Tropical Cyclone Research and Review最新文献_第2页

The influence of El Niño-Southern Oscillation (ENSO) on the characteristics of tropical cyclones in Indonesia waters El Niño-Southern涛动（ENSO）对印度尼西亚海域热带气旋特征的影响

IF 4.1 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.08.002

Kadek Krisna Yulianti , Nining Sari Ningsih , Rima Rachmayani , Eko Prasetyo

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.

印度尼西亚与印度洋和太平洋接壤，受热带气旋活动以及厄尔Niño-Southern涛动（ENSO）和印度洋偶极子（IOD）等现象的影响。本研究考察了印度尼西亚四个地区的TC变化，重点关注IOD为中性时的ENSO事件。在过去50年中（1973-2022），有2885个热带气旋（tc）经过印度尼西亚海域，其中最活跃的区域是2区。在El Niño期间，印度尼西亚各地的TC发生率增加，而La Niña总体上的TC发生率减少，但存在区域差异。在La Niña期间，由于垂直风切变（VWS）和涡度等不利的环境因素，区域2的TCs每月减少12.5%。相反，区域3和区域4则分别增加了38.1%和45.7%，这主要归因于海面温度和湿度等有利条件。累积气旋能量（ACE）分析显示，在El Niño期间，1区增加65.4%，2区增加12.4%，而2区在La Niña期间减少35.3%。核密度估计（KDE）突出了季节性和enso驱动的空间变化，在La Niña期间，密度中心普遍向东移动，除了区域2向西移动。最大的移动，632.1公里，发生在La Niña期间的第4区，将TC地层从西努沙登加拉附近移动到帝汶海。对不同地区各热带气旋事件ENSO期间显著波高（SWH）的分析表明，El Niño和La Niña期间密度中心的变化也会影响印度尼西亚海域的显著波高变化。这些发现强调了ENSO对印度尼西亚水域TC活动的影响，为改进防备和海洋安全提供了有价值的见解。

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引用次数: 0

A microwave-based machine learning approach for predicting eyewall replacement cycles 预测眼壁更换周期的微波机器学习方法

IF 4.1 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.07.001

Lorenzo Pulmano

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.

眼壁替换周期（ERCs）通过影响最大风速、风场大小和风暴潮严重程度，极大地增加了热带气旋（TC）的破坏性潜力，同时降低了对TC预报的信心，尤其是对强度预报的信心。机器学习（ML）为提高当前的预测和预测能力提供了新的机会，它的应用将使预报员和最终的公众受益。该项目的目标是创建一个概念验证的ML卷积神经网络（CNN），使用89 GHz微波频段进行训练和测试，以预测erc。训练集由北大西洋盆地（NATL） 1999 - 2009年的风暴组成。测试集包括2019年至2022年的NATL风暴。我们创建了12个模型，这些模型被称为CNN预测眼壁更换周期集合（CE-PERCY），每个模型的训练准确率至少达到80%。利用合成孔径雷达、陆基雷达、飞机侦察、基于微波的ERC概率（M-PERC）、国家飓风中心报告和微波图像，对测试集进行ERC分析。在2019年至2022年的14次飓风中发现了28个erc。CE-PERCY在概念验证中表现良好，版本a和B分别预测21和23个erc。该项目成功地为在ERC预测中使用ML cnn奠定了基础，证明了该技术的可行性，并证明了足够大的微波图像数据集可以用于该特定应用。

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引用次数: 0

Typhoon science meets artificial intelligence: A roundtable on bridging physics-based and data-driven paradigms 台风科学与人工智能的结合：基于物理和数据驱动范式的桥梁圆桌会议

IF 4.1 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.08.006

Zeyi Niu , Zhe-Min Tan , Hui Yu , Jian-Feng Gu , Guomin Chen , Wei Huang

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.

