计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (3): 112-126.doi: 10.11896/jsjkx.240900095

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于深度学习的气象预报模型研究综述

王嫄1, 霍鹏1, 韩毅2, 陈暾2, 汪祥2, 温辉1   

  1. 1 天津科技大学人工智能学院 天津 300457
    2 国防科技大学气象海洋学院 长沙 410073
  • 收稿日期:2024-09-14 修回日期:2024-11-28 出版日期:2025-03-15 发布日期:2025-03-07
  • 通讯作者: 韩毅(hanyi12@nudt.edu.cn)
  • 作者简介:(wangyuan23@tust.edu.cn)
  • 基金资助:
    国家自然科学基金(62372460);湖南省自然科学基金(2024JJ4042);国防科技大学青年自主创新科学基金(ZK24-53)

Survey on Deep Learning-based Meteorological Forecasting Models

WANG Yuan1, HUO Peng1, HAN Yi2, CHEN Tun2, WANG Xiang2, WEN Hui1   

  1. 1 College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,China
    2 College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410073,China
  • Received:2024-09-14 Revised:2024-11-28 Online:2025-03-15 Published:2025-03-07
  • About author:WANG Yuan,born in 1989,Ph.D,associate professor.Her main research interests include intelligent science and natural language processing.
    HAN Yi,born in 1993,Ph.D,assistant researcher.His main research interests include intelligent forecasting of me-teorology and oceans,and knowledge graphs.
  • Supported by:
    National Natural Science Foundation of China(62372460),Natural Science Foundation of Hunan Province,China(2024JJ4042) and Youth Independent Innovation Science Fund of the National University of Defense Technology(ZK24-53).

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摘要： 实时准确的气象预报关乎人民生计、环境生态以及军事决策,受到各界人士的广泛关注和重点研究。数值气象预报是当前的主流预报方法,经过长期发展,其预报精确性和可靠性不断提高,但仍然面临系统误差无法避免、历史观测数据难以利用,以及计算开销巨大等重大挑战。随着深度学习技术的快速兴起,数据驱动的人工智能方法逐渐应用于气象预报领域,为应对上述挑战提供了全新技术手段。基于上述背景,文中全面总结了数值气象预报和深度学习气象预报的研究现状,系统梳理了深度学习气象预报模型的相关概念和输入数据,详细阐述了应用于各类气象预报任务的代表性模型,深入对比了不同模型的技术架构和性能指标,并且分析讨论了该领域目前面临的挑战和未来发展的方向,旨在为相关研究提供参考。

关键词: 气象预测, 深度学习, 大模型, AI4Science

Abstract: Accurate and timely weather forecasting is crucial for people’s livelihoods,environmental ecology,and military decision-making,attracting extensive attention and focused research from various sectors.Numerical weather prediction(NWP) is currently the mainstream forecasting method.Over long-term development,the accuracy and reliability of NWP have continuously improved.However,it still faces significant challenges,such as unavoidable systematic errors,ineffective utilization of historical observation data,and substantial computational costs.With the rapid rise of deep learning,data-driven artificial intelligence me-thods are gradually being applied to the field of weather forecasting,offering novel techniques to overcome these challenges.Against this backdrop,this paper comprehensively summarizes the current research status of NWP and deep learning-based weather forecasting.It systematically reviews the relevant concepts and input data for deep learning-based weather forecasting models,tho-roughly explains representative models applied to various weather forecasting tasks,and provides a detailed comparison of the technical architectures and performance metrics of different models.Additionally,it analyzes and discusses the existing challenges and the future directions in this field.The ultimate purpose of this survey is to provide reference information for related research.

