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Precipitation nowcasting based on radar data plays a crucial role in extreme weather prediction and has broad implications for disaster management.
Convolutional lstm network: A machine learning approach for precipitation nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c · 2015
Earlier work this paper cites.
Deep learning for precipitation nowcasting: A benchmark and a new model
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.-Y., Wong, W.-k., and Woo, W.-c · 2017
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Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms
Wang, Y., Long, M., Wang, J., Gao, Z., and Yu, P. S · 2017
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Pysteps: An open-source python library for probabilistic precipitation nowcasting (v1. 0)
Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., and Foresti, L · 2019
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Disentangling physical dynamics from unknown factors for unsupervised video prediction
Guen, V. L. and Thome, N · 2020
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Meteonet, an open reference weather dataset
Gwennaelle, L., Lea, B., Vincent, C., Brice, Le, P., Bruno, P., and Lior, P · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
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Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology
Veillette, M., Samsi, S., and Mattioli, C · 2020
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Wmo atlas of mortality and economic losses from weather, climate and water extremes (1970–2019)
Association, W. M. et al · 2021
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Skilful precipitation nowcasting using deep generative models of radar
Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., et al · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Imagen video: High definition video generation with diffusion models
Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., et al · 2022
Prediff: Precipitation nowcasting with latent diffusion models
Gao, Z., Shi, X., Han, B., Wang, H., Jin, X., Maddix, D., Zhu, Y., Li, M., and Wang, Y · 2023
Later among the works it cites.
Photorealistic video generation with diffusion models
Gupta, A., Yu, L., Sohn, K., Gu, X., Hahn, M., Fei-Fei, L., Essa, I., Jiang, L., and Lezama, J · 2023
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simple diffusion: End-to-end diffusion for high resolution images
Hoogeboom, E., Heek, J., and Salimans, T · 2023
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Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
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The multi-scale interactions of atmospheric phenomenon in mean and extreme precipitation
Prein, A. F., Mooney, P. A., and Done, J. M · 2023
Later among the works it cites.
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
Cited alongside, same era.
On the importance of noise scheduling for diffusion models
Chen, T · 2023
Cited alongside, same era.
Guidelines for nowcasting techniques
CLIMA, T. and TE, W
Cited in the paper.
Earthformer: Exploring space-time transformers for earth system forecasting
Gao, Z., Shi, X., Wang, H., Zhu, Y., Wang, Y. B., Li, M., and Yeung, D.-Y
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Simvp: Simpler yet better video prediction
Gao, Z., Tan, C., Wu, L., and Li, S. Z
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Skilful nowcasting of extreme precipitation with nowcastnet
Zhang, Y., Long, M., Chen, K., Xing, L., Jin, R., Jordan, M. I., and Wang, J · 2023
Later among the works it cites.
Advancing realistic precipitation nowcasting with a spatiotemporal transformer-based denoising diffusion model
Zhao, Z., Dong, X., Wang, Y., and Hu, C · 2024
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