Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 8
Articles | Volume 16, issue 8
https://doi.org/10.5194/gmd-16-2167-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2167-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 8
Methods for assessment of models
 | 
20 Apr 2023
Methods for assessment of models |  | 20 Apr 2023

Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model

Liang Wang, Bingcheng Wan, Shaohui Zhou, Haofei Sun, and Zhiqiu Gao

Viewed

Total article views: 1,949 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,452 447 50 1,949 120 23 30
  • HTML: 1,452
  • PDF: 447
  • XML: 50
  • Total: 1,949
  • Supplement: 120
  • BibTeX: 23
  • EndNote: 30
Views and downloads (calculated since 21 Nov 2022)
Cumulative views and downloads (calculated since 21 Nov 2022)

Viewed (geographical distribution)

Total article views: 1,949 (including HTML, PDF, and XML) Thereof 1,883 with geography defined and 66 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Apr 2024
Download
Short summary
The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in the northwestern Pacific from hours 6–72 based on GRU_CNN, which we proposed in this paper and which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
Read more