Key words: Weather forecasting, Deep learning, Large model, Artificial intelligence for science

中图分类号: 

  • TP391
[1]MURPHY A H.What is a good forecast? an essay on the nature of goodness in weather forecasting[J].Weather and Forecasting,1993,8(2):281-293.
[2]ZHANG Q J,HONG G,WU D L,et al.Development Status,Problems and Prospects of Agrometeorological Observation Operation in China[J].Chinese Journal of Agrometeorology,2023,44(8):735-749.
[3]XIANG Y,CONG D M,ZHANG Y,et al.Two-Stream Neural Network Fusion Model for Highway Fog Detection[J].Journal of Southwest Jiaotong University,2019,54(1):173-179.
[4]QIU S M,WANG X K,DU X L.Evaluation of Military Communication Effectiveness in Meteorological Environment Based on Optimized BP Neural Network[J].Fire Control & Command Control,2022(3):89-96.
[5]KENDON E J,ROBERTS N M,FOWLER H J,et al.Heavier summer downpours with climate change revealed by weather forecast resolution model[J].Nature Climate Change,2014,4(7):570-576.
[6]LORENC A C.Analysis methods for numerical weather prediction[J].Quarterly Journal of the Royal Meteorological Society,1986,112(474):1177-1194.
[7]TAO S Y,ZHAO S X,ZHOU X P,et al.2003:The Research Progress of the Synoptic Meteorology and Synoptic Forecast[J].Chinese Journal of Atmospheric Sciences,2003,27(4):451-467.
[8]SHEN X S,SU Y,HU J L,et al.Development and operationtransformation of GRAPES global middle-range forecast system[J].Appl Meteor Sci,2017,28(1):1-10.
[9]BAUER P,THORPE A,BRUNET G.The quiet revolution ofnumerical weather prediction[J].Nature,2015,525(7567):47-55.
[10]RICHARDSON L F.Weather prediction by numerical process[M].Combridge:Combridge University Press,1922.
[11]MU M,CHEN B Y,ZHOU F F,et al.Methods and Uncertainties of Meteorological Forecast[J].Meteor Mon,2011,37(1):1-13.
[12]SHEN X S,WANG J J,LI Z C,et al.China’s independent and innovative development of numerical weather prediction[J].Acta Meteorologica Sinica,2020,78(3):451-476.
[13]BUIZZA R,MILLEER M,PALMER T N.Stochastic representation of model uncertainties in the ecmwf ensemble prediction system[J].Quarterly Journal of the Royal Meteorological Society,1999,125(560):2887-2908.
[14]GNEITING T,RAFTERY A E.Weather forecasting with en-semble methods[J].Science,2005,310(5746):248-249.
[15]SCHER S,MESSORI G.Predicting weather forecast uncertaintywith machine learning[J].Quarterly Journal of the Royal Me-teorological Society,2018,144(717):2830-2841.
[16]SONG J Q,WU X J,ZHANG L L,et al.A Study of the Two Helmholtz Solvers in the GRAPES Model Using GCR and GMRES[J].Computer Engineering & Science,2011,33(11):65-70.
[17]RÉMY S,KIPLING Z,FLEMMING J,et al.Description andevaluation of the tropospheric aerosol scheme in the european centre for medium-range weather forecasts(ecmwf) integrated forecasting system(ifs-aer,cycle 45r1)[J].Geoscientific Model Development,2019,12(11):4627-4659.
[18]YANG F,PAN H L,KRUEGER S K,et al.Evaluation of the ncep global forecast system at the arm sgp site[J].Monthly Weather Review,2006,134(12):3668-3690.
[19]SMITH D M,CUSACK S,COLMAN A W,et al.Improved surface temperature prediction for the coming decade from a global climate model[J].Science,2007,317(5839):796-799.
[20]GROVER A,KAPOOR A,HORVITZ E.A deep hybrid model for weather forecasting[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2015:379-386.
[21]LYNCH P.The origins of computer weather prediction and climate modeling[J].Journal of Computational Physics,2008,227(7):3431-3444.
[22]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444.
[23]BERDEJO-ESPINOLA V,AMANO T.AI tools can improveequity inscience[J].Science,2023,379(6636):991-991.
[24]HUANG X M,LIN Y L,XIONG W,et al.Research on international developments of AI large meteorological models in nume-rical forecasting[J].Trans Atmos Sci,2024,47(1):46-54.
