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Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset.<\/jats:p>","DOI":"10.3390\/rs16193597","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T11:40:27Z","timestamp":1727350827000},"page":"3597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MAFNet: Multimodal Asymmetric Fusion Network for Radar Echo Extrapolation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3907-2713","authenticated-orcid":false,"given":"Yanle","family":"Pei","sequence":"first","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"},{"name":"The High Impact Weather Key Laboratory of China Meteorological Administration (CMA), Changsha 410005, China"}]},{"given":"Qian","family":"Li","sequence":"additional","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"},{"name":"The High Impact Weather Key Laboratory of China Meteorological Administration (CMA), Changsha 410005, China"}]},{"given":"Yayi","family":"Wu","sequence":"additional","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"},{"name":"The High Impact Weather Key Laboratory of China Meteorological Administration (CMA), Changsha 410005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1211-1846","authenticated-orcid":false,"given":"Xuan","family":"Peng","sequence":"additional","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"},{"name":"The High Impact Weather Key Laboratory of China Meteorological Administration (CMA), Changsha 410005, China"}]},{"given":"Shiqing","family":"Guo","sequence":"additional","affiliation":[{"name":"The College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410005, China"},{"name":"The High Impact Weather Key Laboratory of China Meteorological Administration (CMA), Changsha 410005, China"}]},{"given":"Chengzhi","family":"Ye","sequence":"additional","affiliation":[{"name":"The High Impact Weather Key Laboratory of China Meteorological Administration (CMA), Changsha 410005, China"},{"name":"The Institute of Meteorological Sciences of Hunan Province, Changsha 410118, China"}]},{"given":"Tianying","family":"Wang","sequence":"additional","affiliation":[{"name":"The High Impact Weather Key Laboratory of China Meteorological Administration (CMA), Changsha 410005, China"},{"name":"The Institute of Meteorological Sciences of Hunan Province, Changsha 410118, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2022.3141498","article-title":"ED-DRAP: Encoder\u2013Decoder Deep Residual Attention Prediction Network for Radar Echoes","volume":"19","author":"Che","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, F., Wang, X., Guan, J., Wu, M., and Guo, L.J.S. (2021). RN-Net: A deep learning approach to 0\u20132 hour rainfall nowcasting based on radar and automatic weather station data. Sensors, 21.","DOI":"10.3390\/s21061981"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1002\/qj.4429","article-title":"Assessing the impact of a NWP warm-start system on model spin-up over tropical Africa","volume":"149","author":"Warner","year":"2023","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6781","DOI":"10.1109\/JSTARS.2022.3194522","article-title":"Focal Frame Loss: A Simple but Effective Loss for Precipitation Nowcasting","volume":"15","author":"Ma","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3500790","article-title":"VRNet: A Vivid Radar Network for Precipitation Nowcasting","volume":"62","author":"Fang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","first-page":"1","article-title":"REMNet: Recurrent Evolution Memory-Aware Network for Accurate Long-Term Weather Radar Echo Extrapolation","volume":"60","author":"Jing","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"453","DOI":"10.32604\/iasc.2021.016589","article-title":"AttEF: Convolutional LSTM Encoder-Forecaster with Attention Module for Precipitation Nowcasting","volume":"29","author":"Fang","year":"2021","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.neucom.2020.09.060","article-title":"STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting","volume":"426","author":"Castro","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Song, K., Yang, G., Wang, Q., Xu, C., Liu, J., Liu, W., Shi, C., Wang, Y., Zhang, G., and Yu, X. (2019, January 8\u201311). Deep Learning Prediction of Incoming Rainfalls: An Operational Service for the City of Beijing China. Proceedings of the 2019 International Conference on Data Mining Workshops (ICDMW), Beijing, China.","DOI":"10.1109\/ICDMW.2019.00036"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.procs.2019.02.036","article-title":"All convolutional neural networks for radar-based precipitation nowcasting","volume":"150","author":"Ayzel","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_11","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W., and Woo, W. (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. arXiv."},{"key":"ref_12","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":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","first-page":"1","article-title":"STUNNER: Radar Echo Extrapolation Model Based on Spatiotemporal Fusion Neural Network","volume":"61","author":"Fang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, J., Zhu, H., Long, M., Wang, J., and Yu, P.S. (2019, January 15\u201320). Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00937"},{"key":"ref_15","first-page":"1","article-title":"Dynamic Multiscale Fusion Generative Adversarial Network for Radar Image Extrapolation","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230051","article-title":"Experimental Study on Generative Adversarial Network for Precipitation Nowcasting","volume":"60","author":"Luo","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, L., Niu, D., Zhang, T., Chen, P., Chen, X., and Li, Y. (2022). Two-Stage UA-GAN for Precipitation Nowcasting. Remote Sens., 14.","DOI":"10.3390\/rs14235948"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2737","DOI":"10.5194\/gmd-16-2737-2023","article-title":"CLGAN: A generative adversarial network (GAN)-based video prediction model for precipitation nowcasting","volume":"16","author":"Ji","year":"2023","journal-title":"Geosci. Model Dev."},{"key":"ref_19","first-page":"1","article-title":"TempEE: Temporal\u2013Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Autoregression","volume":"61","author":"Chen","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3500588","article-title":"SFTformer: A Spatial-Frequency-Temporal Correlation-Decoupling Transformer for Radar Echo Extrapolation","volume":"62","author":"Xu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"1","article-title":"Rainformer: Features Extraction Balanced Network for Radar-Based Precipitation Nowcasting","volume":"19","author":"Bai","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","unstructured":"AMS (2001). National Weather Service proposes modernization of cooperative weather observer program. Bull. Am. Meteorol. Soc., 82, 715."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jurczyk, A., Szturc, J., Otop, I., O\u015br\u00f3dka, K., and Struzik, P. (2020). Quality-Based Combination of Multi-Source Precipitation Data. Remote Sens., 12.","DOI":"10.3390\/rs12111709"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, F., Wang, X., and Guan, J. (2021). A Novel Multi-Input Multi-Output Recurrent Neural Network Based on Multimodal Fusion and Spatiotemporal Prediction for 0\u20134 Hour Precipitation Nowcasting. Atmosphere, 12.","DOI":"10.3390\/atmos12121596"},{"key":"ref_25","first-page":"1","article-title":"MM-RNN: A Multimodal RNN for Precipitation Nowcasting","volume":"61","author":"Ma","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7002","DOI":"10.1109\/JSTARS.2024.3376987","article-title":"FsrGAN: A Satellite and Radar-Based Fusion Prediction Network for Precipitation Nowcasting","volume":"17","author":"Niu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1109\/TCSVT.2021.3075745","article-title":"Efficient and Model-Based Infrared and Visible Image Fusion via Algorithm Unrolling","volume":"32","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","article-title":"FusionGAN: A generative adversarial network for infrared and visible image fusion","volume":"48","author":"Ma","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_29","unstructured":"Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.-Y., Wong, W.-k., and Woo, W.-c. (2017). Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model. arXiv."},{"key":"ref_30","first-page":"22009","article-title":"Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology","volume":"33","author":"Veillette","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","first-page":"14902","article-title":"RainBench: Towards Global Precipitation Forecasting from Satellite Imagery","volume":"35","author":"Tong","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Choi, Y., Cha, K., Back, M., Choi, H., and Jeon, T. (2021, January 11\u201316). Rain-F: A Fusion Dataset for Rainfall Prediction Using Convolutional Neural Network. Proceedings of the IGARSS 2021\u20132021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9555094"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Choi, Y., Cha, K., Back, M., Choi, H., and Jeon, T. (2021). RAIN-F+: The Data-Driven Precipitation Prediction Model for Integrated Weather Observations. Remote Sens., 13.","DOI":"10.3390\/rs13183627"},{"key":"ref_34","unstructured":"Larvor, G., Berthomier, L., Chabot, V., Le Pape, B., Pradel, B., and Perez, L. (2024, May 06). MeteoNet, an open reference weather dataset by Meteo-France. Available online: https:\/\/meteonet.umr-cnrm.fr\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8286","DOI":"10.1109\/JSTARS.2023.3310361","article-title":"A Heterogeneous Spatiotemporal Attention Fusion Prediction Network for Precipitation Nowcasting","volume":"16","author":"Niu","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, D., Han, Q., Zhang, X., and Zhang, Q. (2020, January 27\u201329). Time series assessment of multi-source spatiotemporal fusion reconstruction data based on dynamic time warping. Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China.","DOI":"10.1109\/ICAICA50127.2020.9182494"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Bai, H., Zhang, J., Zhang, Y., Xu, S., Lin, Z., Timofte, R., and Van Gool, L. (2023, January 17\u201324). CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00572"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"6025","DOI":"10.1109\/TMM.2023.3344354","article-title":"Text-to-Image Person Re-Identification Based on Multimodal Graph Convolutional Network","volume":"26","author":"Han","year":"2024","journal-title":"IEEE Trans. Multimed."