ARCHIVES
VOL. 12, ISSUE 11 (2025)
Understanding and improving tropical cyclone prediction systems
Authors
Vandana Kumari, Ashish Chourey, Dr. Ritu Shrivastava, Dr. Rajiv Srivastava
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.
Download
Pages:157-164
How to cite this article:
Vandana Kumari, Ashish Chourey, Dr. Ritu Shrivastava, Dr. Rajiv Srivastava "Understanding and improving tropical cyclone prediction systems". International Journal of Multidisciplinary Research and Development, Vol 12, Issue 11, 2025, Pages 157-164
Download Author Certificate
Please enter the email address corresponding to this article submission to download your certificate.