Abstract
The traditional technology of radar echo image extrapolation for rainfall nowcasting faces such problems as insufficiently high accuracy, the incomplete analysis of the data on radar echo images, and the image blurring from the stacked LSTM (Long Short-Term Memory). In order to more accurately and clearly predict the radar echo image at a future moment, an adversarial prediction network based on multi-scale U-shaped encoder-decoder is proposed. To overcome the problem of insufficient details of the predicted image, the generator of the network adopts a U-shaped encoder-decoder structure with jump-layer connection. At the same time, in order to capture the echo movement at different scales, multi-scale convolution kernels is introduced to the encoder-decoder units. Then the conventional discriminator structure is improved and stacked ConvLSTM(Convolutional Long Short-Term Memory) layers were proposed to classify sequence. Based on the prediction of next ten frames from the given ten frames of images, this paper tests the network on the SRAD(Standardized Radar Dataset), and compares the prediction results of different networks. The test results show that the proposed model reduces image blurring, enhances the prediction accuracy while retaining sufficient prediction details .
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Kaoji, X.: Doppler weather radar echo images clutter suppression and storm clouds segmentation and tracking. M.S. Thesis, Tianjin University, Tianjin, China (2012)
Zhang, L., Huang, Z., Liu, W., Guo, Z., Zhang, Z.: Weather radar echo prediction method based on convolution neural network and long short-term memory networks for sustainable e-agriculture. J. Clean. Prod. 298, 126776 (2021). ISSN 0959-6526
Dixon, M., Wiener, G.: TITAN: thunderstorm identification, tracking, analysis, and nowcasting—a radar-based methodology. J. Atmosph. Oceanic Technol. https://doi.org/10.1175/1520-0426(1993)0102.0.CO
Jacobs, I.S., Bean, C.P.: Fine particles, thin films and exchange anisotropy. In: Rado, G.T., Suhl, H. (eds.) Magnetism, vol. III, pp. 271–350. Academic, New York (1963)
Yuanyi, X.: Research on tracking and extrapolation of rain clouds based on Doppler radar images. M.S. Thesis, Wuhan University of Technology, Wuhan, China (2012)
Srivastava, N., Mansimov, E., Salakhutdinov, R.: Unsupervised learning of video representations using LSTMs. In: Proceedings of International Conference on Machine Learning, pp. 843–852 (2015)
Xingjian, S., Chen, Z., Wang, H.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Proceedings of Advances Neural Information Processing System, pp. 802–810 (2015)
Singh, S., Sarkar, S., Mitra, P.: A deep learning based approach with adversarial regularization for Doppler weather radar ECHO prediction. In: Proceedings of the 2017 IEEE Geoscience and Remote Sensing Symposium (IGARSS), pp. 5205–5208 (2017)
Georgy, A., Tobias, S., Maik, H.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting. Geoentific Model Dev. https://doi.org/10.5194/gmd-13-2631-2020
Staniforth, A., Ct, J.: Semi-lagrangian integration schemes for atmospheric models a review. Mon. Weather Rev. 119(9), 2206–2223 (1991)
Woo, W.C., Wong, W.K.: Operational application of optical flow techniques to radar-based rainfall nowcasting. Atmosphere. https://doi.org/10.3390/atmos8030048
Zhang, C., Zhou, X., Zhuge, X., Xu, M.: Learnable optical flow network for radar echo extrapolation. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 14, 1260–1266 (2021). https://doi.org/10.1109/JSTARS.2020.3031244
Sivakumar, B.: Rainfall dynamics at different temporal scales: a chaotic perspective. Hydrol. Earth Syst. Sci. 5(4), 645–652 (2001)
Li, Y., Wang, Z., Dai, G., Wu, S., Yu, S., Xie, Y.: Evaluation of realistic blurring image quality by using a shallow convolutional neural network. IEEE Int. Conf. Inf. Autom. (ICIA) 2017, 853–857 (2017). https://doi.org/10.1109/ICInfA.2017.8079022
Bingcong, L.: Research on doppler radar map estimation and forecasting based on variational autoencoder. M.S. Thesis, Guangdong University, Guangdong, China (2019)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Proceedings of ICML, Sydney, NSW, Australia, pp. 214–223 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, Alejandro F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Piao, S., Liu, J.: Accuracy improvement of UNet based on dilated convolution. J. Phys.: Conf. Ser. 1345, 5. IOP Publishing (2019)
Sun, S., Mu, L., Wang, L., Liu, P.: L-UNet: an LSTM network for remote sensing image change detection. In: IEEE Geoscience and Remote Sensing Letters, pp. 1–5 (Early Access). https://doi.org/10.1109/LGRS.2020.3041530
Hualian, F.U., et al.: Cloud detection method of FY-2G satellite images based on random forest. Bull. Surveying and Mapp. 3, 61–66 (2019). https://doi.org/10.13474/j.cnki.11-2246.2019.0079
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Mou, X., Liu, C., Zhou, X. (2021). Radar Echo Image Prediction Algorithm Based on Multi-scale Encoding-Decoding Network. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_61
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