本文总结了第21届中国热带气旋研讨会（NWTC-XXI， 2025年4月16-18日，中国长沙）的“人工智能+台风”会议及其附属圆桌论坛，重点介绍了人工智能技术在台风监测、预报和影响评估方面的最新进展。以及最先进的人工智能天气预报（AIWP）模型与传统的基于物理的数值天气预报（NWP）模型的深度融合。综合圆桌论坛的主要见解，强调人工智能模型在台风预报中的优势、局限性和未来发展方向。从前瞻性的角度来看，我们应该准备好迎接新兴的人工智能研究（AI4R）范式，以推动台风科学和技术的发展。

引用次数: 0

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 评估气候变化对北印度洋登陆热带气旋（LFTCs）的影响及其对马哈拉施特拉邦沿海农业的影响：一个案例研究

IF 2.4 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-04-12 DOI: 10.1016/j.tcrr.2025.04.003

Shahenaz Mulla , Sudhir Kumar Singh , Rizwan Ahmed

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.

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.

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.

近20年来，在北印度洋（NIO）上空形成的热带气旋风暴强度有所增加。日益严重的气旋风暴具有严重的社会经济和农业后果。许多人担心气候变化引起的全球变暖对极端天气事件的影响，例如在全球海洋盆地上形成的热带气旋（tc）的频率和强度。高强度气旋在NIO地区变得更加常见，给沿海社区带来了巨大的风险和脆弱性。世界气象组织（WMO）报告称，最温暖的年份是2015-21年，最温暖的十年是2011-2020年，这可能归因于温室气体水平的增加。然而，关于气候变化对2001 - 2021年间登陆热带气旋各特征的影响的研究却很少。因此，我们进行了分析，评估了气候变化对2000 - 2021年间形成的LFTCs的各种特征（如TC模式、眼景、土地持续时间、土地降落强度（LFI））的影响。在NIO上形成的tc（2001-2021）以较高的LFI越过海岸，且电流强度呈显著增加趋势。此外，在2000年至2021年期间，陆地持续时间、眼型tc和眼景的观测值有所增加。本研究还从植被和海岸线动态方面评估了强气旋风暴尼萨尔加对马哈拉施特拉邦沿海农业的影响。“尼萨尔加”的登陆造成了巨大的社会经济和农业损失，包括暴雨、风暴潮和盐水入侵，造成生物多样性丧失和长期土壤退化。归一化植被指数（NDVI）和增强植被指数（EVI）显示气旋后植被健康状况急剧下降，随后数月恢复缓慢。本研究结果可用于提高北印度洋盆地TCs业务预报的准确性。研究结果还强调了有针对性的沿海管理的必要性，包括红树林恢复和适应性农业战略，以增强对未来LFTCs的抵御能力。

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引用次数: 0

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 2023年9月超强台风“索拉”及强台风“海葵”余波对香港的影响评估

IF 2.4 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-04-10 DOI: 10.1016/j.tcrr.2025.04.002

Yuk Sing Lui, The Hong Kong Federation of Insurers, Andy Wang-chun Lai, Chun-wing Choy, Tsz-cheung Lee

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.

2023年9月初，香港受到9月1日至2日超强台风“绍拉”的猛烈袭击和9月7日至8日台风“海葵”残余引发的强暴雨的严重影响。鉴于这两次连续的极端天气在10天内对香港造成严重破坏的情况非常罕见，我们对这两次极端天气对香港造成的破坏和经济损失进行了影响评估。根据政府报告、媒体、调查和保险理赔的现有数据，9月1日至2日的超级台风“索拉”和9月7日至8日的破纪录暴雨造成的直接经济损失估计分别约为4.8亿港元和17.4亿港元。此外，还将“索拉”和2023年9月的破纪录暴雨的影响与香港近十年来主要发生的其他超级台风和黑色暴雨事件进行了比较，以供参考。值得注意的是，与2017年和2018年香港分别发出10号飓风信号的超级台风“天鸽”和“山竹”相比，2023年“绍拉”的整体影响小于“天鸽”和“山竹”。在暴雨事件方面，2023年9月7-8日的黑色暴雨事件的影响显著高于2014年3月和2020年6月的黑色暴雨事件。并简要讨论了这些超强台风和暴雨事件影响差异的可能归因因素。

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引用次数: 0

Tropical cyclone activities in the western North Pacific in 2023 2023年北太平洋西部热带气旋活动

IF 2.4 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-04-10 DOI: 10.1016/j.tcrr.2025.04.001

Xin Huang , Johnny C.L. Chan , Lina Bai , Zifeng Yu , Tingting Sun

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.

