90 citations as recorded by crossref.

1. Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al. https://doi.org/10.5194/gmd-18-8777-2025
2. Climate-invariant machine learning T. Beucler et al. https://doi.org/10.1126/sciadv.adj7250
3. A novel cluster-based learning scheme to design optimal networks for atmospheric greenhouse gas monitoring (CRO2A version 1.0) D. Matajira-Rueda et al. https://doi.org/10.5194/gmd-19-3757-2026
4. MBNO: Mamba-based neural operators for solving partial differential equations N. Cho et al. https://doi.org/10.1016/j.jcp.2025.114639
5. Integrating Reflective Practice into the Self-Improvement Cycle Module for Renewable Energy Forecasting Accuracy G. Veigners et al. https://doi.org/10.2478/plua-2024-0012
6. Artificial Intelligence and the Question of Theological Relevance Z. Ul Haq https://doi.org/10.1515/opth-2025-0067
7. Wind Shear Prediction at Jeju International Airport Using a Tree-Based Machine Learning Algorithm J. Seok et al. https://doi.org/10.3390/forecast8010004
8. Using feature importance as an exploratory data analysis tool on Earth system models D. Ries et al. https://doi.org/10.5194/gmd-18-1041-2025
9. Machine learning methods for predicting shoreline change from submerged breakwater simulations S. Muroi et al. https://doi.org/10.1016/j.engappai.2025.110726
10. Deep learning-driven reconstruction of PM2.5 vertical profiles: A fusion of lidar and tower data Z. Yi et al. https://doi.org/10.1016/j.jclepro.2025.145397
11. Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b Y. Zhang et al. https://doi.org/10.1038/s41598-024-73741-w
12. Predicting thermal trends in smart buildings using machine learning: a case study C. Mejía Rodriguez et al. https://doi.org/10.69821/REMUVAC.v3i1.299
13. Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling R. O'Loughlin et al. https://doi.org/10.5194/gmd-18-787-2025
14. Reanalysis-driven Prediction of Monthly Temperature Anomalies in West Africa Using Interpretable Machine Learning O. Damilola et al. https://doi.org/10.11648/j.ajai.20250902.30
15. Reconstructing historical climate fields with deep learning N. Bochow et al. https://doi.org/10.1126/sciadv.adp0558
16. Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data C. Kent et al. https://doi.org/10.1038/s41612-025-01198-3
17. Anomalous Behavior in Weather Forecast Uncertainty: Implications for Ship Weather Routing M. Marjanović et al. https://doi.org/10.3390/jmse13061185
18. Dust emission forecasting using time series spatial data and machine learning algorithms M. Sanisales et al. https://doi.org/10.1016/j.aeolia.2025.101011
19. Machine learning advances and data model coevolution in geoscience A. Eltijnai & M. Mohammed https://doi.org/10.1007/s44288-026-00524-3
20. A cloud radiative flux computation using machine learning approach: influence of cloud properties in modifying longwave and shortwave outgoing radiation R. Krishnaveni et al. https://doi.org/10.1007/s00704-026-06319-3
21. The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. https://doi.org/10.1007/s10462-024-10962-5
22. A Hybrid Approach for Seasonal Rainfall Forecasting Across Vietnam Using Convolutional Neural Networks and Dynamical Downscaling T. Ngoc et al. https://doi.org/10.1007/s00024-025-03838-4
23. Digital twin development for ship fuel consumption predictions using multiple model adaptive estimation with Eigensystem Realization Algorithm and Observer/Kalman Filter Identification M. Taghavi & L. Perera https://doi.org/10.1016/j.oceaneng.2025.123743
24. Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability Y. Chu et al. https://doi.org/10.5194/acp-26-1415-2026
25. Premium Climate Services for Mining Company, How Good is It? . Supari et al. https://doi.org/10.1088/1755-1315/1472/1/012001
