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In recent years, methods such as convolutional recurrent neural networks (ConvRNN) in deep learning techniques have been used to solve this task. Despite the effective improvement in forecasting quality, there are still problems with blurred and distorted prediction images, as well as difficulties in effectively forecasting high echo regions. To solve the above problems, this article presents a spatio-temporal long\u2013short-term memory network model in view of multi-scale context fusion and attention mechanisms. This method fully extracts the short-term context information of different scales of radar image through the multi-scale context fusion module. The attention module broadens the time perception domain of the prediction unit so that the model perceives more historical time dynamics. Using the Hong Kong region weather radar data as a sample, the results of the experimental comparative analysis show that the spatio-temporal long and short-term memory network in view of multi-scale context fusion and attention mechanism achieves better prediction performance. Our model is effective in improving both image quality and meteorological assessment metrics with higher accuracy and more details.<\/jats:p>","DOI":"10.3390\/rs16020376","type":"journal-article","created":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T07:41:28Z","timestamp":1705477288000},"page":"376","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Long Short-Term Memory Model with Multi-Scale Context Fusion and Attention for Radar Echo Extrapolation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2179-5119","authenticated-orcid":false,"given":"Guangxin","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), and International Joint Research Laboratory on Climate and Environment Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Guangzhou Institute of Tropical Marine Meteorology, China Meteorological Administration, Guangzhou 510080, China"},{"name":"Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Hebei Provincial Institute of Meteorological Sciences, Shijiazhuang 050021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Qu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), and International Joint Research Laboratory on Climate and Environment Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Guangzhou Institute of Tropical Marine Meteorology, China Meteorological Administration, Guangzhou 510080, China"},{"name":"Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Hebei Provincial Institute of Meteorological Sciences, Shijiazhuang 050021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjia","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), and International Joint Research Laboratory on Climate and Environment Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), and International Joint Research Laboratory on Climate and Environment Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Guangzhou Institute of Tropical Marine Meteorology, China Meteorological Administration, Guangzhou 510080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), and International Joint Research Laboratory on Climate and Environment Change (ILCEC), and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Singh, S., Sarkar, S., and Mitra, P. (2017, January 23\u201328). A deep learning based approach with adversarial regularization for Doppler weather radar ECHO prediction. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128174"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1175\/1520-0469(1948)005<0165:TDORWS>2.0.CO;2","article-title":"The distribution of raindrops with size","volume":"5","author":"Marshall","year":"1948","journal-title":"J. Meteor."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/273287a0","article-title":"Three-dimensional storm motion detection by conventional weather radar","volume":"273","author":"Rinehart","year":"1978","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1175\/JTECH-D-18-0194.1","article-title":"A method of radar echo extrapolation based on TREC and Barnes filter","volume":"36","author":"Zou","year":"2019","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1175\/2008JTECHA1153.1","article-title":"An efficient, general-purpose technique for identifying storm cells in geospatial images","volume":"26","author":"Lakshmanan","year":"2009","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1175\/MWR-D-19-0193.1","article-title":"Improving radar echo Lagrangian extrapolation nowcasting by blending numerical model wind information: Statistical performance of 16 typhoon cases","volume":"148","author":"Chung","year":"2020","journal-title":"Mon. Weather. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Woo, W., and Wong, W. (2017). Operational application of optical flow techniques to radar-based rainfall nowcasting. Atmosphere, 8.","DOI":"10.3390\/atmos8030048"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.5194\/gmd-12-1387-2019","article-title":"Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)","volume":"12","author":"Ayzel","year":"2019","journal-title":"Geosci. Model Dev."},{"key":"ref_9","first-page":"26950","article-title":"A Motion-Aware Unit for Video Prediction and Beyond","volume":"34","author":"Chang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tamaru, R., Siritanawan, P., and Kotani, K. (2021, January 17\u201320). Interaction Aware Relational Representations for Video Prediction. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Melbourne, Australia.","DOI":"10.1109\/SMC52423.2021.9658846"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bei, X., Yang, Y., and Soatto, S. (2021, January 20\u201325). Learning semantic-aware dynamics for video prediction. