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.
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.
Read more at Chinese Academy of Sciences Headquarters
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