153 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
2. Optimizing Echo State Networks for Enhancing Large Prediction Horizons of Chaotic Time Series A. González-Zapata et al. 10.3390/math10203886
3. 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
4. Reconstructing coupled time series in climate systems using three kinds of machine-learning methods Y. Huang et al. 10.5194/esd-11-835-2020
5. Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework A. Farchi et al. 10.1029/2022MS003474
6. Temperature forecasts for the continental United States: a deep learning approach using multidimensional features J. Ali & L. Cheng 10.3389/fclim.2024.1289332
7. Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop S. Haupt et al. 10.1098/rsta.2020.0091
8. Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations G. Gottwald & S. Reich 10.1063/5.0066080
9. Application of Deep Learning to Understanding ENSO Dynamics N. Shin et al. 10.1175/AIES-D-21-0011.1
10. Forecast calibrations of surface air temperature over Xinjiang based on U-net neural network Y. Zhu et al. 10.3389/fenvs.2022.1011321
11. Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels B. Hamzi et al. 10.1098/rspa.2021.0326
12. A Toy Model to Investigate Stability of AI‐Based Dynamical Systems B. Balogh et al. 10.1029/2020GL092133
13. On the Optimization of Machine Learning Techniques for Chaotic Time Series Prediction A. González-Zapata et al. 10.3390/electronics11213612
14. Sensitivity of dryland vegetation patterns to storm characteristics O. Crompton & S. Thompson 10.1002/eco.2269
15. Advances and prospects of deep learning for medium-range extreme weather forecasting L. Olivetti & G. Messori 10.5194/gmd-17-2347-2024
16. Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning S. Scher 10.1029/2018GL080704
17. Application of Recurrent Neural Networks to Model Bias Correction: Idealized Experiments With the Lorenz‐96 Model A. Amemiya et al. 10.1029/2022MS003164
18. Domain-driven models yield better predictions at lower cost than reservoir computers in Lorenz systems R. Pyle et al. 10.1098/rsta.2020.0246
19. Using neural networks to improve simulations in the gray zone R. Kriegmair et al. 10.5194/npg-29-171-2022
20. 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
21. Memory-based parameterization with differentiable solver: Application to Lorenz ’96 M. Bhouri & P. Gentine 10.1063/5.0131929
22. NSGA-2 Optimized Fuzzy Inference System for Crop Plantation Correctness Index Identification R. Priya et al. 10.1109/TSUSC.2021.3064417
23. Challenges and opportunities for a hybrid modelling approach to earth system science S. See & J. Adie 10.1007/s42514-021-00071-y
24. Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
25. 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
26. Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation S. Pawar & O. San 10.1029/2022MS003170
27. Piecewise integrable neural network: An interpretable chaos identification framework N. Novelli et al. 10.1063/5.0134984
28. Predicting the spatiotemporal characteristics of atmospheric rivers: A novel data-driven approach S. Meghani et al. 10.1016/j.gloplacha.2023.104295
29. 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
30. Post-processing rainfall in a high-resolution simulation of the 1994 Piedmont flood S. Meech et al. 10.1007/s42865-020-00028-z
31. Data-driven decadal climate forecasting using Wasserstein time-series generative adversarial networks A. Bouteska et al. 10.1007/s10479-023-05722-7
32. RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting G. Ayzel et al. 10.5194/gmd-13-2631-2020
33. Physics-informed and data-driven discovery of governing equations for complex phenomena in heterogeneous media M. Sahimi 10.1103/PhysRevE.109.041001
34. Resampling with neural networks for stochastic parameterization in multiscale systems D. Crommelin & W. Edeling 10.1016/j.physd.2021.132894
35. Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning A. Chattopadhyay et al. 10.1029/2020MS002084
36. Short‐Term Precipitation Prediction for Contiguous United States Using Deep Learning G. Chen & W. Wang 10.1029/2022GL097904
37. One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes P. Villanueva et al. 10.1021/acs.est.3c07764
