A Deep Learning Model with Axial Attention for Radar Echo Extrapolation

References

• Agrawal, S., L. Barrington, C. Bromberg, J. Burge, C. Gazen, and J. Hickey, 2019. Machine learning for precipitation nowcasting from radar images. Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, Canada,1–18. doi:10.48550/arXiv.1912.12132.
   Google Scholar
• Ayzel, G., M. Heistermann, and T. Winterrath. 2019. Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1). Geoscientific Model Development 12 (4):1387–1402. doi:10.5194/gmd-12-1387-2019.
   Web of Science ®Google Scholar
• Ballas, N., L. Yao, C. Pal, and A. Courville. 2015. Delving deeper into convolutional networks for learning video representations. arXiv E-Prints. doi:10.48550/arXiv.1511.06432.
   Google Scholar
• Berenguer, M., D. Sempere-Torres, and G. G. S. Pegram. 2011. Sbmcast – an ensemble nowcasting technique to assess the uncertainty in rainfall forecasts by Lagrangian extrapolation. Canadian Journal of Fisheries and Aquatic Sciences 404 (3–4):226–40. doi:10.1016/j.jhydrol.2011.04.033.
   Google Scholar
• Bowler, N. E., C. E. Pierce, and A. W. Seed. 2004. Development of a rainfall nowcasting algorithm based on optical flow techniques. Canadian Journal of Fisheries and Aquatic Sciences 288 (1):74–91. doi:10.1016/j.jhydrol.2003.11.011.
   Google Scholar
• Bowler, N. E., C. E. Pierce, and A. W. Seed. 2006. STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP. Quarterly Journal of the Royal Meteorological Society 132 (620):2127–55. doi:10.1256/qj.04.100.
   Web of Science ®Google Scholar
• Dixon, M., and G. Wiener. 1993. TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology. Journal of Atmospheric and Oceanic Technology 10 (6):785. doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.
   Web of Science ®Google Scholar
• Dosovitskiy, A., L. Beyer, A. Kolesnikov, D. Weissenborn, and N. Houlsby. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv E-Prints. doi:10.48550/arXiv.2010.11929.
   Google Scholar
• Doviak, R. J., D. S. Zrnic, and R. M. Schotland. 1994. Doppler radar and weather observations. Applied Optics 33 (21):453.
   PubMedGoogle Scholar
• Ganguly, A. R., and R. L. Bras. 2002. Distributed quantitative precipitation forecasting using information from radar and numerical weather prediction models. Journal of Hydrometeorology 4 (6):1168–80. doi:10.1175/1525-7541(2003)004<1168:DQPFUI>2.0.CO;2.
   Web of Science ®Google Scholar
• Guo, S. Z., D. Xiao, and X. Y. Yuan. 2017. Short-term rainfall prediction method based on neural network and model integration. Chinese Journal of Progress in Meteorological Science and Technology 7 (1):7.
   Google Scholar
• Handwerker, J. 2002. Cell tracking with TRACE3D—a new algorithm. Atmospheric Research 61 (1):15–34. doi:10.1016/S0169-8095(01)00100-4.
   Web of Science ®Google Scholar
• Ho, J., N. Kalchbrenner, D. Weissenborn, and T. Salimans. 2019. Axial attention in multidimensional transformers. arXiv E-Prints. doi:10.48550/arXiv.1912.12180.
   Google Scholar
• Huang, Z., X. Wang, L. Huang, C. Huang, Y. Wei, and W. Liu. 2019. Ccnet: Criss-cross attention for semantic segmentation. In ICCV. doi:10.48550/arXiv.1811.11721.
   Google Scholar
• Johnson, J. T., P. L. Mackeen, A. Witt, E. D. W. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas. 1998. The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Weather and Forecasting 13 (2):263–76. doi:10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.
   Web of Science ®Google Scholar
• Kingma, D., and J. Ba. 2014. Adam: A method for stochastic optimization. Computer science. arXiv E-Prints. doi:10.48550/arXiv.1412.6980.
   Google Scholar
• Klein, B., L. Wolf, and Y. Afek. 2015. A dynamic convolutional layer for short rangeweather prediction. Computer Vision & Pattern Recognition. IEEE. doi:10.1109/CVPR.2015.7299117.
   Google Scholar
• Lin, Z., M. Li, Z. Zheng, Y. Cheng, and C. Yuan. 2020. Self-attention ConvLSTM for spatiotemporal prediction. Proceedings of the AAAI Conference on Artificial Intelligence 34 (7):11531–38. doi:10.1609/aaai.v34i07.6819.
   Google Scholar
• Li, L., W. Schmid, and J. Joss. 1995. Nowcasting of motion and growth of precipitation with radar over a complex orography. Journal of Applied Meteorology 34 (6):1286–300. doi:10.1175/1520-0450(1995)034<1286:NOMAGO>2.0.CO;2.
   Google Scholar
• Luo, C., X. Li, Y. Wen, Y. Ye, and X. Zhang. 2021. A novel LSTM Model with interaction dual attention for radar echo extrapolation. Remote Sensing 13 (2):164. doi:10.3390/rs13020164.