[25]ALABDULMOHSIN I M,NEYSHABUR B,ZHAI X.Revisiting neural scaling laws in language and vision[J].Advances in Neural Information Processing Systems,2022,35:22300-22312.
[26]SHI X,CHEN Z,WANG H,et al.Convolutional lstm network:A machine learning approach for precipitation nowcasting[C]//Proceedings of the 28th International Conference on NEURAL Information Processing Systems.2015:802-810.
[27]MARKOVICS D,MAYER M J.Comparison of machine learningmethods for photovoltaic power forecasting based on numerical weather prediction[J].Renewable and Sustainable Energy Reviews,2022,161:112364.
[28]REN X,LI X,REN K,et al.Deep learning-based weather prediction:A survey[J].Big Data Research,2021,23:100178.
[29]JASEENA K U,KOVOOR B C.Deterministic weather forecasting models based on intelligent predictors:A survey[J].Journal of King Saud University-computer and Information Sciences,2022,34(6):3393-3412.
[30]ABDALLA A M,GHAITH I H,TAMIMI A A.Deep learning weather forecasting techniques:literature survey[C]//2021 International Conference on Information Technology(ICIT).IEEE,2021:622-626.
[31]YANG X,DAI K,ZHU Y J.Progress and challenges of deeplearning techniques in intelligent grid weather forecasting[J].Acta Meteorologica Sinica,2022,80(5):649-667.
[32]LAZO J K,MORSS R E,DEMUTH J L.300 billion served:Sources,perceptions,uses,and values of weather forecasts[J].Bulletin of the American Meteorological Society,2009,90(6):785-798.
[33]SUN J,CAO Z,LI H,et al.Application of artificial intelligence technology to numerical weather prediction[J].Journal of Application Meteorological Science,2021,32(1):1-11.
[34]ESTÉVEZ J,GAVILÁN P,GIRÁLDEZ J V.Guidelines on validation procedures for meteorological data from automatic wea-ther stations[J].Journal of Hydrology,2011,402(1/2):144-154.
[35]CARRASSI A,BOCQUET M,BERTINO L,et al.Data assimilation in the geosciences:An overview of methods,issues,and perspectives[J].Wiley Interdisciplinary Reviews:Climate Change,2018,9(5):e535.
[36]WANG X,MA Y,WANG Y,et al.Traffic flow prediction via spatial temporal graph neural network[C]//Proceedings of the Web Conference 2020.2020:1082-1092.
[37]GAO F,YANG Y,WANG J,et al.A deep convolutional generative adversarial networks(DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar(SAR) images[J].Remote Sensing,2018,10(6):846.
[38]WANG B,ZOU X,ZHU J.Data assimilation and its applications[J].Proceedings of the National Academy of Sciences,2000,97(21):11143-11144.
[39]KHODARAHMI M,MAIHAMI V.A review on kalman filter models[J].Archives of Computational Methods in Engineering,2023,30(1):727-747.
[40]COURTIER P,ANDERSSON E,HECKLEY W,et al.The ECMWF implementation of three-dimensional variational assimilation(3D-var).I:Formulation[J].Quarterly Journal of the Royal Meteorological Society,1998,124(550):1783-1807.
[41]AMARI S I.Backpropagation and stochastic gradient descentmethod[J].Neurocomputing,1993,5(4/5):185-196.
[42]FLETCHER R,REEVES C M.Function minimization by conju-gate gradients[J].The Computer Journal,1964,7(2):149-154.
[43]HERSBACH H,BELL B,BERRISFORD P,et al.The era5global reanalysis[J].Quarterly Journal of the Royal Meteorological Society,2020,146(730):1999-2049.
[44]RASP S,DUEBEN P D,SCHER S,et al.Weatherbench:abenchmark data set for data-driven weather forecasting[J].Journal of Advances in Modeling Earth Systems,2020,12(11):e2020MS002203.
[45]RASP S,HOYER S,MEROSE A,et al.Weatherbench 2:Abenchmark for the next generation of data-driven global wea-ther models[J].Journal of Advances in Modeling Earth Systems,2024,16(6):e2023MS004019.
[46]JUMPER J,EVANS R,PRITZEL A,et al.Highly accurate protein structure prediction with alphafold[J].Nature,2021,596(7873):583-589.