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xu, G., Jiang, X., Li, X., Zhang, Z., and Liu, X. (2023). Exploring Self-Supervised Learning for Multi-Modal Remote Sensing Pre-Training via Asymmetric Attention Fusion. Remote Sens., 15.","DOI":"10.3390\/rs15245682"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7608","DOI":"10.1109\/JSTARS.2024.3381822","article-title":"Spatiotemporal Enhanced Adversarial Network for Precipitation Nowcasting","volume":"17","author":"Zhou","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1038\/s41586-023-06184-4","article-title":"Skilful nowcasting of extreme precipitation with NowcastNet","volume":"619","author":"Zhang","year":"2023","journal-title":"Nature"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e2021JD035498","DOI":"10.1029\/2021JD035498","article-title":"Climatology of the Vertical Profiles of Polarimetric Radar Variables and Retrieved Microphysical Parameters in Continental\/Tropical MCSs and Landfalling Hurricanes","volume":"127","author":"Hu","year":"2022","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e2022JD037772","DOI":"10.1029\/2022JD037772","article-title":"Regime-Specific Cloud Vertical Overlap Characteristics From Radar and Lidar Observations at the ARM Sites","volume":"128","author":"Balmes","year":"2023","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/TASSP.1975.1162641","article-title":"Minimum prediction residual principle applied to speech recognition","volume":"23","author":"Itakura","year":"1975","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"122432","DOI":"10.1016\/j.eswa.2023.122432","article-title":"ESDTW: Extrema-based shape dynamic time warping","volume":"239","author":"Qiu","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"109804","DOI":"10.1016\/j.patcog.2023.109804","article-title":"Constrained DTW preserving shapelets for explainable time-series clustering","volume":"143","author":"Lampert","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107093","DOI":"10.1016\/j.atmosres.2023.107093","article-title":"MCGLN: A multimodal ConvLSTM-GAN framework for lightning nowcasting utilizing multi-source spatiotemporal data","volume":"297","author":"Lu","year":"2024","journal-title":"Atmos. Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Geng, Y.-a., Li, Q., Lin, T., Jiang, L., Xu, L., Zheng, D., Yao, W., Lyu, W., and Zhang, Y. (2019, January 4\u20138). LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction. Proceedings of the KDD \u201819: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330717"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1109\/LRA.2020.2967738","article-title":"Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data","volume":"5","author":"Narayanan","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"e2021GL095302","DOI":"10.1029\/2021GL095302","article-title":"Improving Nowcasting of Convective Development by Incorporating Polarimetric Radar Variables into a Deep-Learning Model","volume":"48","author":"Pan","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., and Yang, M.-H. (2022, January 18\u201324). Restormer: Efficient Transformer for High-Resolution Image Restoration. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Guo, S., Sun, N., Pei, Y., and Li, Q. (2023). 3D-UNet-LSTM: A Deep Learning-Based Radar Echo Extrapolation Model for Convective Nowcasting. Remote Sens., 15.","DOI":"10.3390\/rs15061529"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"104485","DOI":"10.1016\/j.dsp.2024.104485","article-title":"Dual-former: Hybrid self-attention transformer for efficient image restoration","volume":"149","author":"Chen","year":"2024","journal-title":"Digit. Signal Process."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6937","DOI":"10.1109\/TGRS.2017.2737033","article-title":"OSSIM: An Object-Based Multiview Stereo Algorithm Using SSIM Index Matching Cost","volume":"55","author":"Fei","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"121346","DOI":"10.1016\/j.eswa.2023.121346","article-title":"MOD-YOLO: Rethinking the YOLO architecture at the level of feature information and applying it to crack detection","volume":"237","author":"Su","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021, January 3\u20138). Attentional Feature Fusion. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2005GL023451","DOI":"10.1029\/2005GL023451","article-title":"Precipitation forecast skill of numerical weather prediction models and radar nowcasts","volume":"32","author":"Lin","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1175\/2009WAF2222350.1","article-title":"Equitability Revisited: Why the \u201cEquitable Threat Score\u201d Is Not Equitable","volume":"25","author":"Hogan","year":"2010","journal-title":"Weather. Forecast."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Hore, A., and Ziou, D. (2010, January 23\u201326). Image Quality Metrics: PSNR vs. SSIM. Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR), Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.579"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wu, H., Yao, Z., Wang, J., and Long, M. (2021, January 20\u201325). MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01518"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.patrec.2021.01.036","article-title":"SmaAt-UNet: Precipitation nowcasting using a small attention-UNet architecture","volume":"145","author":"Trebing","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_62","first-page":"25390","article-title":"Earthformer: Exploring Space-Time Transformers for Earth System Forecasting","volume":"35","author":"Gao","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_63","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3597\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:04:16Z","timestamp":1760112256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,26]]},"references-count":63,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193597"],"URL":"https:\/\/doi.org\/10.3390\/rs16193597","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,26]]}}}