A Review on the Application of Machine Learning Methods in Tropical Cyclone Forecasting

At present, there is still a bottleneck in tropical cyclone (TC) forecasting due to its complex dynamical mechanisms and various impact factors. Machine learning (ML) methods have substantial advantages in data processing and image recognition, and the potential of satellite, radar and surface observation data in TC forecasting has been deeply explored in recent ML studies, which provides a new strategy to solve the difficulties in TC forecasting. In this paper, through analyzing the existing problems of TC forecasting, the current application of ML methods in TC forecasting is reviewed. In addition, the various predictors and advanced algorithm models are comprehensively summarized. Moreover, a preliminary discussion on the challenges of applying ML methods in TC forecasting is presented. Overall, the ML methods with higher interpretation, intervention and precision are needed in the future to improve the skill of TC prediction.

• Publication year

  2022

• Venue

  Frontiers in Earth Science

• Publication date

  2022-06-15

• Fields of study

  Not labeled

• Identifiers
  DOI 10.3389/feart.2022.902596
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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