Tropical cyclone ensemble forecast framework based on spatiotemporal model-Reference-Cited by-同舟云学术

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Tropical cyclone ensemble forecast framework based on spatiotemporal model

Published:2024-07-29 Issue: Volume: Page:
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Li Tongfei,Che Kaihua,Lu Jiadong,Zeng Yifan,Lv Wei,Liang Zhiyao

Funder

Guangdong Foundation for Basic and Applied Basic Research

the key research platforms and projects of ordinary universities under the Guangdong Provincial Department of Education

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12145-024-01418-z.pdf

Reference36 articles.

1. Alemany S, Beltran J, Perez A, Ganzfried S (2019) Predicting hurricane trajectories using a recurrent neural network. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, pp 468–475. https://doi.org/10.1609/aaai.v33i01.3301468. https://ojs.aaai.org/index.php/AAAI/article/view/3819

2. Ali MM, Kishtawal CM, Jain S (2007) Predicting cyclone tracks in the north indian ocean: An artificial neural network approach. Geophys Res Lett 34(4). https://doi.org/10.1029/2006GL028353. https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006GL028353

3. Chen R, Zhang W, Wang X (2020) Machine learning in tropical cyclone forecast modeling: a review. Atmosphere 11(7). https://doi.org/10.3390/atmos11070676

4. Chen R, Wang X, Zhang W, Zhu X, Li A, Yang C (2019) A hybrid CNN-LSTM model for typhoon formation forecasting. GeoInformatica 23(3):375–396. https://doi.org/10.1007/s10707-019-00355-0

5. Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30years. Nature 436(7051):686–688. https://doi.org/10.1038/nature03906

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