Abstract
It is of vital importance to reduce injuries and economic losses by accurate forecasts of typhoon tracks. A huge amount of typhoon observations have been accumulated by the meteorological department, however, they are yet to be adequately utilized. It is an effective method to employ machine learning to perform forecasts. A long short term memory (LSTM) neural network is trained based on the typhoon observations during 1949–2011 in China’s Mainland, combined with big data and data mining technologies, and a forecast model based on machine learning for the prediction of typhoon tracks is developed. The results show that the employed algorithm produces desirable 6–24 h nowcasting of typhoon tracks with an improved precision.
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Foundation item: The National Natural Science Foundation of China under contract Nos 61273245 and 41306028; the Beijing Natural Science Foundation under contract No. 4152031; the National Special Research Fund for Non-Profit Marine Sector under contract Nos 201405022-3 and 2013418026-4; the Ocean Science and Technology Program of North China Sea Branch of State Oceanic Administration under contract No. 2017A01; the Operational Marine Forecasting Program of State Oceanic Administration.
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Gao, S., Zhao, P., Pan, B. et al. A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network. Acta Oceanol. Sin. 37, 8–12 (2018). https://doi.org/10.1007/s13131-018-1219-z
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DOI: https://doi.org/10.1007/s13131-018-1219-z