26. Detecting Signatures of Giant Hail Events With Integrated GNSS Tomography: A Case Study Over Southern Poland A. Cegla et al. https://doi.org/10.1109/TGRS.2026.3659018
27. A novel “WRF+AutoML” framework for enhanced heat estimation in urban environments M. Gao et al. https://doi.org/10.1016/j.scs.2025.106908
28. Spatiotemporal Model Cards Enabling Future-Proof GeoAI Systems A. Graser et al. https://doi.org/10.1145/3809183
29. Faster dieback of rainforests altering tropical carbon sinks under climate change D. Nath et al. https://doi.org/10.1038/s41612-024-00793-0
30. Hydroclimate volatility on a warming Earth D. Swain et al. https://doi.org/10.1038/s43017-024-00624-z
31. Enhancing multivariate weather forecasting via temporal attention and spatiotemporal fusion J. Liu et al. https://doi.org/10.1016/j.engappai.2025.113133
32. Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks S. Soldatenko & Y. Angudovich https://doi.org/10.3103/S1068373925020013
33. A physics-informed machine learning parameterization for cloud microphysics in ICON E. Sarauer et al. https://doi.org/10.1017/eds.2025.10016
34. AI-Driven Prediction of Ecological Footprint Using an Optimized Extreme Learning Machine Framework I. Alrmah et al. https://doi.org/10.3390/su172210319
35. Beyond coastal hazards: A comprehensive methodology for the assessment of climate-related hazards in European coastal cities E. Laino & G. Iglesias https://doi.org/10.1016/j.ocecoaman.2024.107343
36. Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation M. Shams Eddin & J. Gall https://doi.org/10.5194/gmd-17-2987-2024
37. Application of machine learning for coastal flooding A. Babati et al. https://doi.org/10.1007/s44327-025-00125-8
38. Climate Indices as Potential Predictors in Empirical Long-Range Meteorological Forecasting Models S. Soldatenko et al. https://doi.org/10.3390/forecast8010009
39. Protocols for Water and Environmental Modeling Using Machine Learning in California M. He et al. https://doi.org/10.3390/hydrology12030059
40. TYPHOON TRACK PREDICTION BY CNN-LSTM NETWORK BASED ON LARGE ENSEMBLE CLIMATE DATASET d4PDF T. NAMEGAI et al. https://doi.org/10.2208/jscejj.25-17255
41. Multi-physics coupled simulation and hybrid mechanistic data driven modeling for high-accuracy radiation error correction in sounding temperature sensors J. Lu et al. https://doi.org/10.1108/SR-07-2025-0509
42. Spatio-temporal graph neural networks to improve precipitation forecasts from numerical models U. Yousaf et al. https://doi.org/10.1007/s00500-025-10635-7
43. Система автоматизованого прогнозування змін площі лісового покриву методами штучного інтелекту Б. Вишинський & М. Сенів https://doi.org/10.36930/40350607
44. Leveraging automated machine learning (AutoML) for urban climate emulation J. Yu et al. https://doi.org/10.1016/j.bdes.2025.100040
45. Embedding machine-learnt sub-grid variability improves climate model precipitation patterns D. Giles et al. https://doi.org/10.1038/s43247-024-01885-8
46. A Fine‐Tuned Pangu Weather Model and Its Performance Based on an Operational Framework in South China X. Xia et al. https://doi.org/10.1002/met.70114
47. An analytics framework for interpretable subseasonal forecasting under decadal climate variability J. Chen et al. https://doi.org/10.1016/j.dajour.2025.100660
48. Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study M. Zouzoua et al. https://doi.org/10.5194/gmd-18-3211-2025
49. Approximation of Spatial and Temporal Variability of the Urban Heat Island in Moscow Using Machine Learning M. Varentsov et al. https://doi.org/10.3103/S0027134924702254
50. Modeling and prediction of climate change impacts on water resources vulnerability: A multi-model approach T. Mengistu et al. https://doi.org/10.1016/j.jenvman.2025.126025
51. PIP² Net: Physics-informed partition penalty deep operator network H. Mi et al. https://doi.org/10.3934/era.2026090
