75 citations as recorded by crossref.

1. Optimization of deep learning model for coastal chlorophyll a dynamic forecast D. Wenxiang et al. 10.1016/j.ecolmodel.2022.109913
2. Generalization properties of feed-forward neural networks trained on Lorenz systems S. Scher & G. Messori 10.5194/npg-26-381-2019
3. 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
4. A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction R. Zhu et al. 10.3390/rs16081466
5. Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky 10.3390/cli12060078
6. Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning A. Chattopadhyay et al. 10.1029/2019MS001958
7. Data-driven global weather predictions at high resolutions J. Taylor et al. 10.1177/10943420211039818
8. Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas M. Lalande et al. 10.5194/tc-17-5095-2023
9. Deep-learning-based information mining from ocean remote-sensing imagery X. Li et al. 10.1093/nsr/nwaa047
10. Temperature forecasts for the continental United States: a deep learning approach using multidimensional features J. Ali & L. Cheng 10.3389/fclim.2024.1289332
11. Ensemble numerical weather prediction model to improve the efficiency of Henan parameterization scheme X. Ma et al. 10.2478/amns-2024-2813
12. Non‐Gaussian Detection Using Machine Learning With Data Assimilation Applications M. Goodliff et al. 10.1029/2021EA001908
13. Machine learning for weather and climate are worlds apart D. Watson-Parris 10.1098/rsta.2020.0098
14. Reducing data-driven dynamical subgrid scale models by physical constraints W. Edeling & D. Crommelin 10.1016/j.compfluid.2020.104470
15. Artificial Intelligence and Its Application in Numerical Weather Prediction S. Soldatenko 10.3103/S1068373924040010
16. RF-Based Moisture Content Determination in Rice Using Machine Learning Techniques N. Azmi et al. 10.3390/s21051875
17. Predicting climate change using an autoregressive long short-term memory model S. Chin & V. Lloyd 10.3389/fenvs.2024.1301343
18. Advances and prospects of deep learning for medium-range extreme weather forecasting L. Olivetti & G. Messori 10.5194/gmd-17-2347-2024
19. Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1 A. Mahesh et al. 10.5194/gmd-17-3533-2024
20. 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
21. Selection of CMIP6 GCM with projection of climate over the Amu Darya River Basin O. Salehie et al. 10.1007/s00704-022-04332-w
22. Temperature forecasting by deep learning methods B. Gong et al. 10.5194/gmd-15-8931-2022
23. Statistical Seasonal Prediction of European Summer Mean Temperature Using Observational, Reanalysis, and Satellite Data M. Pyrina et al. 10.1175/WAF-D-20-0235.1
24. Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing A. Wikner et al. 10.1016/j.neunet.2023.10.054
25. Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization H. Song & S. Roh 10.1029/2021MS002609
26. Deep blue AI: A new bridge from data to knowledge for the ocean science G. Chen et al. 10.1016/j.dsr.2022.103886
27. Deep Learning-Based Extreme Heatwave Forecast V. Jacques-Dumas et al. 10.3389/fclim.2022.789641
28. Optimal Input Variables Disposition of Artificial Neural Networks Models for Enhancing Time Series Forecasting Accuracy H. Fogno Fotso et al. 10.1080/08839514.2020.1782003
29. Training a convolutional neural network to conserve mass in data assimilation Y. Ruckstuhl et al. 10.5194/npg-28-111-2021
30. Prior-Guided gated convolutional networks for rainstorm forecasting T. Zhang et al. 10.1016/j.jhydrol.2024.130962
31. A data-driven model based on modal decomposition: application to the turbulent channel flow over an anisotropic porous wall S. Le Clainche et al. 10.1017/jfm.2022.159
32. Machine learning-based integration of large-scale climate drivers can improve the forecast of seasonal rainfall probability in Australia P. Feng et al. 10.1088/1748-9326/ab9e98
33. A Deep Learning-Based Sensor Modeling for Smart Irrigation System M. Sami et al. 10.3390/agronomy12010212
34. A sensitivity analysis of a regression model of ocean temperature R. Furner et al. 10.1017/eds.2022.10
35. Application of LSTM Neural Network Technology Embedded in English Intelligent Translation Y. Yang & K. Sun 10.1155/2022/1085577
36. Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence A. Chattopadhyay et al. 10.1017/eds.2022.30
