Development and application of a tornado database for the Chinese mainland
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GEOGRAPHIC DISTRIBUTION OF HISTORICAL TORNADOES IN CHINESE MAINLAND
view moreCredit: Fang, G., et al
A study published in the KeAi journal Advances in Wind Engineering presents an open-access tornado database for mainland China. Developed using the Yearbook of Meteorological Disasters in China (YMDC) and supplementary media reports, the database includes data from 738 tornadoes recorded between 2003 and 2019, with detailed information on tornado occurrence times, locations, intensities,and damage descriptions.
“The research highlights the spatial concentration of tornadoes in Jiangsu and Guangdong provinces,” says first author Genshen Fang. “These areas are more susceptible due to their climatic and geographic conditions. Temporally, the study finds tornadoes are most frequent in summer, peaking in July, with the least activity in winter.”
Notably, the application of a modified Enhanced Fujita (EF) scale is adapted to local conditions to estimate tornado intensities based on available damage descriptions. This approach addresses the challenges of using conventional EF scale indicators in a region with different building materials and structural designs.
The data were further analyzed using statistical models and stochastic simulations to evaluate tornado risks across different regions and intensities. For instance, the probability of high-intensity tornadoes (EF2 and above) was calculated for specific counties, providing valuable insights for disaster risk management and urban planning.
While the database provides a comprehensive overview, the authors note some limitations.
“The annual tornado frequency showed a decreasing trend, which may be influenced by reporting inconsistencies rather than a true meteorological decline,” adds Fang. “The study emphasizes the need for continued data collection and improvements in tornado reporting and monitoring.”
Nonetheless, this database represents a valuable resource for understanding tornado activity in China, supporting risk assessment, disaster preparedness and future research efforts.
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Contact author name, affiliation, email address:
Genshen Fang, State Key Lab of Disaster Reduction in Civil Engineering, Tongji University, Shanghai, 200092, China, 2222tjfgs@tongji.edu.cn
Journal
Advances in Wind Engineering
Method of Research
Data/statistical analysis
Subject of Research
Not applicable
Article Title
Development and application of a tornado database for the Chinese mainland
Scientists develop new AI method to forecast cyclone rapid intensification
Chinese Academy of Sciences Headquarters
Rapid Intensification (RI) of a tropical cyclone (TC), defined as a maximum sustained wind increase of at least 13 m/s within 24 hours, remains one of the most challenging weather phenomena to forecast because of its unpredictable and destructive nature. Although only 5% of TCs experience RI, its sudden and severe development poses significant risks to affected regions.
Traditional forecasting methods, such as numerical weather prediction and statistical approaches, often fail to consider the complex environmental and structural factors driving RI. While artificial intelligence (AI) has been explored as a means to improve RI prediction, most AI techniques have struggled with high false alarm rates and limited reliability.
To address this issue, researchers from the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS) have developed a new model for forecasting RI of TCs based on "contrastive learning." This study was published in the Proceedings of the National Academy of Sciences (PNAS) on January 21.
The new model has two inputs: Input A, a known RI TC sample, and Input B, an unknown sample to be forecasted. It extracts features from both inputs and calculates their distance in a high-dimensional space. If the distance is small, Input B is forecasted as an RI TC; if large, it is classified as a non-RI TC. Each unknown sample is compared with 10 known RI TC samples, and if more than five of the comparisons classify it as an RI TC, it is then classified as such.
Additionally, this study uses satellite imagery alongside atmospheric and oceanic data to balance RI and non-RI TC data. The model learns to differentiate between RI and non-RI TCs by comparing the two inputs during training.
When tested on data from the Northwest Pacific between 2020 and 2021, the method achieved an impressive accuracy of 92.3% and reduced false alarms to 8.9%. Compared to existing techniques, it improved accuracy by 12% and reduced false alarms by a factor of three, representing a major advancement in forecasting.
Although the model was initially trained on reanalysis data, the researchers created an operational forecasting scenario by replacing the reanalysis data with ECMWF-IFS numerical model forecast data from 2020 to 2021 as input. The results demonstrated comparable forecasting accuracy, further validating the reliability of this approach and confirming its suitability for real-time forecasting scenarios. This capability can significantly enhance early warning systems, thus improving global disaster preparedness.
"This study addresses the challenges of low accuracy and high false alarm rates in RI TC forecasting," said Prof. LI Xiaofeng, the corresponding author. "Our method enhances understanding of these extreme events and supports better defenses against their devastating impacts."
Journal
Proceedings of the National Academy of Sciences
Article Title
Advancing forecasting capabilities: A contrastive learning model for forecasting tropical cyclone rapid intensification
Article Publication Date
21-Jan-2025
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