   Web of Science ®Google Scholar
• Luo, C., X. Li, and Y. Ye. 2020. PFST-LSTM: A SpatioTemporal LSTM Model with pseudoflow prediction for precipitation nowcasting. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 99:1–1. doi:10.1109/JSTARS.2020.3040648.
   Google Scholar
• Mo, S., and N. Atanasov. 2017. A spatiotemporal model with visual attention for video classification. arXiv E-Prints. doi:10.48550/arXiv.1707.02069.
   Google Scholar
• Pulkkinen, S., D. Nerini, A. Pérez Hortal, C. Velasco-Forero, and L. Foresti. 2019. Pysteps – a community-driven open-source library for precipitation nowcasting. doi:10.13140/RG.2.2.31368.67840.
   Google Scholar
• Ranzato, M., A. Szlam, J. Bruna, M. Mathieu, R. Collobert, and S. Chopra. 2014. Video (language) modeling: a baseline for generative models of natural videos. arXiv E-Prints. doi:10.48550/arXiv.1412.6604.
   Google Scholar
• Rinehart, R. E., and E. T. Garvey. 1978. Three-dimensional storm motion detection by conventional weather radar. Nature 273 (5660):287–289. doi:10.1038/273287a0.
   Web of Science ®Google Scholar
• Seed, A. W. 2003. A dynamic and spatial scaling approach to advection forecasting. Journal of Applied Meteorology 42 (3):381–88. doi:10.1175/1520-0450(2003)042<0381:ADASSA>2.0.CO;2.
   Google Scholar
• Shi, X., Z. Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. arXiv e-prints. d oi: arXiv:1506.04214.
   Google Scholar
• Shi, X., Z. Gao, L. Lausen, H. Wang, D. Y. Yeung, W. K. Wong, and W.C. Woo. 2017. Deep learning for precipitation nowcasting: A benchmark and a new model. arXiv e-prints. doi:10.48550/arXiv.1706.03458.
   Google Scholar
• Shi, E., Q. Li, D. Q. Gu, and Z.M. Zhao. 2018. Radar echo extrapolation method based on convolutional neural network. Chinese Journal of Computer Applications 38 (3):6.
   Google Scholar
• Shi, H., Q. Zhou, Y. Ni, X. Wu, and L. J. Latecki. 2021. DPNET: Dual-path network for efficient object detectioj with lightweight self-attention. arXiv E-Prints. doi:10.48550/arXiv.2111.00500.
   Google Scholar
• Sønderby, C. K., L. Espeholt, J. Heek, M. Dehghani, A. Oliver, T. Salimans, S. Agrawal, J. Hickey, and N. Kalchbrenner. 2020. Metnet: A neural weather model for precipitation forecasting. arXiv E-Prints. doi:10.48550/arXiv.2003.12140.
   Google Scholar
• Srivastava, R. K., K. Greff, and J. Schmidhuber. 2015. Training very deep networks. Advances in Neural Information Processing Systems 2377–85. doi:10.48550/arXiv.1507.06228.
   Google Scholar
• Trebing, K., T. Staǹczyk, and S. Mehrkanoon. 2021. SmaAt-UNet: Precipitation nowcasting using a small attention-UNet architecture. Pattern Recognition Letters 145:178–186. doi:10.1016/j.patrec.2021.01.036.
   Web of Science ®Google Scholar
• Tuttle, J. D., and G. B. Foote. 1990. Determination of the boundary layer airflow from a single doppler radar. Journal of Atmospheric and Oceanic Technology 7 (2):218–32. doi:10.1175/1520-0426(1990)007<0218:DOTBLA>2.0.CO;2.
   Web of Science ®Google Scholar
• Wang, Y., Z. Gao, M. Long, J. Wang, and P. S. Yu. 2018. PredRNN++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. arXiv e-prints. doi:10.48550/arXiv.1804.06300.
   Google Scholar
• Wang, X., R. Girshick, A. Gupta, and K. He. 2018. Non-local neural networks. CVPR. doi:10.48550/arXiv.1711.07971.
   Google Scholar
• Wang, Y., M. Long, J. Wang, Z. Gao, and P. S. Yu. 2017. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 879–888.
   Google Scholar
• Wang, Y., H. Wu, J. Zhang, Z. Gao, J. Wang, P. S. Yu, and M. Long. 2021. PredRNN: A recurrent neural network for spatiotemporal predictive learning. arXiv e-prints. doi:10.48550/arXiv.2103.09504.
   Google Scholar
• Wu, K., W. Liang, and S. Q. Wang. 2018. Regional rainfall forecast based on 3D convolutional neural network. Chinese Journal of Image and Signal Processing 7 (4):200–12. doi:10.12677/JISP.2018.74023.
   Google Scholar
• Wu, D., L. Wu, T. Zhang, W. Zhang, J. Huang, and X. Wang. 2022. Short-term rainfall prediction based on radar echo using an improved self-attention PredRNN deep learning model. Atmosphere 13 (12):1963. doi:10.3390/atmos13121963.
   Web of Science ®Google Scholar
• Zhang, J. L. 2020. Research on method of precipitation prediction in severe convective weather based on deep learning. Nanjing University of Science and Technology.
   Google Scholar