Theoretical Assessment for Weather Nowcasting Using Deep Learning Methods
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11831-024-10096-5.pdf
Reference25 articles.
1. Zhang Y, Long M, Chen K, Xing L, Jin R, Jordan MI, Wang J (2023) Skilful nowcasting of extreme precipitation with NowcastNet. Nature 619:526–532. https://doi.org/10.1038/s41586-023-06184-4
2. Imran S, Anuradha T, Bharat R (2023): Radar Based Precipitation Nowcasting Prediction by Using Deep Learning Techniques. E3S Web of Conferences. 405, 1–9. https://doi.org/10.1051/e3sconf/202340504003
3. Mihoc A, Ionescu V-S, Mircea I-G, Czibula G, Mihulet E, Aspenes T (2023) ConvSNow: a tailored Conv-LSTM architecture for weather nowcasting based on satellite imagery. Procedia Comput Sci 225:298–307. https://doi.org/10.1016/j.procs.2023.10.014
4. Zheng L, Lu W, Zhou Q (2023) Weather image-based short-term dense wind speed forecast with a ConvLSTM-LSTM deep learning model. Build Environ 239:110446. https://doi.org/10.1016/j.buildenv.2023.110446
5. Yoon S-S, Shin H, Heo J-Y, Choi K-B (2023) Assessment of Deep Learning-based nowcasting using Weather Radar in South Korea. https://doi.org/10.3390/rs15215197
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