[47]SINGHAL K,AZIZI S,TU T,et al.Large language models encode clinical knowledge[J].Nature,2023,620(7972):172-180.
[48]ABRAMSON J,ADLER J,DUNGER J,et al.Accurate struc-ture prediction of biomolecular interactions with alphafold 3[J].Nature,2024,630:493-500.
[49]MERCHANT A,BATZNER S,SCHOENHOLZ S S,et al.Sca-ling deep learning for materials discovery[J].Nature,2023,624(7990):80-85.
[50]KHARE D,CHEN Z,GULOTTY R,et al.Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process[J].NPJ Computational Materials,2024,10(1):120.
[51]REICHSTEIN M,CAMPS-VALLS G,STEVENS B,et al.Deep learning and process understanding for data-driven earth system science[J].Nature,2019,566(7743):195-204.
[52]BENGIO Y.Learning deep architectures for AI[J].Foundations and Trends in Machine Learning,2009,2(1):1-127.
[53]LI M,ZHANG T,CHEN Y,et al.Efficient mini-batch training for stochastic optimization[C]//Proceedings of the 20th ACM SIGKDD International Conference on KNOWLEDGE Discovery and Data Mining.2014:661-670.
[54]LI Z,LIU F,YANG W,et al.A survey of convolutional neural networks:analysis,applications,and prospects[J].IEEE Transactions on Neural Networks and Learning Systems,2021,33(12):6999-7019.
[55]SCHUSTER M,PALIWAL K K.Bidirectional recurrent neural networks[J].IEEE Transactions on Signal Processing,1997,45(11):2673-2681.
[56]WU Z,PAN S,CHEN F,et al.A comprehensive survey ongraph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(1):4-24.
[57]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].Advances in Neural Information Processing Systems,2017,11(4):6000-6010.
[58]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[J].Communications of the ACM,2017,60(6):84-90.
[59]HAN K,WANG Y,CHEN H,et al.A survey on vision transformer[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2022,45(1):87-110.
[60]ZHU Y,TOTH Z,WOBUS R,et al.The economic value of ensemble-based weather forecasts[J].Bulletin of the American Me-teorological Society,2002,83(1):73-84.
[61]GLAHN H R,RUTH D P.The new digital forecast database of the national weather service[J].Bulletin of the American Me-teorological Society,2003,84(2):195-202.
[62]RODRIGUES E R,OLIVEIRA I,CUNHA R,et al.Deepdownscale:A deep learning strategy for high-resolution weather forecast[J].arXiv:1808.05264,2018.
[63]BAI C,SUN F,ZHANG J,et al.Rainformer:Features extraction balanced network for radar-based precipitation nowcasting[J].IEEE Geoscience and Remote Sensing Letters,2022,19:1-5.
[64]RAVURI S,LENC K,WILLSON M,et al.Skilful precipitation nowcasting using deep generative models of radar[J].Nature,2021,597(7878):672-677.
[65]ANDRYCHOWICZ M,ESPEHOLT L,LI D,et al.Deep lear-ning for day forecasts from sparse observations[J].arXiv:2306.06079,2023.
[66]GAO Z,SHI X,WANG H,et al.Earthformer:Exploring space-time transformers for earth system forecasting[J].Advances in Neural Information Processing Systems,2022,35:25390-25403.
[67]MOON S H,KIM Y H,LEE Y H,et al.Application of machine learning to an early warning system for very short-term heavy rainfall[J].Journal of Hydrology,2019,568:1042-1054.
[68]LEBEDEV V,IVASHKIN V,RUDENKO I,et al.Precipitation nowcasting with satellite imagery[C]//Proceedings of the 25th ACM SIGKDD International Conference on KnowledgeDisco-very & Data Mining.2019:2680-2688.
[69]RONNEBERGER O,FISCHER P,BROX T.U-net:Convolu-tional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-assisted Intervention-MICCAI 2015:18th International Conference,Munich,Germany,October 5-9,2015,Proceedings,Part III 18.Springer,2015:234-241.
[70]SUN D,ROTH S,BLACK M J.A quantitative analysis of current practices in optical flow estimation and the principles behind them[J].International Journal of Computer Vision,2014,106:115-137.
[71]AGRAWAL S,BARRINGTON L,BROMBERG C,et al.Ma-chine learning for precipitation nowcasting from radar images[J].arXiv:1912.12132,2019.