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00096"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1049\/itr2.12044","article-title":"Spatial-temporal attention wavenet: A deep learning framework for traffic prediction considering spatial-temporal dependencies","volume":"15","author":"Tian","year":"2021","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4927","DOI":"10.1109\/TITS.2021.3054840","article-title":"Deep learning on traffic prediction: Methods, analysis and future directions","volume":"23","author":"Yin","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117511","DOI":"10.1016\/j.eswa.2022.117511","article-title":"Attention-based dynamic spatial-temporal graph convolutional networks for traffic speed forecasting","volume":"204","author":"Zhao","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_15","first-page":"107","article-title":"Short-term rainfall prediction method based on neural network and model ensemble","volume":"7","author":"Guo","year":"2017","journal-title":"Adv. Meteor. Sci. Technol."},{"key":"ref_16","first-page":"317","article-title":"Application of deep learning network in precipitation phase identification and prediction","volume":"47","author":"Huang","year":"2021","journal-title":"Meteor. Mon."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s11442-017-1369-5","article-title":"Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001\u20132013","volume":"27","author":"Guo","year":"2017","journal-title":"J. Geogr. Sci."},{"key":"ref_18","first-page":"60","article-title":"Research on hourly precipitation forecast correction method based on convolutional neural network","volume":"47","author":"Chen","year":"2021","journal-title":"Meteor. Mon."},{"key":"ref_19","first-page":"93","article-title":"Meteorological radar echo extrapolation based on ConvLSTM","volume":"39","author":"Li","year":"2021","journal-title":"J. Qinghai Univ."},{"key":"ref_20","first-page":"18","article-title":"An improved recurrent neural network radar image extrapolation algorithm","volume":"49","author":"Yin","year":"2021","journal-title":"Meteor. Sci. Technol."},{"key":"ref_21","first-page":"817","article-title":"Extrapolation and effect analysis of weather radar echo sequence based on deep learning","volume":"27","author":"Huang","year":"2021","journal-title":"Acta Meteor. Sin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/JSTARS.2020.3040648","article-title":"A spatiotemporal LSTM model with pseudo flow prediction for precipitation nowcasting","volume":"14","author":"Luo","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1038\/s41586-021-03854-z","article-title":"Skilful precipitation nowcasting using deep generative models of radar","volume":"597","author":"Ravuri","year":"2021","journal-title":"Nature"},{"key":"ref_24","unstructured":"Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., and Azizzadenesheli, K. (2022). Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., and Alet, F. (2022). GraphCast: Learning skillful medium-range global weather forecasting. arXiv.","DOI":"10.1126\/science.adi2336"},{"key":"ref_26","unstructured":"Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q. (2022). Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. arXiv."},{"key":"ref_27","unstructured":"Andrychowicz, M., Espeholt, L., Li, D., Merchant, S., Merose, A., Zyda, F., Agrawal, S., and Kalchbrenner, N. (2023). Deep Learning for Day Forecasts from Sparse Observations. arXiv."},{"key":"ref_28","first-page":"322","article-title":"SwinRDM: Integrate SwinRNN with diffusion model towards high-resolution and high-quality weather forecasting","volume":"37","author":"Chen","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_29","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., and Woo, W. (2015, January 7\u201312). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1109\/TPAMI.2022.3165153","article-title":"Predrnn: A Recurrent Neural Network for Spatiotemporal Predictive Learning","volume":"45","author":"Wang","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","unstructured":"Wang, Y., Gao, Z., and Long, M. (2018, January 10\u201315). Predrnn++: Towards a resolution of thedeep-in-time dilemma in spatiotemporal predictive learning. Proceedings of the International Conference on Machine Learning, Stockholm, Sweden."},{"key":"ref_32","unstructured":"Wang, Y., Jiang, L., and Yang, M. (May, January 30). Eidetic 3D LSTM: A model for video prediction and beyond. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., and Zhu, H. (2019, January 15\u201320). Memory in memory: A predictive neural network for learning higher-order non-stationarity from spatiotemporal dynamics. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00937"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107900","DOI":"10.1016\/j.knosys.2021.107900","article-title":"Predrann: The spatiotemporal attention convolution recurrent neural network for precipitation nowcasting","volume":"239","author":"Luo","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isatra.2022.06.046","article-title":"A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars","volume":"132","author":"Yang","year":"2023","journal-title":"ISA Trans."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4109108","DOI":"10.1109\/TGRS.2022.3198222","article-title":"Preciplstm: A meteorological spatiotemporal lstm for precipitation nowcasting","volume":"60","author":"Ma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/376\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:50Z","timestamp":1760104130000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/376"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,17]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020376"],"URL":"https:\/\/doi.org\/10.3390\/rs16020376","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,17]]}}}