本文利用中国气象局上海台风研究所的最佳轨迹数据，对2023年西北太平洋（WNP）和南海（SCS）的热带气旋（TC）活动进行了详细总结和分析。以1951 - 2020年的历史记录为气候基准，研究了亚太地区的TC频率、起源位置、路径、强度、持续时间以及登陆事件的异常情况。2023年的TC频率低于气候学，秋季明显减少。尽管强热带风暴的起源有明显的向西北移动，但它们的起源位置（标志着它们路径的起点）通常与气候学一致。在0-20°N和110°E至125°E经度范围内，以及覆盖日本西北部至南部和朝鲜半岛周围的近海地区，命名tc的路径密度异常高。比较平均值、中位数、上四分位数和下四分位数均显示，台风强度普遍强于平时，17个命名台风中有8个达到超级台风级别。热带气旋维持热带风暴或以上强度的持续时间也超过气候标准。在登陆方面，6个台风在中国登陆，共计11个，而11个台风在亚太地区登陆，共计20个。主要的年距平活动受El Niño事件调制的大气和海洋条件、北太平洋模态正、太平洋经向模态负、年代际尺度上太平洋年代际振荡负相和大西洋多年代际振荡正相的影响。

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引用次数: 0

Can one reconcile the classical theories and the WISHE theories of tropical cyclone intensification? 关于热带气旋增强的经典理论和WISHE理论能调和吗？

IF 2.4 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-04-01 DOI: 10.1016/j.tcrr.2025.02.002

Roger K. Smith , Michael T. Montgomery , Shanghong Wang

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.

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.

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.

人们努力调和夏皮罗、威洛比、舒伯特和哈克关于热带气旋增强的经典平衡理论和伊曼纽尔的各种预测（或wish -）理论。首先，将经典理论扩展到解释倾斜上升空气中的显性潜热释放，证明了它的洞察力。虽然这样的努力揭示了一系列关于在平衡框架中表示深对流的旧建模问题，但分析提供了对这些问题的新视角。最重要的是，这两种理论不能调和。以条件不稳定大气中的轴对称涡旋为起点，通过一个特殊的计算说明了包含显式潜热释放的经典模型的行为。一旦高空发生凝结，翻转环流的湿润Eliassen方程在对流不稳定区变成双曲型，除非对Eliassen方程进行适当的正则化以去除这些双曲型区域，否则模型无法向前推进。然而，正则化抑制了深层潮湿对流，没有机制来逆转对流层下层摩擦引起的外流，这需要将绝对角动量集中在那里。因此，即使在高架云形成并伴随潜热释放之后，最初的涡旋也会向下旋转。经典理论的明确湿润版本中的流动结构与WISHE理论中的相似，这一事实提出了几个关于WISHE理论中涡旋上升物理学的基本问题，并对这些理论在理解自然界热带气旋增强方面的效用提出了质疑。

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引用次数: 0

Rapid intensification of the Super Cyclone Amphan: Environmental drivers and its future projections 超级气旋安潘的快速强化：环境驱动因素及其未来预测

IF 2.4 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-03-01 DOI: 10.1016/j.tcrr.2025.02.005

R.S. Akhila, J. Kuttippurath, A. Chakraborty, N. Sunanda, R. Peter

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.