38. Teoria estatística da turbulência aplicada ao modelo global BAM-INPE E. Eras et al. 10.5902/2179460X78815
39. Machine Learning-Based Prediction of Icing-Related Wind Power Production Loss S. Scher & J. Molinder 10.1109/ACCESS.2019.2939657
40. A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network J. Frnda et al. 10.3390/s19235144
41. 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
42. Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation G. Gottwald & S. Reich 10.1016/j.physd.2021.132911
43. Combining distribution‐based neural networks to predict weather forecast probabilities M. Clare et al. 10.1002/qj.4180
44. Improving Atmospheric River Forecasts With Machine Learning W. Chapman et al. 10.1029/2019GL083662
45. Improvement of disastrous extreme precipitation forecasting in North China by Pangu-weather AI-driven regional WRF model H. Xu et al. 10.1088/1748-9326/ad41f0
46. Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon M. Falls et al. 10.5194/gmd-15-5713-2022
47. Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere J. Weyn et al. 10.1029/2020MS002109
48. Performing Experimentation with Physics Model to Predict Statistical Weather Condition A. Jayanthiladevi et al. 10.1088/1742-6596/1712/1/012026
49. Open weather and climate science in the digital era M. de Vos et al. 10.5194/gc-3-191-2020
50. Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators M. Nonnenmacher & D. Greenberg 10.1029/2021MS002554
51. Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning K. Zhou et al. 10.1007/s00376-021-1207-7
52. Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management J. Shin et al. 10.1016/j.agrformet.2019.107858
53. A comprehensive review of Gaussian atmospheric dispersion models: current usage and future perspectives H. Snoun et al. 10.1007/s41207-023-00354-6
54. A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models N. Agarwal et al. 10.1029/2021MS002537
55. 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
56. Bridging observations, theory and numerical simulation of the ocean using machine learning M. Sonnewald et al. 10.1088/1748-9326/ac0eb0
57. Using machine learning to correct model error in data assimilation and forecast applications A. Farchi et al. 10.1002/qj.4116
58. 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
59. Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts D. Bari & A. Ouagabi 10.1007/s42452-020-2327-x
60. A Reduced Order Deep Data Assimilation model C. Casas et al. 10.1016/j.physd.2020.132615
61. Physics-informed machine learning: case studies for weather and climate modelling K. Kashinath et al. 10.1098/rsta.2020.0093
62. Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks B. Malozyomov et al. 10.3390/math12030480
63. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
64. Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0) S. Rasp 10.5194/gmd-13-2185-2020
65. Temperature forecasting by deep learning methods B. Gong et al. 10.5194/gmd-15-8931-2022
66. FuXi: a cascade machine learning forecasting system for 15-day global weather forecast L. Chen et al. 10.1038/s41612-023-00512-1
67. Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science S. Kawamleh 10.1086/714877
68. The Application of Machine Learning Techniques to Improve El Niño Prediction Skill H. Dijkstra et al. 10.3389/fphy.2019.00153
69. Training a convolutional neural network to conserve mass in data assimilation Y. Ruckstuhl et al. 10.5194/npg-28-111-2021
70. Optimization of Deep Learning Precipitation Models Using Categorical Binary Metrics P. Larraondo et al. 10.1029/2019MS001909
71. Deep Learning-Based Extreme Heatwave Forecast V. Jacques-Dumas et al. 10.3389/fclim.2022.789641
72. An innovative approach to predict atmospheric rivers: Exploring convolutional autoencoder S. Singh & M. Goyal 10.1016/j.atmosres.2023.106754
73. 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
74. Predicting particle trajectories in oceanic flows using artificial neural networks M. Grossi et al. 10.1016/j.ocemod.2020.101707
75. Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
76. Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning A. Chattopadhyay et al. 10.1029/2019MS001958
77. Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks R. Brecht & A. Bihlo 10.1029/2022GL101452
78. 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