37. Optimization of Deep Learning Precipitation Models Using Categorical Binary Metrics P. Larraondo et al. 10.1029/2019MS001909
38. 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
39. A generative adversarial network approach to (ensemble) weather prediction A. Bihlo 10.1016/j.neunet.2021.02.003
40. Model-free short-term fluid dynamics estimator with a deep 3D-convolutional neural network M. Lopez-Martin et al. 10.1016/j.eswa.2021.114924
41. Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning A. Chattopadhyay et al. 10.1029/2020MS002084
42. Physics-informed neural networks for the shallow-water equations on the sphere A. Bihlo & R. Popovych 10.1016/j.jcp.2022.111024
43. A Machine Learning‐Based Global Atmospheric Forecast Model T. Arcomano et al. 10.1029/2020GL087776
44. Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks B. François et al. 10.1007/s00382-021-05869-8
45. Systematic Bias Correction in Ocean Mesoscale Forecasting Using Machine Learning G. Liu et al. 10.1029/2022MS003426
46. Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5 A. Chattopadhyay et al. 10.5194/gmd-15-2221-2022
47. Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss S. Scher & J. Molinder 10.1109/ACCESS.2019.2939657
48. Enhancing geophysical flow machine learning performance via scale separation D. Faranda et al. 10.5194/npg-28-423-2021
49. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
50. Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere J. Weyn et al. 10.1029/2020MS002109
51. WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting S. Rasp et al. 10.1029/2020MS002203
52. Reservoir Computing as a Tool for Climate Predictability Studies B. Nadiga 10.1029/2020MS002290
53. Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development Y. Wu & W. Xue 10.3390/atmos15060689
54. Accurate medium-range global weather forecasting with 3D neural networks K. Bi et al. 10.1038/s41586-023-06185-3
55. Scalable Projection-Based Reduced-Order Models for Large Multiscale Fluid Systems C. Wentland et al. 10.2514/1.J062869
56. Short‐Term Precipitation Prediction for Contiguous United States Using Deep Learning G. Chen & W. Wang 10.1029/2022GL097904
57. Purely satellite data–driven deep learning forecast of complicated tropical instability waves G. Zheng et al. 10.1126/sciadv.aba1482
58. Physics-informed machine learning: case studies for weather and climate modelling K. Kashinath et al. 10.1098/rsta.2020.0093
59. Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence T. Smith et al. 10.1029/2023MS003792
60. Configuration and intercomparison of deep learning neural models for statistical downscaling J. Baño-Medina et al. 10.5194/gmd-13-2109-2020
61. Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian 10.1007/s00382-024-07406-9
62. Ensemble Methods for Neural Network‐Based Weather Forecasts S. Scher & G. Messori 10.1029/2020MS002331
63. Defining model complexity: An ecological perspective C. Malmborg et al. 10.1002/met.2202
64. Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution O. San et al. 10.1002/gamm.202100007
65. Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application S. Fritzner et al. 10.1029/2020JC016277
66. Spin-up characteristics with three types of initial fields and the restart effects on forecast accuracy in the GRAPES global forecast system Z. Ma et al. 10.5194/gmd-14-205-2021
67. A dynamical systems characterization of atmospheric jet regimes G. Messori et al. 10.5194/esd-12-233-2021
68. 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
69. Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land L. Beusch et al. 10.5194/esd-11-139-2020
70. Projection of Hot and Cold Extremes in the Amu River Basin of Central Asia using GCMs CMIP6 O. Salehie et al. 10.1007/s00477-022-02201-6
71. Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
72. A modified deep learning weather prediction using cubed sphere for global precipitation M. Singh et al. 10.3389/fclim.2022.1022624
73. Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America D. Faranda et al. 10.1007/s00382-019-05106-3
74. Detection of Non‐Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model M. Goodliff et al. 10.1029/2019JD031551
75. Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks B. Malozyomov et al. 10.3390/math12030480