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The Application Research of FCN Algorithm in the Classification of Strong Convective Short-time Nowcasting Technology on the Northeastern Side of the Qinghai-Tibet Plateau—Gansu Province
Huang, W.; Fu, J.; Feng, X.; Guo, R.; Zhang, J.; Lei, Y. The Application Research of FCN Algorithm in Different Severe Convection Short-Time Nowcasting Technology in China, Gansu Province. Atmosphere2024, 15, 241.
Huang, W.; Fu, J.; Feng, X.; Guo, R.; Zhang, J.; Lei, Y. The Application Research of FCN Algorithm in Different Severe Convection Short-Time Nowcasting Technology in China, Gansu Province. Atmosphere 2024, 15, 241.
Huang, W.; Fu, J.; Feng, X.; Guo, R.; Zhang, J.; Lei, Y. The Application Research of FCN Algorithm in Different Severe Convection Short-Time Nowcasting Technology in China, Gansu Province. Atmosphere2024, 15, 241.
Huang, W.; Fu, J.; Feng, X.; Guo, R.; Zhang, J.; Lei, Y. The Application Research of FCN Algorithm in Different Severe Convection Short-Time Nowcasting Technology in China, Gansu Province. Atmosphere 2024, 15, 241.
Abstract
This study explores the application of the Fully Convolutional Network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG) and thunderstorms, short-term heavy rain(STHR) in Gansu. Training data comes from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations, the performance of the proposed FCN model based on 2017 to 2021 training datasets demonstrate a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when testing against the 2022 dataset. Operational deployment in 2023 has yielded an average Critical Success Index (CSI) of 24.3%, a Probability of Detection (POD) of 62.6%, and a False Alarm Ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model’s optimal performance for hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model's ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
