Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.07.004
Yubin Yu , Dajun Zhao , Huan Tang , Yuhao Zheng
The intensity changes of tropical cyclones (TCs) are a key focus in TC research community and pose significant challenges for operational forecasting. Water vapor plays a crucial role in the variations of TC intensity. This paper reviews and summarizes representative findings regarding the influence of water vapor on TC intensity. The discussion primarily covers the impact of water vapor sources, transport, distribution, budget, and phase changes on TC intensity. However, critical scientific challenges remain, including establishing quantitative thresholds for dry and cold air intrusion, understanding microphysical and dynamic interaction mechanisms in high-resolution models, and developing advanced moist thermodynamic approaches. Addressing these challenges is essential for advancing research and improving forecasts of the impact of water vapor on TC intensity.
{"title":"Research progress on the influence of water vapor on tropical cyclone intensity","authors":"Yubin Yu , Dajun Zhao , Huan Tang , Yuhao Zheng","doi":"10.1016/j.tcrr.2025.07.004","DOIUrl":"10.1016/j.tcrr.2025.07.004","url":null,"abstract":"<div><div>The intensity changes of tropical cyclones (TCs) are a key focus in TC research community and pose significant challenges for operational forecasting. Water vapor plays a crucial role in the variations of TC intensity. This paper reviews and summarizes representative findings regarding the influence of water vapor on TC intensity. The discussion primarily covers the impact of water vapor sources, transport, distribution, budget, and phase changes on TC intensity. However, critical scientific challenges remain, including establishing quantitative thresholds for dry and cold air intrusion, understanding microphysical and dynamic interaction mechanisms in high-resolution models, and developing advanced moist thermodynamic approaches. Addressing these challenges is essential for advancing research and improving forecasts of the impact of water vapor on TC intensity.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 219-229"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247924","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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.07.002
R. Emmanuel , Medha Deshpande , Anandh T.S. , Ralf Toumi , Ganadhi Mano Kranthi , S.T. Ingle
A precise understanding and prediction of tropical cyclone (TC) genesis remains one of the fundamental objectives for the meteorological community. Monitoring would be much easier if we could anticipate in advance the regions where a TC would form. In this study, we considered 8 cases each of developing and non-developing TCs over the North Indian Ocean (NIO). We found that the stream function averaging over a layer (850-500 hPa) can effectively identify the quasi closed circulation (QCC) before the low-pressure area (LPA) formation. Based on this, we designed an algorithm to track the QCC. The day after an LPA the negative stream-function value at the center of QCC gradually increases in all developing cases. Whereas, in non-developing cases, the negative stream function values are comparatively smaller and remain steady. The total precipitable water within the QCC for developing cases gradually increased on the day of the LPA and persisted until the day of depression. A strong QCC can trap and enhance the availability of moisture through vertical moisture flux transport from the surface in developing lows. However, in non-developing lows, a feeble QCC can only trap moisture at the initial stage but fails to sufficiently moisten the mid-levels. We applied machine learning to identify the threshold values for the stream function and total precipitable water to find the potential of the QCC to become a depression. We tested an algorithm for pre and post monsoon seasons during 2020–2022. The algorithm successfully detected many vortices 5–7 days before the formation of a depression, and it identified depressions 3–4 days in advance. As the thresholds are obtained by machine learning method from the training data, this algorithm could be applied to other basins. This advances our knowledge of the TC origin and aids in its early monitoring.
