175 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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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
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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
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26. A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network J. Frnda et al. 10.3390/s19235144
27. Performing Experimentation with Physics Model to Predict Statistical Weather Condition A. Jayanthiladevi et al. 10.1088/1742-6596/1712/1/012026
28. Open weather and climate science in the digital era M. de Vos et al. 10.5194/gc-3-191-2020
29. Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning K. Zhou et al. 10.1007/s00376-021-1207-7
30. A comprehensive review of Gaussian atmospheric dispersion models: current usage and future perspectives H. Snoun et al. 10.1007/s41207-023-00354-6
31. A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models N. Agarwal et al. 10.1029/2021MS002537
32. Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. 10.1088/1748-9326/ac0eb0
33. Using machine learning to correct model error in data assimilation and forecast applications A. Farchi et al. 10.1002/qj.4116
34. A Reduced Order Deep Data Assimilation model C. Casas et al. 10.1016/j.physd.2020.132615
35. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
36. Temperature forecasting by deep learning methods B. Gong et al. 10.5194/gmd-15-8931-2022
37. FuXi: a cascade machine learning forecasting system for 15-day global weather forecast L. Chen et al. 10.1038/s41612-023-00512-1
38. Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science S. Kawamleh 10.1086/714877
39. The Application of Machine Learning Techniques to Improve El Niño Prediction Skill H. Dijkstra et al. 10.3389/fphy.2019.00153
40. An innovative approach to predict atmospheric rivers: Exploring convolutional autoencoder S. Singh & M. Goyal 10.1016/j.atmosres.2023.106754
41. 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
42. Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning A. Chattopadhyay et al. 10.1029/2019MS001958
43. Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks R. Brecht & A. Bihlo 10.1029/2022GL101452
44. 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
45. 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
46. Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information J. Hu et al. 10.1007/s00376-024-3336-2
47. 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
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49. MeteoMex: open infrastructure for networked environmental monitoring and agriculture 4.0 R. Winkler 10.7717/peerj-cs.343
50. Non‐Gaussian Detection Using Machine Learning With Data Assimilation Applications M. Goodliff et al. 10.1029/2021EA001908
51. A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. 10.3389/feart.2023.1149566
52. 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
53. Forecast combinations: An over 50-year review X. Wang et al. 10.1016/j.ijforecast.2022.11.005
54. 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
55. Urban ozone variability using automated machine learning: inference from different feature importance schemes S. Nath et al. 10.1007/s10661-024-12549-7
56. Site-specific adjustment of a NWP-based photovoltaic production forecast H. Böök & A. Lindfors 10.1016/j.solener.2020.10.024
57. Enhancing data-driven soil moisture modeling with physically-guided LSTM networks Q. Geng et al. 10.3389/ffgc.2024.1353011
58. Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images D. Bari et al. 10.3390/atmos14060953
59. What if? Numerical weather prediction at the crossroads P. Bauer 10.1016/j.jemets.2024.100002
60. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
61. 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
62. Ensemble Methods for Neural Network‐Based Weather Forecasts S. Scher & G. Messori 10.1029/2020MS002331
63. Neural Network-Based Surrogate Modeling for Buckling Performance Optimization of Lightweight-Composite Collapsible Tubular Masts F. Palmeri & S. Laurenzi 10.3390/biomimetics9080494
64. A long-term regional variability analysis of wintertime temperature and its deep learning aspects S. Singh et al. 10.1007/s12145-023-01106-4
65. Systematic Bias Correction in Ocean Mesoscale Forecasting Using Machine Learning G. Liu et al. 10.1029/2022MS003426
66. Deep Data Assimilation: Integrating Deep Learning with Data Assimilation R. Arcucci et al. 10.3390/app11031114
67. Deeplearning-based approach to improving numerical weather forecasts А. Doroshenko et al. 10.15407/pp2023.03.091
68. 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
69. On the modern deep learning approaches for precipitation downscaling B. Kumar et al. 10.1007/s12145-023-00970-4
70. 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
71. Analysis and prediction of atmospheric ozone concentrations using machine learning S. Räss & M. Leuenberger 10.3389/fdata.2024.1469809
72. Using machine learning to produce a very high resolution land-cover map for Ireland E. Walsh et al. 10.5194/asr-18-65-2021
73. Data Learning: Integrating Data Assimilation and Machine Learning C. Buizza et al. 10.1016/j.jocs.2021.101525
74. 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
75. Recurrent neural networks for partially observed dynamical systems U. Bhat & S. Munch 10.1103/PhysRevE.105.044205
76. Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development Y. Wu & W. Xue 10.3390/atmos15060689
77. Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network H. Jeon et al. 10.3390/s22197179
78. Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence T. Smith et al. 10.1029/2023MS003792
79. Deep learning for post-processing ensemble weather forecasts P. Grönquist et al. 10.1098/rsta.2020.0092
80. Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation M. Bocquet 10.3389/fams.2023.1133226
81. 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
82. 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
83. Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models J. Weyn et al. 10.1029/2021MS002502
84. 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
85. Advancing Research for Seamless Earth System Prediction P. Ruti et al. 10.1175/BAMS-D-17-0302.1
86. Ten-Meter Wind Speed Forecast Correction in Southwest China Based on U-Net Neural Network T. Xiang et al. 10.3390/atmos14091355
87. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
88. Learning earth system models from observations: machine learning or data assimilation? A. Geer 10.1098/rsta.2020.0089
89. 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
90. Machine learning for the physics of climate A. Bracco et al. 10.1038/s42254-024-00776-3
91. 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
92. Reconstructing coupled time series in climate systems using three kinds of machine-learning methods Y. Huang et al. 10.5194/esd-11-835-2020
93. Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework A. Farchi et al. 10.1029/2022MS003474
94. Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations G. Gottwald & S. Reich 10.1063/5.0066080
95. Forecast calibrations of surface air temperature over Xinjiang based on U-net neural network Y. Zhu et al. 10.3389/fenvs.2022.1011321
96. A Toy Model to Investigate Stability of AI‐Based Dynamical Systems B. Balogh et al. 10.1029/2020GL092133
97. On the Optimization of Machine Learning Techniques for Chaotic Time Series Prediction A. González-Zapata et al. 10.3390/electronics11213612
98. A hybrid deep learning and data assimilation method for model error estimation Z. Peng et al. 10.1007/s11430-024-1395-7
99. Deep empirical neural network for optical phase retrieval over a scattering medium H. Tu et al. 10.1038/s41467-025-56522-5
100. Sensitivity of dryland vegetation patterns to storm characteristics O. Crompton & S. Thompson 10.1002/eco.2269
101. Using neural networks to improve simulations in the gray zone R. Kriegmair et al. 10.5194/npg-29-171-2022
102. 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
103. 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
104. Memory-based parameterization with differentiable solver: Application to Lorenz ’96 M. Bhouri & P. Gentine 10.1063/5.0131929
105. NSGA-2 Optimized Fuzzy Inference System for Crop Plantation Correctness Index Identification R. Priya et al. 10.1109/TSUSC.2021.3064417
106. Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
107. Predicting the spatiotemporal characteristics of atmospheric rivers: A novel data-driven approach S. Meghani et al. 10.1016/j.gloplacha.2023.104295
108. 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
109. 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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111. Short‐Term Precipitation Prediction for Contiguous United States Using Deep Learning G. Chen & W. Wang 10.1029/2022GL097904
112. 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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118. Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere J. Weyn et al. 10.1029/2020MS002109
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