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.
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.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
20 Apr 2023
Methods for assessment of models |
| 20 Apr 2023
| 20 Apr 2023
Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model
Liang Wang
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Bingcheng Wan
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Shaohui Zhou
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
Haofei Sun
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
Zhiqiu Gao
CORRESPONDING AUTHOR
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China
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- How ocean thermal structure and atmospheric steering govern typhoon evolution: evidence from interpretable machine learning H. Dang et al. https://doi.org/10.1088/2515-7620/aeabf0
- Relationship between Convection and Tropical Cyclone Size and Size Change Rate P. Rafael et al. https://doi.org/10.4236/gep.2026.142003
- Vertical thermal response of the North Indian Ocean to tropical cyclones R. Singh & M. Behera https://doi.org/10.1016/j.dynatmoce.2026.101697
- Physics-guided non-autoregressive transformer for lightweight cyclone track prediction in the Bay of Bengal A. Faiaz et al. https://doi.org/10.1016/j.eswa.2026.131972
- Remote sensing and analysis of tropical cyclones: Current and emerging satellite sensors L. Ricciardulli et al. https://doi.org/10.1016/j.tcrr.2023.12.003
- Late Holocene hydroclimatic anomalies in the NW Japan Sea: Distinguishing moisture sources and Pacific linkages N. Razjigaeva et al. https://doi.org/10.1016/j.quascirev.2025.109757
- Seasonal prediction of Western North Pacific tropical cyclone formation using convolutional neural network Z. Jiang et al. https://doi.org/10.1007/s00382-026-08203-2
- Complex network-based detection and forecasting of high-intensity tropical cyclones Z. Jiang et al. https://doi.org/10.1016/j.ijdrr.2026.106030
- Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting H. Huang et al. https://doi.org/10.3390/rs17152675
- Rare Tropical Cyclones: Tropical Cyclone Hidaya, A High‐Impact Low‐Latitude Event K. Quagraine & L. Poku https://doi.org/10.1002/met.70235
- Extreme flood events on Western Sakhalin and their linkage to cyclogenesis activity in the western North Pacific in middle-late Holocene N. Razjigaeva et al. https://doi.org/10.1016/j.palaeo.2025.112933
- Time-Member Progressive Inference for Long-Term Tropical Cyclone Track Forecasting L. Luo et al. https://doi.org/10.1109/JSTARS.2025.3614300
- AI-Enhanced Detection and Comparative Analysis of Tropical Cyclones Across Global Ocean Basins G. Gunawardena et al. https://doi.org/10.1016/j.tcrr.2026.04.007
- Probabilistic Forecast of Tropical Cyclone Precipitation Based on Diffusion Model P. Du et al. https://doi.org/10.3390/rs18111786
- MMF-RNN: A Multimodal Fusion Model for Precipitation Nowcasting Using Radar and Ground Station Data Q. Liu et al. https://doi.org/10.1109/TGRS.2025.3528423
- MSFF-CAA-ResNet: multi-scale and attention-enhanced residual network for tropical cyclone center location Z. Chen et al. https://doi.org/10.1007/s00343-025-5251-5
- Multi-task learning framework for cyclone prediction using cascaded BiLSTM and attention mechanism P. Singla et al. https://doi.org/10.1007/s43538-026-00926-4
- A random forest-based method for reconstructing pressure fields in tropical cyclones from wind data J. Youn et al. https://doi.org/10.1016/j.atmosres.2025.108588
Saved (final revised paper)
Latest update: 24 Oct 2026
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.
The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in...
