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    A Novel Deep Learning Model by BiGRU with Attention Mechanism for Tropical Cyclone Track Prediction in the Northwest Pacific

    Source: Journal of Applied Meteorology and Climatology:;2022:;volume( 061 ):;issue: 001
    DOI: 10.1175/JAMC-D-20-0291.1
    Abstract: Tropical cyclones are among the most powerful and destructive meteorological systems on Earth. In this paper, we propose a novel deep learning model for tropical cyclone track prediction method. Specifically, the track task is regarded as a time series predicting challenge, and then a deep learning framework by a bidirectional gate recurrent unit network (BiGRU) with attention mechanism is developed for track prediction. This proposed model can excavate the effective information of the historical track in a deeper and more accurate way. Data experiments are conducted on tropical cyclone best-track data provided by the Joint Typhoon Warning Center (JTWC) from 1988 to 2017 in the northwestern Pacific Ocean. Results show that our model performs well for tracks of 6, 12, 24, 48, and 72 h in the future. The prediction results show that our proposed combined model is superior to state-of-the-art deep learning models, including a recurrent neural network (RNN), long short-term memory neural network (LSTM), gate recurrent unit network (GRU), and BiGRU without the use of attention mechanism. In comparison with the methods used by the China Meteorological Administration, Japan Meteorological Agency, and the JTWC, our method has obvious advantages in the mid- to long-term track forecasting, especially in the next 72 h.
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      A Novel Deep Learning Model by BiGRU with Attention Mechanism for Tropical Cyclone Track Prediction in the Northwest Pacific

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4286082
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    date accessioned2022-05-09T01:00:37Z
    date available2022-05-09T01:00:37Z
    date copyright08 Jan 2022
    date issued2022
    identifier otherJAMC-D-20-0291.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4286082
    description abstractTropical cyclones are among the most powerful and destructive meteorological systems on Earth. In this paper, we propose a novel deep learning model for tropical cyclone track prediction method. Specifically, the track task is regarded as a time series predicting challenge, and then a deep learning framework by a bidirectional gate recurrent unit network (BiGRU) with attention mechanism is developed for track prediction. This proposed model can excavate the effective information of the historical track in a deeper and more accurate way. Data experiments are conducted on tropical cyclone best-track data provided by the Joint Typhoon Warning Center (JTWC) from 1988 to 2017 in the northwestern Pacific Ocean. Results show that our model performs well for tracks of 6, 12, 24, 48, and 72 h in the future. The prediction results show that our proposed combined model is superior to state-of-the-art deep learning models, including a recurrent neural network (RNN), long short-term memory neural network (LSTM), gate recurrent unit network (GRU), and BiGRU without the use of attention mechanism. In comparison with the methods used by the China Meteorological Administration, Japan Meteorological Agency, and the JTWC, our method has obvious advantages in the mid- to long-term track forecasting, especially in the next 72 h.
    titleA Novel Deep Learning Model by BiGRU with Attention Mechanism for Tropical Cyclone Track Prediction in the Northwest Pacific
    typeJournal Paper
    journal volume61
    journal issue1
    journal titleJournal of Applied Meteorology and Climatology
    identifier doi10.1175/JAMC-D-20-0291.1
    page3–12
    treeJournal of Applied Meteorology and Climatology:;2022:;volume( 061 ):;issue: 001
    contenttypeFulltext
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