183 citations as recorded by crossref.

1. Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network A. Chattopadhyay et al. 10.5194/npg-27-373-2020
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4. Temperature forecasts for the continental United States: a deep learning approach using multidimensional features J. Ali & L. Cheng 10.3389/fclim.2024.1289332
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6. Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al. 10.1175/AIES-D-21-0011.1
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10. Advances and prospects of deep learning for medium-range extreme weather forecasting L. Olivetti & G. Messori 10.5194/gmd-17-2347-2024
11. Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning S. Scher 10.1029/2018GL080704
12. Application of Recurrent Neural Networks to Model Bias Correction: Idealized Experiments With the Lorenz‐96 Model A. Amemiya et al. 10.1029/2022MS003164
13. Domain-driven models yield better predictions at lower cost than reservoir computers in Lorenz systems R. Pyle et al. 10.1098/rsta.2020.0246
14. Challenges and opportunities for a hybrid modelling approach to earth system science S. See & J. Adie 10.1007/s42514-021-00071-y
15. Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
16. Prediction of heavy rainfall days over a peninsular Indian station using the machine learning algorithms K. Subrahmanyam et al. 10.1007/s12040-021-01725-9
17. Piecewise integrable neural network: An interpretable chaos identification framework N. Novelli et al. 10.1063/5.0134984
18. Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks A. Bouteska et al. 10.1007/s10479-023-05722-7
19. RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting G. Ayzel et al. 10.5194/gmd-13-2631-2020
20. Resampling with neural networks for stochastic parameterization in multiscale systems D. Crommelin & W. Edeling 10.1016/j.physd.2021.132894
21. Evaluation of five global AI models for predicting weather in Eastern Asia and Western Pacific C. Liu et al. 10.1038/s41612-024-00769-0
22. Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning A. Chattopadhyay et al. 10.1029/2020MS002084
23. One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes P. Villanueva et al. 10.1021/acs.est.3c07764
24. Teoria estatística da turbulência aplicada ao modelo global BAM-INPE E. Eras et al. 10.5902/2179460X78815
25. Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss S. Scher & J. Molinder 10.1109/ACCESS.2019.2939657
26. A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network J. Frnda et al. 10.3390/s19235144
27. No right to an explanation B. Karlan & H. Kugelberg 10.1111/phpr.70008
28. Performing Experimentation with Physics Model to Predict Statistical Weather Condition A. Jayanthiladevi et al. 10.1088/1742-6596/1712/1/012026
29. Open weather and climate science in the digital era M. de Vos et al. 10.5194/gc-3-191-2020
30. Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning K. Zhou et al. 10.1007/s00376-021-1207-7
31. A comprehensive review of Gaussian atmospheric dispersion models: current usage and future perspectives H. Snoun et al. 10.1007/s41207-023-00354-6
32. A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models N. Agarwal et al. 10.1029/2021MS002537
33. Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. 10.1088/1748-9326/ac0eb0
34. Using machine learning to correct model error in data assimilation and forecast applications A. Farchi et al. 10.1002/qj.4116
35. Review of machine learning and WEAP models for water allocation under climate change D. Hirko et al. 10.1007/s12145-025-01820-1
36. A Reduced Order Deep Data Assimilation model C. Casas et al. 10.1016/j.physd.2020.132615
37. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
38. Temperature forecasting by deep learning methods B. Gong et al. 10.5194/gmd-15-8931-2022
39. FuXi: a cascade machine learning forecasting system for 15-day global weather forecast L. Chen et al. 10.1038/s41612-023-00512-1
40. Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science S. Kawamleh 10.1086/714877
41. The Application of Machine Learning Techniques to Improve El Niño Prediction Skill H. Dijkstra et al. 10.3389/fphy.2019.00153
42. An innovative approach to predict atmospheric rivers: Exploring convolutional autoencoder S. Singh & M. Goyal 10.1016/j.atmosres.2023.106754
43. Using physics to extend the range of machine learning models for an aerodynamic, hydraulic and combusting system: The toy model concept I. Brahma et al. 10.1016/j.egyai.2021.100113
44. Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning A. Chattopadhyay et al. 10.1029/2019MS001958
45. Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks R. Brecht & A. Bihlo 10.1029/2022GL101452
46. Evaluation of statistical climate reconstruction methods based on pseudoproxy experiments using linear and machine-learning methods Z. Zhang et al. 10.5194/cp-18-2643-2022
47. 地球表层特征参量反演与模拟的机理<bold>-</bold>学习耦合范式 焕. 沈 & 良. 张 10.1360/SSTe-2022-0089
48. Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information J. Hu et al. 10.1007/s00376-024-3336-2
49. Reconstruction of all-weather land surface temperature based on a combined physical and data-driven model X. Zhang et al. 10.1007/s11356-023-27986-z
50. Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems J. Wu et al. 10.1016/j.jcp.2019.109209
51. MeteoMex: open infrastructure for networked environmental monitoring and agriculture 4.0 R. Winkler 10.7717/peerj-cs.343
52. Non‐Gaussian Detection Using Machine Learning With Data Assimilation Applications M. Goodliff et al. 10.1029/2021EA001908
53. A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. 10.3389/feart.2023.1149566
54. Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources S. Salcedo-Sanz et al. 10.1016/j.inffus.2020.07.004
55. Forecast combinations: An over 50-year review X. Wang et al. 10.1016/j.ijforecast.2022.11.005
56. Developing intelligent Earth System Models: An AI framework for replacing sub-modules based on incremental learning and its application B. Mu et al. 10.1016/j.atmosres.2024.107306
57. Urban ozone variability using automated machine learning: inference from different feature importance schemes S. Nath et al. 10.1007/s10661-024-12549-7
58. Site-specific adjustment of a NWP-based photovoltaic production forecast H. Böök & A. Lindfors 10.1016/j.solener.2020.10.024
59. Enhancing data-driven soil moisture modeling with physically-guided LSTM networks Q. Geng et al. 10.3389/ffgc.2024.1353011
60. Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images D. Bari et al. 10.3390/atmos14060953
61. What if? Numerical weather prediction at the crossroads P. Bauer 10.1016/j.jemets.2024.100002
62. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
63. Key drivers of large scale changes in North Atlantic atmospheric and oceanic circulations and their predictability B. Dong et al. 10.1007/s00382-025-07591-1
64. Ensemble Methods for Neural Network‐Based Weather Forecasts S. Scher & G. Messori 10.1029/2020MS002331
65. Neural Network-Based Surrogate Modeling for Buckling Performance Optimization of Lightweight-Composite Collapsible Tubular Masts F. Palmeri & S. Laurenzi 10.3390/biomimetics9080494
66. A long-term regional variability analysis of wintertime temperature and its deep learning aspects S. Singh et al. 10.1007/s12145-023-01106-4
67. Systematic Bias Correction in Ocean Mesoscale Forecasting Using Machine Learning G. Liu et al. 10.1029/2022MS003426
68. Deep Data Assimilation: Integrating Deep Learning with Data Assimilation R. Arcucci et al. 10.3390/app11031114
69. Deeplearning-based approach to improving numerical weather forecasts А. Doroshenko et al. 10.15407/pp2023.03.091
70. Skillful bias correction of offshore near-surface wind field forecasting based on a multi-task machine learning model Q. Liu et al. 10.1016/j.aosl.2025.100590
71. On the modern deep learning approaches for precipitation downscaling B. Kumar et al. 10.1007/s12145-023-00970-4
72. Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI M. Chantry et al. 10.1098/rsta.2020.0083
73. Analysis and prediction of atmospheric ozone concentrations using machine learning S. Räss & M. Leuenberger 10.3389/fdata.2024.1469809
74. Using machine learning to produce a very high resolution land-cover map for Ireland E. Walsh et al. 10.5194/asr-18-65-2021
75. Data Learning: Integrating Data Assimilation and Machine Learning C. Buizza et al. 10.1016/j.jocs.2021.101525
76. Polar Vortex Multi-Day Intensity Prediction Relying on New Deep Learning Model: A Combined Convolution Neural Network with Long Short-Term Memory Based on Gaussian Smoothing Method K. Peng et al. 10.3390/e23101314
77. Recurrent neural networks for partially observed dynamical systems U. Bhat & S. Munch 10.1103/PhysRevE.105.044205
78. Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development Y. Wu & W. Xue 10.3390/atmos15060689
79. Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network H. Jeon et al. 10.3390/s22197179
80. Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence T. Smith et al. 10.1029/2023MS003792
81. Deep learning for post-processing ensemble weather forecasts P. Grönquist et al. 10.1098/rsta.2020.0092
82. Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation M. Bocquet 10.3389/fams.2023.1133226
83. 基于人工智能大模型改进全球天气和海浪预报 丰. 零 et al. 10.1360/N072024-0186
84. Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China S. Xu et al. 10.3390/rs17020191
85. Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models J. Weyn et al. 10.1029/2021MS002502
86. Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models F. Tomizawa & Y. Sawada 10.5194/gmd-14-5623-2021
87. Advancing Research for Seamless Earth System Prediction P. Ruti et al. 10.1175/BAMS-D-17-0302.1
88. Ten-Meter Wind Speed Forecast Correction in Southwest China Based on U-Net Neural Network T. Xiang et al. 10.3390/atmos14091355
89. Surface drifter trajectory prediction in the Gulf of Mexico using neural networks M. Grossi et al. 10.1016/j.ocemod.2025.102543
90. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
91. Learning earth system models from observations: machine learning or data assimilation? A. Geer 10.1098/rsta.2020.0089
92. Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data J. Weyn et al. 10.1029/2019MS001705
93. Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
94. Short-term prediction for chaotic time series based on photonic reservoir computing using VCSEL with a feedback loop X. Guo et al. 10.1364/PRJ.517275
95. Reconstructing coupled time series in climate systems using three kinds of machine-learning methods Y. Huang et al. 10.5194/esd-11-835-2020
96. Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework A. Farchi et al. 10.1029/2022MS003474
97. Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations G. Gottwald & S. Reich 10.1063/5.0066080
98. Forecast calibrations of surface air temperature over Xinjiang based on U-net neural network Y. Zhu et al. 10.3389/fenvs.2022.1011321
99. A Toy Model to Investigate Stability of AI‐Based Dynamical Systems B. Balogh et al. 10.1029/2020GL092133
100. On the Optimization of Machine Learning Techniques for Chaotic Time Series Prediction A. González-Zapata et al. 10.3390/electronics11213612
101. A hybrid deep learning and data assimilation method for model error estimation Z. Peng et al. 10.1007/s11430-024-1395-7
102. Deep empirical neural network for optical phase retrieval over a scattering medium H. Tu et al. 10.1038/s41467-025-56522-5
103. Sensitivity of dryland vegetation patterns to storm characteristics O. Crompton & S. Thompson 10.1002/eco.2269
104. Using neural networks to improve simulations in the gray zone R. Kriegmair et al. 10.5194/npg-29-171-2022
105. Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea G. Bonino et al. 10.5194/os-20-417-2024
106. Exploring the typhoon intensity forecasting through integrating AI weather forecasting with regional numerical weather model H. Xu et al. 10.1038/s41612-025-00926-z
107. Memory-based parameterization with differentiable solver: Application to Lorenz ’96 M. Bhouri & P. Gentine 10.1063/5.0131929
108. NSGA-2 Optimized Fuzzy Inference System for Crop Plantation Correctness Index Identification R. Priya et al. 10.1109/TSUSC.2021.3064417
109. Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
110. Predicting the spatiotemporal characteristics of atmospheric rivers: A novel data-driven approach S. Meghani et al. 10.1016/j.gloplacha.2023.104295
111. Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data A. Chattopadhyay et al. 10.1038/s41598-020-57897-9
112. Post-processing rainfall in a high-resolution simulation of the 1994 Piedmont flood S. Meech et al. 10.1007/s42865-020-00028-z
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115. Short‐Term Precipitation Prediction for Contiguous United States Using Deep Learning G. Chen & W. Wang 10.1029/2022GL097904
116. Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook P. Dueben et al. 10.1175/AIES-D-21-0002.1
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125. The challenge of land in a neural network ocean model R. Furner et al. 10.1017/eds.2024.49
126. Developing an efficient climate forecasting model for the spatiotemporal climate dynamics estimation and the prediction that fits the variable topography feature of the upper Blue Nile basin, Ethiopia M. Wondie et al. 10.1016/j.heliyon.2023.e22870
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