[72]ZHANG J,HOWARD K,LANGSTON C,et al.Multi-radarmulti-sensor(mrms) quantitative precipitation estimation:Initial operating capabilities[J].Bulletin of the American Meteorological Society,2016,97(4):621-638.
[73]WU H,YAO Z,WANG J,et al.Motionrnn:A flexible model for video prediction with spacetime-varying motions[C]//Procee-dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:5435-15444.
[74]ZHOU C,SUN C,LIU Z,et al.A c-lstm neural network for text classification[J].arXiv:1511.08630,2015.
[75]WANG Y,WU H,ZHANG J,et al.Predrnn:A recurrent neural network for spatiotemporal predictive learning[J].IEEE Tran-sactions on Pattern Analysis and Machine Intelligence,2022,45(2):2208-2225.
[76]WU X H,HUA Y J,GUAN Y H,et al.Application of CNN-At-tention-BP to precipitation forecast[J].Journal of Nanjing University of Information Science & Technology,2022,14(2):148-155.
[77]PAN B,HSU K,AGHAKOUCHAK A,et al.Improving preci-pitation estimation using convolutional neural network[J].Water Resources Research,2019,55(3):2301-2321.
[78]WERBOS P J.Backpropagation through time:what it does and how to do it[J].Proceedings of the IEEE,1990,78(10):1550-1560.
[79]SHIN S I,NEWMAN M.Seasonal predictability of global and north american coastal sea surface temperature and height anomalies[J].Geophysical Research Letters,2021,48(10):e2020GL091886.
[80]SANDERY P A,SAKOV P.Ocean forecasting of mesoscale features can deteriorate by increasing model resolution towards the submesoscale[J].Nature Communications,2017,8(1):1566.
[81]ZHANG Q,WANG H,DONG J,et al.Prediction of sea surface temperature using long short-term memory[J].IEEE Geoscience and Remote Sensing Letters,2017,14(10):1745-1749.
[82]YANG Y,DONG J,SUN X,et al.A cfcc-lstm model for sea surface temperature prediction[J].IEEE Geoscience and Remote Sensing Letters,2017,15(2):207-211.
[83]LIN X G,WANG Z Y,LI J S,et al.Sea temperature forecasting based on LSTM neural network along the coast of eastern Guangdong[J].Marine Forecasts,2022,39(5):27-36.
[84]WENG S J,CAI J H,PANG Y X,et al.Application of convolutional neural network to sea surface temperature prediction in the coastal waters[J].Journal of Tropical Oceanography,2024,43(1):40-47.
[85]CHEN W B,CHEN H,HSIAO S C,et al.Wind forcing effect on hind-casting of typhoon-driven extreme waves[J].Ocean Engineering,2019,188:106260.
[86]GONG Y,DONG S,WANG Z.Forecasting of typhoon wavebased on hybrid machine learning models[J].Ocean Enginee-ring,2022,266:112934.
[87]CHEN S T.Probabilistic forecasting of coastal wave height du-ring typhoon warning period using machine learning methods[J].Journal of Hydroinformatics,2019,21(2):343-358.
[88]ZHOU S H,HONG X,LIANG C X,et al.A method of tropical cyclone wave height calculation based on artificial meural network[J].Journal of Tropical Oceanography,2020,39(4):25-33.
[89]SHANG F C,LI C Q,ZHAN K,et al.Application of Improved LSTM Neural Network in Time-Series Prediction of Extreme Short-Term Wave[J].Journal of Shanghai Jiao Tong University,2023,57(6):659-665.
[90]REICHENBACH P,ROSSI M,MALAMUD B D,et al.A review of statistically-based landslide susceptibility models[J].Earth-science Reviews,2018,180:60-91.
[91]TIAN N M,LAN H X,WU Y M,et al.Performance Comparison of BP Artificial Neural Network and CART Decision Tree Model in Landslide Susceptibility Prediction[J].Journal of Geo-information Science,2020,22(12):2304-2316.
[92]YANG J K,DANG J W,YANG J Y,et al.Displacement prediction of step-like landslide based on temporal analysis and CNN-BiLSTM-AM[J].Foreign Electronic Measurement Technology,2024,43(1):126-134.
[93]HAO Z,HAO F,SINGH V P.A general framework for multivariate multi-index drought prediction based on multivariate ensemble stream-flow prediction(mesp)[J].Journal of Hydrology,2016,539:1-10.
