Tropical Cyclone Research and Review最新文献

Research progress on the influence of water vapor on tropical cyclone intensity 水汽对热带气旋强度影响的研究进展

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.07.004

Yubin Yu , Dajun Zhao , Huan Tang , Yuhao Zheng

引用次数: 0

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 流函数在北印度洋发展中和非发展中热带气旋准闭合环流跟踪中的应用及其特征

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.07.002

R. Emmanuel , Medha Deshpande , Anandh T.S. , Ralf Toumi , Ganadhi Mano Kranthi , S.T. Ingle

{"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}

引用次数: 0

Parameterization of boundary layer height based on helicity and its application in tropical cyclone numerical simulation 基于螺旋度的边界层高度参数化及其在热带气旋数值模拟中的应用

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.07.003

Jin Zhang , Jie Tang

{"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}

引用次数: 0

Analysis of tropical cyclone eye over the North Indian Ocean during 2013–2023 2013-2023年北印度洋热带气旋风眼分析

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.08.003

Sunil Kumar , Shashi Kant , Amrit Kumar

{"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}

引用次数: 0

From vulnerability to resilience: Addressing the causes, impacts, and solutions for recurrent flash floods in the Philippines 从脆弱性到恢复力：解决菲律宾经常性山洪暴发的原因、影响和解决方案

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.08.005

Alice T. Rivera , Jim Boy G. Dela Vega

{"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}

引用次数: 0

Challenges in forecasting super typhoon Yagi (2024) 超级台风八城（2024）预报面临的挑战

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.08.007

Yumei Li , Johnny CL. Chan , Xun Li , Wen Feng , Yu Zhang

{"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}

引用次数: 0

Review of artificial intelligence application in typhoon forecasting 人工智能在台风预报中的应用综述

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.07.005

Xinyuan Bi , Jinping Liu , Yihong Duan

{"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}

引用次数: 0

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 在大型河流系统中，作为生态生物指标的水生生物对气旋干预的响应——以印度恒河为例

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 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

{"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}

引用次数: 0

On the physics of a new time-dependent theory of tropical cyclone intensification 关于热带气旋增强新时变理论的物理学

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.08.004

Roger K. Smith , Michael T. Montgomery

引用次数: 0

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 利用卷积神经网络（cnn）、长短期记忆网络（LSTMs）和统计模型预测2025 - 2034年墨西哥尤卡坦半岛飓风的频率和强度

IF 4.1 4区地球科学

Tropical Cyclone Research and Review Pub Date : 2025-09-01 DOI: 10.1016/j.tcrr.2025.07.006

Hermes De Gracia , Jorge Celeron , Consuelo Diaz , Aristeo Hernandez , Victoria Serrano

{"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}

引用次数: 0