79. 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
80. Process‐Guided Deep Learning Predictions of Lake Water Temperature J. Read et al. 10.1029/2019WR024922
81. 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
82. Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems J. Wu et al. 10.1016/j.jcp.2019.109209
83. MeteoMex: open infrastructure for networked environmental monitoring and agriculture 4.0 R. Winkler 10.7717/peerj-cs.343
84. Applying Machine Learning in Numerical Weather and Climate Modeling Systems V. Krasnopolsky 10.3390/cli12060078
85. Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization H. Song & S. Roh 10.1029/2021MS002609
86. Application of random forest algorithm in hail forecasting over Shandong Peninsula H. Yao et al. 10.1016/j.atmosres.2020.105093
87. Non‐Gaussian Detection Using Machine Learning With Data Assimilation Applications M. Goodliff et al. 10.1029/2021EA001908
88. Generalization properties of feed-forward neural networks trained on Lorenz systems S. Scher & G. Messori 10.5194/npg-26-381-2019
89. A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. 10.3389/feart.2023.1149566
90. 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
91. Forecast combinations: An over 50-year review X. Wang et al. 10.1016/j.ijforecast.2022.11.005
92. Confronting Grand Challenges in environmental fluid mechanics T. Dauxois et al. 10.1103/PhysRevFluids.6.020501
93. 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
94. Deep Learning-Based Weather Prediction: A Survey X. Ren et al. 10.1016/j.bdr.2020.100178
95. Urban ozone variability using automated machine learning: inference from different feature importance schemes S. Nath et al. 10.1007/s10661-024-12549-7
96. AtmoDist: Self-supervised representation learning for atmospheric dynamics S. Hoffmann & C. Lessig 10.1017/eds.2023.1
97. Predicting climate change using an autoregressive long short-term memory model S. Chin & V. Lloyd 10.3389/fenvs.2024.1301343
98. Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction S. Dewitte et al. 10.3390/rs13163209
99. Site-specific adjustment of a NWP-based photovoltaic production forecast H. Böök & A. Lindfors 10.1016/j.solener.2020.10.024
100. Enhancing data-driven soil moisture modeling with physically-guided LSTM networks Q. Geng et al. 10.3389/ffgc.2024.1353011
101. Weather and climate forecasting with neural networks: using general circulation models (GCMs) with different complexity as a study ground S. Scher & G. Messori 10.5194/gmd-12-2797-2019
102. Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models M. Bocquet et al. 10.5194/npg-26-143-2019
103. Is the climate getting WARMer? A framework and tool for climate data comparison E. Bhardwaj & P. Khaiter 10.1016/j.envsoft.2023.105879
104. Enhancing geophysical flow machine learning performance via scale separation D. Faranda et al. 10.5194/npg-28-423-2021
105. Machine Learning for Fog-and-Low-Stratus Nowcasting from Meteosat SEVIRI Satellite Images D. Bari et al. 10.3390/atmos14060953
106. Nonlinear Data Assimilation by Deep Learning Embedded in an Ensemble Kalman Filter T. TSUYUKI & R. TAMURA 10.2151/jmsj.2022-027
107. Blockchain Evaluation Approaches: State-of-the-Art and Future Perspective S. Smetanin et al. 10.3390/s20123358
108. Ensemble Methods for Neural Network‐Based Weather Forecasts S. Scher & G. Messori 10.1029/2020MS002331
109. Detection of Non‐Gaussian Behavior Using Machine Learning Techniques: A Case Study on the Lorenz 63 Model M. Goodliff et al. 10.1029/2019JD031551
110. RadNet 1.0: exploring deep learning architectures for longwave radiative transfer Y. Liu et al. 10.5194/gmd-13-4399-2020
111. A long-term regional variability analysis of wintertime temperature and its deep learning aspects S. Singh et al. 10.1007/s12145-023-01106-4
112. Systematic Bias Correction in Ocean Mesoscale Forecasting Using Machine Learning G. Liu et al. 10.1029/2022MS003426
113. Deep Data Assimilation: Integrating Deep Learning with Data Assimilation R. Arcucci et al. 10.3390/app11031114
114. Deeplearning-based approach to improving numerical weather forecasts А. Doroshenko et al. 10.15407/pp2023.03.091
115. A comparison of combined data assimilation and machine learning methods for offline and online model error correction A. Farchi et al. 10.1016/j.jocs.2021.101468