[94]HAO Z,AGHAKOUCHAK A.A nonparametric multivariatemulti-index drought monitoring framework[J].Journal of Hydrometeorology,2014,15(1):89-101.
[95]NGUYEN T,BRANDSTETTER J,KAPOOR A,et al.Climax:A foundation model for weather and climate[J].arXiv:2301.10343,2023.
[96]SCHULTZ M G,BETANCOURT C,GONG B,et al.Can deep learning beat numerical weather prediction?[J].Philosophical Transactions of the Royal Society A,2021,379(2194):20200097.
[97]KURTH T,SUBRAMANIAN S,HARRINGTON P,et al.Fourcastnet:Accelerating global high-resolution weather forecasting using adaptive fourier neural operators[C]//Procee-dings of the Platform for Advanced Scientific Computing Confe-rence.2023:1-11.
[98]GUIBAS J,MARDANI M,LI Z,et al.Adaptive fourier neural operators:Efficient token mixers for transformers[J].arXiv:2111.13587,2021.
[99]BI K,XIE L,ZHANG H,et al.Pangu-weather:A 3D high-resolution model for fast and accurate global weather forecast[J].arXiv:2211.02556,2022.
[100]BI K,XIE L,ZHANG H,et al.Accurate medium-range global weather forecasting with 3D neural networks[J].Nature,2023,619(7970):533-538.
[101]LIU Z,LIN Y,CAO Y,et al.Swin transformer:Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:10012-10022.
[102]LAM R,SANCHEZ-GONZALEZ A,WILLSON M,et al.Graphcast:Learning skillful medium-range global weather forecasting[J].arXiv:2212.12794,2022.
[103]LAM R,SANCHEZ-GONZALEZ A,WILLSON M,et al.Learning skillful medium-range global weather forecasting[J].Science,2023,382(6677):1416-1421.
[104]CHEN K,HAN T,GONG J,et al.Fengwu:Pushing the skillful global medium-range weather forecast beyond 10 days lead[J].arXiv:2304.02948,2023.
[105]RADFORD A,KIM J W,HALLACY C,et al.Learning transferable visual models from natural language supervision[C]//International Conference on Machine Learning.PMLR,2021:8748-8763.
[106]CHERTI M,BEAUMONT R,WIGHTMAN R,et al.Reprodu-cible scaling laws for contrastive language-image learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2023:2818-2829.
[107]CHEN L,ZHONG X,ZHANG F,et al.Fuxi:A cascade machine learning forecasting system for 15-day global weather forecast[J].Npj Climate and Atmospheric Science,2023,6(1):190.
[108]LIU Z,HU H,LIN Y,et al.Swin transformer v2:Scaling up capacity and resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:12009-12019.
[109]MAGNUSSON L,BIDLOT J R,BONAVITA M,et al.Ecmwf activities for improved hurricane forecasts[J].Bulletin of the American Meteorological Society,2019,100(3):445-458.
[110]CHEN K,BAI L,LING F,et al.Towards an end-to-end artificial intelligence driven global weather forecasting system[J].arXiv:2312.12462,2023.
[111]YU J,LIN Z,YANG J,et al.Free-form image inpainting with gated convolution[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:4471-4480.
[112]XIAO Y,BAI L,XUE W,et al.Fengwu-4Dvar:Coupling the data-driven weather forecasting model with 4D variational assimilation[J].arXiv:2312.12455,2023.
[113]FABLET R,CHAPRON B,DRUMETZ L,et al.Learning variational data assimilation models and solvers[J].Journal of Advances in Modeling Earth Systems,2021,13(10):e2021MS002572.
[114]XU X,SUN X,HAN W,et al.Fuxi-da:A generalized deeplearning data assimilation framework for assimilating satellite observations[J].arXiv:2404.08522,2024.
[115]KASHINATH K,MUSTAFA M,ALBERT A,et al.Physics-informed machinelearning:case studies for weather and climate modelling[J].Philosophical Transactions of the Royal Society A,2021,379(2194):20200093.
[116]ARCOMANO T,SZUNYOGH I,WIKNER A,et al.A hybrid approach to atmospheric modeling that combines machine lear-ning with a physics－based numerical model[J].Journal of Advances in Modeling Earth Systems,2022,14(3):e2021MS002712.
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