{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T10:27:25Z","timestamp":1781087245662,"version":"3.54.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2022YFB3305401"],"award-info":[{"award-number":["2022YFB3305401"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s10994-023-06445-3","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T13:02:28Z","timestamp":1705064548000},"page":"3399-3417","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Hierarchical U-net with re-parameterization technique for spatio-temporal weather forecasting"],"prefix":"10.1007","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8868-3840","authenticated-orcid":false,"given":"Baowen","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuelei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengbao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"issue":"6425","key":"6445_CR1","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1126\/science.aav7274","volume":"363","author":"RB Alley","year":"2019","unstructured":"Alley, R. B., Emanuel, K. A., & Zhang, F. (2019). Advances in weather prediction. Science, 363(6425), 342\u2013344.","journal-title":"Science"},{"issue":"13","key":"6445_CR2","doi-asserted-by":"publisher","first-page":"2493","DOI":"10.1175\/1520-0442(2004)017<2493:RATCPP>2.0.CO;2","volume":"17","author":"A Arakawa","year":"2004","unstructured":"Arakawa, A. (2004). The cumulus parameterization problem: Past, present, and future. Journal of Climate, 17(13), 2493\u20132525.","journal-title":"Journal of Climate"},{"key":"6445_CR3","unstructured":"Bauer, P., Quintino, T., Wedi, N., Bonanni, A., Chrust, M.,\u00a0Deconinck, W., Diamantakis, M., D\u00fcben, P., English, S., & Flemming, J. et\u00a0al. (2020). The ECMWF scalability programme: Progress and plans. European Centre for Medium Range Weather Forecasts."},{"issue":"7567","key":"6445_CR4","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1038\/nature14956","volume":"525","author":"P Bauer","year":"2015","unstructured":"Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47\u201355.","journal-title":"Nature"},{"issue":"419","key":"6445_CR5","first-page":"178","volume":"99","author":"A Betts","year":"1973","unstructured":"Betts, A. (1973). Non-precipitating cumulus convection and its parameterization. Quarterly Journal of the Royal Meteorological Society, 99(419), 178\u2013196.","journal-title":"Quarterly Journal of the Royal Meteorological Society"},{"key":"6445_CR6","unstructured":"Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., & Tian, Q. ( 2022) Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. arXiv preprint arXiv:2211.02556.."},{"key":"6445_CR7","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., G.\u00a0Heigold, G., & Gelly S. et\u00a0al., (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929."},{"key":"6445_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105151","volume":"115","author":"MA Ganaie","year":"2022","unstructured":"Ganaie, M. A., Hu, M., Malik, A., Tanveer, M., & Suganthan, P. (2022). Ensemble deep learning: A review. Engineering Applications of Artificial Intelligence, 115, 105151.","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"6445_CR9","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. R., & Xu, D. (2021). Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes. (2021). Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021. Revised Selected Papers, Part I.,2022, 272\u2013284.","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"6445_CR10","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning. Image Recognition, vol.\u00a07."},{"issue":"2","key":"6445_CR11","doi-asserted-by":"publisher","first-page":"e2022MS003211","DOI":"10.1029\/2022MS003211","volume":"15","author":"Y Hu","year":"2023","unstructured":"Hu, Y., Chen, L., Wang, Z., & Li, H. (2023). Swinvrnn: A data-driven ensemble forecasting model via learned distribution perturbation. Journal of Advances in Modeling Earth Systems, 15(2), e2022MS003211.","journal-title":"Journal of Advances in Modeling Earth Systems"},{"key":"6445_CR12","unstructured":"Keisler, R. (2022). Forecasting global weather with graph neural networks. arXiv preprint arXiv:2202.07575."},{"issue":"7","key":"6445_CR13","doi-asserted-by":"publisher","first-page":"7470","DOI":"10.1609\/aaai.v36i7.20711","volume":"36","author":"H Lin","year":"2022","unstructured":"Lin, H., Gao, Z., Xu, Y., Wu, L., Li, L., & Li, S. Z. (2022). Conditional local convolution for spatio-temporal meteorological forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7470\u20137478.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"6445_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"7","key":"6445_CR15","doi-asserted-by":"publisher","first-page":"3431","DOI":"10.1016\/j.jcp.2007.02.034","volume":"227","author":"P Lynch","year":"2008","unstructured":"Lynch, P. (2008). The origins of computer weather prediction and climate modeling. Journal of Computational Physics, 227(7), 3431\u20133444.","journal-title":"Journal of Computational Physics"},{"issue":"529","key":"6445_CR16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1002\/qj.49712252905","volume":"122","author":"F Molteni","year":"1996","unstructured":"Molteni, F., Buizza, R., Palmer, T. N., & Petroliagis, T. (1996). The ecmwf ensemble prediction system: Methodology and validation. Quarterly Journal of the Royal Meteorological Society, 122(529), 73\u2013119.","journal-title":"Quarterly Journal of the Royal Meteorological Society"},{"key":"6445_CR17","unstructured":"Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J.