{"title":"Application of stream function in tracking a quasi-closed circulation and its characteristics in developing and non-developing tropical cyclones over the North Indian Ocean","authors":"R. Emmanuel , Medha Deshpande , Anandh T.S. , Ralf Toumi , Ganadhi Mano Kranthi , S.T. Ingle","doi":"10.1016/j.tcrr.2025.07.002","DOIUrl":"10.1016/j.tcrr.2025.07.002","url":null,"abstract":"<div><div>A precise understanding and prediction of tropical cyclone (TC) genesis remains one of the fundamental objectives for the meteorological community. Monitoring would be much easier if we could anticipate in advance the regions where a TC would form. In this study, we considered 8 cases each of developing and non-developing TCs over the North Indian Ocean (NIO). We found that the stream function averaging over a layer (850-500 hPa) can effectively identify the quasi closed circulation (QCC) before the low-pressure area (LPA) formation. Based on this, we designed an algorithm to track the QCC. The day after an LPA the negative stream-function value at the center of QCC gradually increases in all developing cases. Whereas, in non-developing cases, the negative stream function values are comparatively smaller and remain steady. The total precipitable water within the QCC for developing cases gradually increased on the day of the LPA and persisted until the day of depression. A strong QCC can trap and enhance the availability of moisture through vertical moisture flux transport from the surface in developing lows. However, in non-developing lows, a feeble QCC can only trap moisture at the initial stage but fails to sufficiently moisten the mid-levels. We applied machine learning to identify the threshold values for the stream function and total precipitable water to find the potential of the QCC to become a depression. We tested an algorithm for pre and post monsoon seasons during 2020–2022. The algorithm successfully detected many vortices 5–7 days before the formation of a depression, and it identified depressions 3–4 days in advance. As the thresholds are obtained by machine learning method from the training data, this algorithm could be applied to other basins. This advances our knowledge of the TC origin and aids in its early monitoring.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 185-202"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247922","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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.07.003
Jin Zhang , Jie Tang
This study introduces a helicity-based parameterization method for determining the planetary boundary layer (PBL) height to better capture the complex dynamics of the tropical cyclone (TC) boundary layer (TCBL). By integrating this method into the Yonsei University (YSU) PBL scheme of the China Meteorological Administration (CMA) Mesoscale Model (CMA-MESO), the PBL height is dynamically determined using helicity as a proxy for frictional forcing in TCBL regions, while retaining the traditional bulk Richardson number (Rib) method in areas with weak or ambiguous helicity signals. Simulations of 28 Northwest Pacific TCs in 2022 demonstrate that this approach has negligible impact on track forecasts but substantially reduces the systematic underestimation of TC intensity compared to the traditional Rib-based method. The improvements in TC intensity predictions primarily originate from helicity-modulated PBL height adjustments, particularly the distinct elevation of PBL height within the eyewall region. Analysis of PBL tendencies indicates that elevated PBL height enhances low-level stratification instability through deepened heating within the PBL and expanded cooling at the PBL top. Meanwhile, the deepened frictional layer augments low-level convergence through strengthened agradient forcing induced by momentum dissipation. These thermodynamic and dynamic modifications foster convective concentration in the eyewall, where intensified diabatic heating interacts with high inertial stability to elevate heating efficiency, thus driving TC intensification. These findings highlight that the helicity-based parameterization method outperforms the Rib-based method by better determining the eyewall PBL height, whose deeper structure enhances low-level convergence and unstable stratification, providing a practical pathway to improve TC intensity prediction in numerical models.
为了更好地捕捉热带气旋边界层（TCBL）的复杂动力学，提出了一种基于螺旋度的行星边界层（PBL）高度参数化方法。将该方法与中国气象局（CMA）中尺度模式（CMA- meso）的延世大学（YSU） PBL方案相结合，利用螺旋度作为TCBL区域摩擦力的代表动态确定PBL高度，而在螺旋度信号较弱或不明确的地区保留传统的bulk Richardson number （Rib）方法。对2022年西北太平洋28个TC的模拟表明，该方法对路径预报的影响可以忽略不计，但与传统的基于肋的方法相比，大大减少了对TC强度的系统低估。对TC强度预测的改进主要来自于螺旋调制的PBL高度调整，特别是眼壁区域内PBL高度的明显升高。PBL趋势分析表明，PBL高度升高通过加深PBL内部加热和扩大PBL顶部冷却来增强低层分层不稳定性。同时，加深的摩擦层通过动量耗散引起的加强的梯度强迫增强了低层辐合。这些热力学和动力学的变化促进了眼壁的对流集中，在眼壁中，增强的非绝热加热与高惯性稳定性相互作用，提高了加热效率，从而推动了TC的增强。这些结果表明，基于螺旋度的参数化方法优于基于肋的方法，可以更好地确定眼壁PBL高度，其较深的结构增强了低层辐合和不稳定分层，为改进数值模型中的TC强度预测提供了实用途径。
