias in forecasts for Super Typhoon Halong (TC2522) substantially influenced long-lead time statistics. Differences in track errors among AIWP models increased progressively beyond 48 h. For intensity, mean absolute errors (MAEs) increased with lead time. NWP models outperformed AIWP models, with the latter consistently underestimating intensity across all lead times, though the degree of underestimation decreased relative to 2024. These findings highlight not only the improvement in TC track forecast capability but also the persistent challenges in intensity forecasting, particularly for AIWP models.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 2","pages":"Pages 193-202"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208296","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":"Variations Observed in Surface Layer of the Convective Regions of Mesoscale Convective Systems Detected in Cyclone ‘Laila’ and in Normal (Non-cyclone) Southwest Monsoon Using PRWONAM Measurements","authors":"Narendra Nelli Reddy,&nbsp;Kusuma G. Rao","doi":"10.1016/j.tcrr.2026.04.005","DOIUrl":"10.1016/j.tcrr.2026.04.005","url":null,"abstract":"<div><div>For the first time, surface layer variations of Convective regions (CRs) of Mesoscale Convective Systems (MCS) embedded in a cyclone, ‘Laila’ are examined over the Indian region during 17–21, May 2010 using PRWONAM measurements. CRs are detected in MCSs based on the Detect-And-Spread (DAS) algorithm applied on IR (InfraRed) Brightness Temperatures (IRBRT) for Bangalore station. During Jul–Aug in 2009 (2010), the CRs were detected only during 1400 IST – 0500 IST (Indian Standard Time = UTC+530 h) everyday with percentage of occurrence at 9.3 % (18.6 %). The detected CR in MCS within Laila during 1900 IST, 18 May – 0400 IST, 19 May is associated with stratiform region extending from 0000 IST to 0400 IST, 19 May. The striking observation is, a combination of (i) weak meso-low (L1) forming ahead of precipitating CR during 2104 IST – 2129 IST, 18 May, (ii) well-defined meso-high (H1) during precipitating CR, (iii) well-defined wake-low (L2) in the wake of the CR. A rise in surface specific humidity to 18.7 g kg⁻¹ and convective activity intensifying with IRBRT at 190 K are observed ahead of precipitating CR. A meso-high (H1) formed in precipitating CR with wind gusting and surface cooling. A meso-low (L2) formed later with an amplitude at 2.2 hPa of the pressure couplet (H1, L2). The wake-low drying observed in surface specific humidity reduction by 3.9 g kg⁻¹ due to unsaturated air descent at back edge of trailing stratiform region into precipitating CR is indicated in wind direction change. Normalcy restored from wake-low conditions in 23 min. Further, the Surface layer variations in CRs of Normal (non-cyclone) MCSs of Pre, Onset, Peak monsoon phases of the southwest monsoon show similarities with MCS in Cyclone in meso-low (L1), meso-high (H1) and a wake low (L2) occurrences over Bangalore location.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 2","pages":"Pages 226-254"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208299","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":"Observational and Forecasting Aspects of Super Typhoon Ragasa in Hong Kong","authors":"S.K. Lai ,&nbsp;Y.H. He ,&nbsp;C.K. Ho ,&nbsp;Y.S. Lui ,&nbsp;P. Cheung ,&nbsp;C.C. Lam ,&nbsp;D. Hui ,&nbsp;K.W. Lo ,&nbsp;P.W. Chan ,&nbsp;J.Y. He","doi":"10.1016/j.tcrr.2026.04.006","DOIUrl":"10.1016/j.tcrr.2026.04.006","url":null,"abstract":"<div><div>Super Typhoon Ragasa in September 2025 ranked the second-strongest tropical cyclone (TC) in the South China Sea since records began in 1950. Its closest approach to Hong Kong was only about 120 km yet its impact on the territory was not particularly severe. This work analysed the meteorological perspective using observations and model forecasts, where Ragasa's high wind region skirted just around the door step of the territory of Hong Kong. This work also serves as a valuable case study for near-coast eyewall replacement of TC; and for meteorological authorities that pursue early warnings to intense TC by showing that (i) observational data, especially near-sea-surface dropsonde data, are crucial for determining the spatial extent of hurricane winds associated with Ragasa, and (ii) high-resolution regional model was used to capture subtle changes in Ragasa's track, intensity, and radius of maximum wind as it approached Hong Kong, thereby providing essential information for assessing its potential impact on the territory. Furthermore, the performance of location-specific wind forecasts over Hong Kong is discussed. A regional ensemble prediction system with data assimilation of all-sky radiance from satellites is shown to provide the smallest error on local wind forecasts among other global and regional deterministic models for Ragasa.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"15 2","pages":"Pages 255-273"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208293","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}