\u00a0K., & Grover, A. (2023). Climax: A foundation model for weather and climate. arXiv preprint arXiv:2301.10343."},{"key":"6445_CR18","unstructured":"Pathak, J., Subra manian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., & Azizzadenesheli K. et\u00a0al.,(2022) Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11214."},{"key":"6445_CR19","doi-asserted-by":"crossref","unstructured":"Pincus, R., Barker, H.\u00a0W., & Morcrette, J.-J.(2003). A fast, flexible, approximate technique for computing radiative transfer in inhomogeneous cloud fields. Journal of Geophysical Research: Atmospheres, vol. 108, no. D13, .","DOI":"10.1029\/2002JD003322"},{"issue":"11","key":"6445_CR20","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1175\/BAMS-84-11-1547","volume":"84","author":"D Randall","year":"2003","unstructured":"Randall, D., Khairoutdinov, M., Arakawa, A., & Grabowski, W. (2003). Breaking the cloud parameterization deadlock. Bulletin of the American Meteorological Society, 84(11), 1547\u20131564.","journal-title":"Bulletin of the American Meteorological Society"},{"key":"6445_CR21","doi-asserted-by":"crossref","unstructured":"Rasp, S., Thuerey, N. (2021) Data-driven medium-range weather prediction with a resnet pretrained on climate simulations: A new model for weatherbench. Journal of Advances in Modeling Earth Systems, vol.\u00a013, no.\u00a02, p. e2020MS002405.","DOI":"10.1029\/2020MS002405"},{"issue":"11","key":"6445_CR22","doi-asserted-by":"publisher","first-page":"e2020MS002203","DOI":"10.1029\/2020MS002203","volume":"12","author":"S Rasp","year":"2020","unstructured":"Rasp, S., Dueben, P. .\u00a0D., Scher, S., Weyn, J. .\u00a0A., Mouatadid, S., & Thuerey, N. (2020). Weatherbench: A benchmark data set for data-driven weather forecasting. Journal of Advances in Modeling Earth Systems, 12(11), e2020MS002203.","journal-title":"Journal of Advances in Modeling Earth Systems"},{"issue":"2","key":"6445_CR23","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1175\/1520-0493(1995)123<0489:IOTSLM>2.0.CO;2","volume":"123","author":"H Ritchie","year":"1995","unstructured":"Ritchie, H., Temperton, C., Simmons, A., Hortal, M., Davies, T., Dent, D., & Hamrud, M. (1995). Implementation of the semi-lagrangian method in a high-resolution version of the ecmwf forecast model. Monthly Weather Review, 123(2), 489\u2013514.","journal-title":"Monthly Weather Review"},{"key":"6445_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation,in Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015. Proceedings, Part,III(18), 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"6445_CR25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O, Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., W.\u00a0M.\u00a0W. III, & Frangi, A.\u00a0F., (Eds.), Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015\u201418th International Conference Munich, Germany, October 5 - 9, 2015, Proceedings, Part III, ser. Lecture Notes in Computer Science, vol. 9351. Springer, , pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2194","key":"6445_CR26","doi-asserted-by":"publisher","first-page":"20200097","DOI":"10.1098\/rsta.2020.0097","volume":"379","author":"MG Schultz","year":"2021","unstructured":"Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., Mozaffari, A., & Stadtler, S. (2021). Can deep learning beat numerical weather prediction? Philosophical Transactions of the Royal Society A, 379(2194), 20200097.","journal-title":"Philosophical Transactions of the Royal Society A"},{"key":"6445_CR27","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-c. (2015) Convolutional lstm network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, vol.\u00a028, ."},{"key":"6445_CR28","doi-asserted-by":"crossref","unstructured":"Weyn, J.\u00a0A., Durran, D.\u00a0R., Caruana, R., & Cresswell-Clay, N. (2021) Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models. Journal of Advances in Modeling Earth Systems, vol.\u00a013, no.\u00a07, p. e2021MS002502.","DOI":"10.1029\/2021MS002502"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06445-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-023-06445-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-023-06445-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T18:04:22Z","timestamp":1764266662000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-023-06445-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":28,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["6445"],"URL":"https:\/\/doi.org\/10.1007\/s10994-023-06445-3","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]},"assertion":[{"value":"16 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}