热带气旋是起源于温暖的热带海洋的强烈天气系统，它们改变了海洋的动力、化学和生物状态。本文对2020年5月发生在孟加拉湾（BoB）的超级气旋安潘（Amphan）快速增强的原因进行了深入研究。海温升高是安番增强为超级气旋的主要原因之一。此外，气旋路径上存在的暖核涡流也有助于其快速增强。热带气旋热势（TCHP）和上层海洋热含量（OHC）保持一致，并在整个气旋期间保持高强度。在此期间，虽然气旋诱发的冷尾较强，但背景海温条件仍然较高，有利于气旋进一步加强。浅层和深层的垂直风切变都很小，这进一步促进了稳定强气旋涡的形成，从而促进了气旋涡的快速增强。利用大气-海洋耦合模式分析了气旋安潘在未来情景中的行为。与目前的情景相比，预计未来气旋的严重程度将增加（RCP 8.5）。在RCP 4.5的情况下，观察到提前登陆。由于UOHC升高，Amphan在RCP 8.5中的强度高于目前情景。未来平动速度增大，使气旋移动速度加快。由于Amphan的通过，UOHC减少，在未来气候变暖的情况下，UOHC会更高。这表明来自海洋的额外能量被转移到大气中，导致气旋进一步加强。根据大气-海洋耦合模式的结果，未来温暖的大气和海洋条件将更有利于强气旋的发生和发展。

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引用次数: 0

Ensemble deep learning models for tropical cyclone intensity prediction using heterogeneous datasets 基于异构数据集的热带气旋强度预测集成深度学习模型

IF 2.4 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-03-01 DOI: 10.1016/j.tcrr.2025.02.001

Dikshant Gupta, Menaka Pushpa Arthur

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.

预测热带气旋强度有助政府采取适当的预防措施，并向市民发出适当的警告。由于其内部和外部影响因素都是动态变化的，因此强度预测是一项非常具有挑战性的任务。我们提出了一个系统，使用基于cnn的集成深度学习模型来预测TC强度，该模型由TC的卫星图像和数值数据训练。本文对CNN、递归神经网络（RNN）和迁移学习模型（AlexNet和VGG）等几种深度学习模型进行了全面的研究，以确定它们在预测TC强度方面的有效性。我们的重点是四个广泛认可的模型：AlexNet， VGG16， RNN和一个定制的基于cnn的集成模型，所有这些模型都是专门在图像数据上训练的，以及一个利用图像和数字数据集进行训练的集成模型。我们的分析根据所造成的损失来评估每种模型的性能。结果提供了对所选深度学习模型的比较评估，并以均方误差（MSE）的形式提供了各自的预测损失，即100个epoch的均方误差（MSE）为194，执行时间为1229 s来预测TC强度。我们还强调了将图像和数值数据合并到集成模型中的潜在好处，这可以提高预测精度。这项研究为气象和灾害管理领域提供了宝贵的知识，为更有弹性和更精确的TC强度预测模型铺平了道路。

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引用次数: 0

Evaluation of operational extended range forecast of cyclogenesis over the north Indian Ocean 北印度洋气旋形成的业务扩展范围预报评价

IF 2.4 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Tropical Cyclone Research and Review

Pub Date : 2025-03-01 DOI: 10.1016/j.tcrr.2025.02.006

M. Sharma , M. Mohapatra , P. Suneetha

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 & AS and towards over-warning for high categories over NIO and BoB in week 1. It is biased towards over-warning for moderate & 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.

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 & 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.

根据2018年4月至2023年12月期间阿拉伯海（AS）、孟加拉湾（BoB）和北印度洋（NIO）在第1周和第2周每周四发布的868份预报，对印度气象局发布的零、低、中、高气旋形成概率的业务扩展范围预报的表现进行了评估。预测偏向于低预警和中等类别的NIO。在第1周对NIO和BoB的高类别进行过度警告。它倾向于对中度的过度警告。第二周NIO和BoB的高类别预报和低类别预报。在第2周，它倾向于低和高类别的预警不足，中等类别的预警过度。第1周和第2周的Brier评分（Brier技能评分）分别比NIO高0.051（48.7%）和0.087（8.6%）。马登朱利安涛动（MJO）、赤道罗斯比波（ERW）和开尔文波（KW）与成因的关联增强，低频背景波（LW）和热带辐合带（ITCZ）与成因的关联减弱，风暴强度从低气压增加到极强气旋风暴（VSCS）。发生VSCS的病例分别为100%、92%、92%、92%和100%；NIO上空的上述类型风暴与南面较强的西风带、北面较强的东风带、MJO、ERW和KW的对流相有关。

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引用次数: 0