{"title":"Parameterization of boundary layer height based on helicity and its application in tropical cyclone numerical simulation","authors":"Jin Zhang , Jie Tang","doi":"10.1016/j.tcrr.2025.07.003","DOIUrl":"10.1016/j.tcrr.2025.07.003","url":null,"abstract":"<div><div>This study introduces a helicity-based parameterization method for determining the planetary boundary layer (PBL) height to better capture the complex dynamics of the tropical cyclone (TC) boundary layer (TCBL). By integrating this method into the Yonsei University (YSU) PBL scheme of the China Meteorological Administration (CMA) Mesoscale Model (CMA-MESO), the PBL height is dynamically determined using helicity as a proxy for frictional forcing in TCBL regions, while retaining the traditional bulk Richardson number (Rib) method in areas with weak or ambiguous helicity signals. Simulations of 28 Northwest Pacific TCs in 2022 demonstrate that this approach has negligible impact on track forecasts but substantially reduces the systematic underestimation of TC intensity compared to the traditional Rib-based method. The improvements in TC intensity predictions primarily originate from helicity-modulated PBL height adjustments, particularly the distinct elevation of PBL height within the eyewall region. Analysis of PBL tendencies indicates that elevated PBL height enhances low-level stratification instability through deepened heating within the PBL and expanded cooling at the PBL top. Meanwhile, the deepened frictional layer augments low-level convergence through strengthened agradient forcing induced by momentum dissipation. These thermodynamic and dynamic modifications foster convective concentration in the eyewall, where intensified diabatic heating interacts with high inertial stability to elevate heating efficiency, thus driving TC intensification. These findings highlight that the helicity-based parameterization method outperforms the Rib-based method by better determining the eyewall PBL height, whose deeper structure enhances low-level convergence and unstable stratification, providing a practical pathway to improve TC intensity prediction in numerical models.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 203-218"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247923","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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.08.003
Sunil Kumar , Shashi Kant , Amrit Kumar
This study examines the tropical cyclone (TC) eyes over the North Indian Ocean (NIO) from 2013 to 2023. TCs feature a warm and cloud-free region called the eye. In recent years, meteorologists have taken a keen interest in geometric and thermodynamic characteristics of TC’s eye as these attributes are useful for operational forecasting of TCs. In this study, we analyzed data from the INSAT-3D/R satellite, passive microwave imagery, and thermodynamic parameters over an 11-year period (2013–2023).
Analysis showed that 37.73 % of the TCs developed an eye over the NIO, with 60 % of these occurring in the Arabian Sea (AS) and 40 % in the Bay of Bengal (BoB). The eye was observed most frequently approximately 36 h (1.5 days) after the storm's onset (>34 knots). The mean maximum sustained wind speed at which an eye formed was 66 knots, with a standard deviation of 14.26 over the NIO. The average estimated central pressure of the eye was 982.15 hPa. TCs' eyes formed at an average latitude of 13.60°N and longitude of 83.67°E in the BoB, with standard deviations of 2.33° and 5.93°, respectively. The average radius of a TC's eye was 22.3 km (with a diameter of 44.6 km) over the NIO. The calculated Eye Roundness Value (ERV) was 0.59, with a range from 0.5 to 0.8. The average intensity of TC's eyes over the NIO was classified as Dvorak’s T4.0 (64–89 knots). The dominant pattern observed before the formation of the TC's eye was the Curved Band Pattern. Our results indicated that as one moves poleward, both the size and number of eyes increase.
The findings of this study are valuable for operational forecasters and disaster managers in mitigating socioeconomic impacts and preserving human lives.
{"title":"Analysis of tropical cyclone eye over the North Indian Ocean during 2013–2023","authors":"Sunil Kumar , Shashi Kant , Amrit Kumar","doi":"10.1016/j.tcrr.2025.08.003","DOIUrl":"10.1016/j.tcrr.2025.08.003","url":null,"abstract":"<div><div>This study examines the tropical cyclone (TC) eyes over the North Indian Ocean (NIO) from 2013 to 2023. TCs feature a warm and cloud-free region called the eye. In recent years, meteorologists have taken a keen interest in geometric and thermodynamic characteristics of TC’s eye as these attributes are useful for operational forecasting of TCs. In this study, we analyzed data from the INSAT-3D/R satellite, passive microwave imagery, and thermodynamic parameters over an 11-year period (2013–2023).</div><div>Analysis showed that 37.73 % of the TCs developed an eye over the NIO, with 60 % of these occurring in the Arabian Sea (AS) and 40 % in the Bay of Bengal (BoB). The eye was observed most frequently approximately 36 h (1.5 days) after the storm's onset (>34 knots). The mean maximum sustained wind speed at which an eye formed was 66 knots, with a standard deviation of 14.26 over the NIO. The average estimated central pressure of the eye was 982.15 hPa. TCs' eyes formed at an average latitude of 13.60°N and longitude of 83.67°E in the BoB, with standard deviations of 2.33° and 5.93°, respectively. The average radius of a TC's eye was 22.3 km (with a diameter of 44.6 km) over the NIO. The calculated Eye Roundness Value (ERV) was 0.59, with a range from 0.5 to 0.8. The average intensity of TC's eyes over the NIO was classified as Dvorak’s T4.0 (64–89 knots). The dominant pattern observed before the formation of the TC's eye was the Curved Band Pattern. Our results indicated that as one moves poleward, both the size and number of eyes increase.</div><div>The findings of this study are valuable for operational forecasters and disaster managers in mitigating socioeconomic impacts and preserving human lives.</div></div>","PeriodicalId":44442,"journal":{"name":"Tropical Cyclone Research and Review","volume":"14 3","pages":"Pages 287-296"},"PeriodicalIF":4.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247929","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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.08.005
Alice T. Rivera , Jim Boy G. Dela Vega
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.