116. On the modern deep learning approaches for precipitation downscaling B. Kumar et al. 10.1007/s12145-023-00970-4
117. 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
118. Application of Neural Network in Meteorology 珂. 张 10.12677/ojns.2024.123073
119. Accuracy of six years of operational statistical seasonal forecasts of rainfall in Western Australia (2013 to 2018) F. Evans et al. 10.1016/j.atmosres.2019.104697
120. Bioclimatic inference based on mammal community using machine learning regression models: perspectives for paleoecological studies P. Linchamps et al. 10.3389/fevo.2023.1178379
121. Using machine learning to produce a very high resolution land-cover map for Ireland E. Walsh et al. 10.5194/asr-18-65-2021
122. Data Learning: Integrating Data Assimilation and Machine Learning C. Buizza et al. 10.1016/j.jocs.2021.101525
123. 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
124. A Machine Learning‐Based Global Atmospheric Forecast Model T. Arcomano et al. 10.1029/2020GL087776
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126. Applying Machine Learning to Improve Simulations of a Chaotic Dynamical System Using Empirical Error Correction P. Watson 10.1029/2018MS001597
127. Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development Y. Wu & W. Xue 10.3390/atmos15060689
128. Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network H. Jeon et al. 10.3390/s22197179
129. Stability analysis of chaotic systems from data G. Margazoglou & L. Magri 10.1007/s11071-023-08285-1
130. Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence T. Smith et al. 10.1029/2023MS003792
131. Deep learning for post-processing ensemble weather forecasts P. Grönquist et al. 10.1098/rsta.2020.0092
132. Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation M. Bocquet 10.3389/fams.2023.1133226
133. Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto–Sivashinsky test case R. Mojgani et al. 10.1063/5.0091282
134. Sub‐Seasonal Forecasting With a Large Ensemble of Deep‐Learning Weather Prediction Models J. Weyn et al. 10.1029/2021MS002502
135. 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
136. Advancing Research for Seamless Earth System Prediction P. Ruti et al. 10.1175/BAMS-D-17-0302.1
137. Multi-Model Ensemble Forecasts of Surface Air Temperatures in Henan Province Based on Machine Learning T. Wang et al. 10.3390/atmos14030520
138. Machines Learn Better with Better Data Ontology: Lessons from Philosophy of Induction and Machine Learning Practice D. Li 10.1007/s11023-023-09639-9
139. Ten-Meter Wind Speed Forecast Correction in Southwest China Based on U-Net Neural Network T. Xiang et al. 10.3390/atmos14091355
140. Data-driven global weather predictions at high resolutions J. Taylor et al. 10.1177/10943420211039818
141. Data-driven Seeing Prediction for Optics Telescope: from Statistical Modeling, Machine Learning to Deep Learning Techniques W. Ni et al. 10.1088/1674-4527/ac977b
142. Machine learning for numerical weather and climate modelling: a review C. de Burgh-Day & T. Leeuwenburg 10.5194/gmd-16-6433-2023
143. Learning earth system models from observations: machine learning or data assimilation? A. Geer 10.1098/rsta.2020.0089
144. Addressing up-scaling methodologies for convection-permitting EPSs using statistical and machine learning tools T. Comito et al. 10.5194/asr-18-145-2021
145. 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
146. Recently emerging trends in big data analytic methods for modeling and combating climate change effects A. Ikegwu et al. 10.1186/s42162-024-00307-5
147. Can deep learning beat numerical weather prediction? M. Schultz et al. 10.1098/rsta.2020.0097
148. A sensitivity analysis of a regression model of ocean temperature R. Furner et al. 10.1017/eds.2022.10
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151. Data‐Driven Medium‐Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench S. Rasp & N. Thuerey 10.1029/2020MS002405
152. Heat Waves: Physical Understanding and Scientific Challenges D. Barriopedro et al. 10.1029/2022RG000780
153. Temperature forecasts for the continental United States: a deep learning approach using multidimensional features J. Ali & L. Cheng 10.3389/fclim.2024.1289332