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Ensemble forecast of tropical cyclone tracks based on deep neural networks

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Abstract

A nonlinear artificial intelligence ensemble forecast model has been developed in this paper for predicting tropical cyclone (TC) tracks based on the deep neural network (DNN) by using the 24-h forecast data from the China Meteorological Administration (CMA), Japan Meteorological Agency (JMA) and Joint Typhoon Warning Center (JTWC). Data from a total of 287 TC cases over the Northwest Pacific Ocean from 2004 to 2015 were used to train and validate the DNN based ensemble forecast (DNNEF) model. The comparison of model results with Best Track data of TCs shows that the DNNEF model has a higher accuracy than any individual forecast center or the traditional ensemble forecast model. The average 24-h forecast error of 82 TCs from 2016 to 2018 is 63 km, which has been reduced by 17.1%, 16.0%, 20.3%, and 4.6%, respectively, compared with that of CMA, JMA, JTWC, and the error-estimation based ensemble method. The results indicate that the nonlinear DNNEF model has the capability of adjusting the model parameter dynamically and automatically, thus improving the accuracy and stability of TC prediction.

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Acknowledgments

This study was supported by the National Key Project of Research and Development Plan of China (No. 2016YFC1401905), the National Natural Science Foundation of China (Grant Nos. 41976163 and 41575107), the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (No. GML2019ZD0302), and the Guangdong Special Fund Program for Marine Economy Development (No. GDNRC[2020]050).

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Authors and Affiliations

  1. Key Laboratory of Marine Hazards Forecasting (Ministry of Natural Resources), Hohai University, Nanjing, 210098, China

    Chong Wang

  2. College of Marine Technology, Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China

    Qing Xu

  3. Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, 511458, China

    Yongcun Cheng

  4. PIESAT Information Technology Co. Ltd., Beijing, 100195, China

    Yongcun Cheng

  5. College of Harbor, Coastal and Offshore Engineering, Hohai University, Nanjing, 210098, China

    Yi Pan

  6. Shanghai Typhoon Institute, China Meteorological Administration, Shanghai, 200030, China

    Hong Li

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  1. Chong Wang
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  2. Qing Xu
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  3. Yongcun Cheng
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  4. Yi Pan
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  5. Hong Li
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Correspondence to Qing Xu.

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Wang, C., Xu, Q., Cheng, Y. et al. Ensemble forecast of tropical cyclone tracks based on deep neural networks. Front. Earth Sci. 16, 671–677 (2022). https://doi.org/10.1007/s11707-021-0931-8

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  • DOI: https://doi.org/10.1007/s11707-021-0931-8

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