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.
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.
{"title":"From vulnerability to resilience: Addressing the causes, impacts, and solutions for recurrent flash floods in the Philippines","authors":"Alice T. Rivera , 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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.08.007
Yumei Li , Johnny CL. Chan , Xun Li , Wen Feng , Yu Zhang
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 (>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.
八城（2024）是继“威马逊”（2014）之后，2024年又一次登陆海南文昌的超强台风，其破坏力刷新了海南台风灾害的历史记录。基于多源观测资料和数值模式预报结果，介绍了超强台风八木的特点，并讨论了其路径和强度的业务预报面临的巨大挑战。“八城”在南海东北部发生爆炸强化，24 h内风速增加28 m s−1，达到极快速强化（ERI）标准。超强台风强度持续67 h，飓风级大风（>32.7 m s−1）持续影响海南陆地面积约10 h，这些特征超过了超强台风威马逊的特征，给业务预报带来了很大挑战。实际数值天气预报模式对八木早期路径的预测存在明显差异。后期，ECMWF和盘古模式预测台风将在海南东北部登陆，NCEP-GFS和三个CMA模式预测台风将在雷州半岛登陆。考虑到历史上广东的登陆概率远高于海南，这种差异大大增加了确定登陆位置的难度。由于超强台风在海南的罕见登陆以及NWP模式对其强度的预测偏低，八木的强度预报也面临着很大的挑战。
{"title":"Challenges in forecasting super typhoon Yagi (2024)","authors":"Yumei Li , Johnny CL. Chan , Xun Li , Wen Feng , 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<sup>−1</sup> within 24 h—meeting the criteria for extreme rapid intensification (ERI). It maintained supertyphoon intensity for 67 h, with hurricane-force winds (>32.7 m s<sup>−1</sup>) 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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.07.005
Xinyuan Bi , Jinping Liu , Yihong Duan
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.
{"title":"Review of artificial intelligence application in typhoon forecasting","authors":"Xinyuan Bi , Jinping Liu , 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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.08.001
Basanta Kumar Das , Nitish Kumar Tiwari , Trupti Rani Mohanty , Shreya Roy , Archisman Ray , Supriti Bayen , Subhadeep Das Gupta , Kausik Mondal , Himanshu Sekhar Swain , Raju Baitha , Mitesh Hiradas Ramteke , Canciyal Johnson , Thangjam Nirupada Chanu , Manisha Bhor
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.
{"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 , Nitish Kumar Tiwari , Trupti Rani Mohanty , Shreya Roy , Archisman Ray , Supriti Bayen , Subhadeep Das Gupta , Kausik Mondal , Himanshu Sekhar Swain , Raju Baitha , Mitesh Hiradas Ramteke , Canciyal Johnson , Thangjam Nirupada Chanu , 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<sup>−1</sup>) to Yass (670 cell<sup>−1</sup>) and finally during post-Yaas period (196 cell<sup>−1</sup>). Contrary to phytoplankton, zooplankton responded favorably as its density increased from pre-Yaas period (196 cell<sup>−1</sup>) to Yaas period (370 cell<sup>−1</sup>), and during the post-Yaas (24 cell<sup>−1</sup>). 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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.08.004
Roger K. Smith , Michael T. Montgomery
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.
最近的研究（Wang et al. 2021; Li et al. 2024）提出了一种新的热带气旋增强时间依赖理论。在这里，我们检查了这个新理论的物理学，并指出模型中的强化必须是一个未指明的绝对角动量来源的结果。由于这个原因，我们开始质疑这个理论的物理完整性。我们也质疑试图调整理论中引入的未知参数的方法。
{"title":"On the physics of a new time-dependent theory of tropical cyclone intensification","authors":"Roger K. Smith , 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}
Pub Date : 2025-09-01DOI: 10.1016/j.tcrr.2025.07.006
Hermes De Gracia , Jorge Celeron , Consuelo Diaz , Aristeo Hernandez , Victoria Serrano
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.
The data were processed using Python, and each model was trained to predict hurricane frequency within three wind speed categories: <50 knots, 50–100 knots, and >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 >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.
These findings emphasize the need for adaptive forecasting systems that account for nonlinear behavior under climate change conditions.
The model outputs offer valuable insights for risk management, contingency planning, and infrastructure resilience in the hurricane-prone Yucatan Peninsula.
{"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 , Jorge Celeron , Consuelo Diaz , Aristeo Hernandez , 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: <50 knots, 50–100 knots, and >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 >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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号