Year: 2022
Type: article
Abstract: 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.
Source: Frontiers in Earth Science
Authors: Zhen Wang, Jun Zhao, Hong Huang, Xuezhong Wang
Institution: National University of Defense Technology
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $2,950
Awards: G1810827827, G6145909146
OpenAlex ID: W4282961571