52. Developing Guidelines for working with Multi-Model Ensembles in CMIP A. Katzenberger et al. https://doi.org/10.5194/esd-17-495-2026
53. Latent Diffusion and Spatiotemporal Transformers Generate Large Ensemble Climate Simulations J. Meuer et al. https://doi.org/10.1109/TAI.2025.3621121
54. Improving renewable energy forecasting with FuXi-Energy X. Zhong et al. https://doi.org/10.1016/j.apenergy.2026.127657
55. Enhancing coastal winds and surface ocean currents with deep learning for short-term wave forecasting M. García-León et al. https://doi.org/10.5194/os-21-3265-2025
56. A shifting climate: New paradigms and challenges for (early career) scientists in extreme weather research M. Kretschmer et al. https://doi.org/10.1002/asl.1268
57. Cross-Dataset Data Augmentation Using UMAP for Deep Learning-Based Wind Speed Prediction E. Leon-Gomez et al. https://doi.org/10.3390/computers14040123
58. Rewiring climate modeling with machine learning emulators P. Van Katwyk et al. https://doi.org/10.1038/s43247-026-03238-z
59. Conditional Denoising Score Matching for Sequential Data Assimilation Z. Shen https://doi.org/10.34133/olar.0103
60. Artificial neural networks in the monthly air temperature estimation for the Central-West Region of Brazil F. Capuchinho et al. https://doi.org/10.1007/s00521-026-12122-y
61. The convergence of machine learning and data assimilation in Earth system science R. Arcucci et al. https://doi.org/10.1038/s44387-026-00107-0
62. Predicting water potability using a machine learning approach E. Rachid et al. https://doi.org/10.1016/j.envc.2025.101131
63. Temperature Prediction at Street Scale During a Heat Wave Using Random Forest P. Gkirmpas et al. https://doi.org/10.3390/atmos16070877
64. Data-driven forecasts of extreme weather in East Asia: feasibility of operational use S. Oh et al. https://doi.org/10.1016/j.wace.2026.100875
65. Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations J. Hammond et al. https://doi.org/10.3389/frwa.2025.1709138
66. A data-to-forecast machine learning system for global weather X. Sun et al. https://doi.org/10.1038/s41467-025-62024-1
67. The ODE (Overview, Data, and Execution) protocol for a standardized use of machine learning in environmental, social and related interdisciplinary sciences S. Seuru et al. https://doi.org/10.1016/j.envsoft.2026.106912
68. An extension of WeatherBench 2 to binary hydroclimatic forecasts T. Zhao et al. https://doi.org/10.5194/gmd-18-5781-2025
69. Predictability of the anomaly pattern of summer extreme high temperature days over northern China J. Li & Y. Long https://doi.org/10.1007/s00382-024-07301-3
70. How the choice of model calibration procedure affects projections of lake surface water temperatures for future climatic conditions J. Napiorkowski et al. https://doi.org/10.1016/j.jhydrol.2025.133236
71. Reconstructing Late Quaternary coastal landscapes by a machine-learning framework G. Mattei et al. https://doi.org/10.1038/s41598-025-31006-0
72. Advances and prospects of deep learning for medium-range extreme weather forecasting L. Olivetti & G. Messori https://doi.org/10.5194/gmd-17-2347-2024
73. A Novel Approach for Reliable Classification of Marine Low Cloud Morphologies with Vision–Language Models E. Erfani & F. Hosseinpour https://doi.org/10.3390/atmos16111252
74. A high-resolution hourly XCO 2 dataset for eastern China (2016–2020) based on WRF-Chem simulations and multi-source validation W. Li & T. Ren https://doi.org/10.1080/20964471.2025.2585587
75. Using Machine Learning for Climate Modelling: Application of Neural Networks to a Slow-Fast Chaotic Dynamical System as a Case Study S. Soldatenko & Y. Angudovich https://doi.org/10.3390/cli12110189
76. Water exchange in the Baltic Sea: a historical view of research approaches from basin scales to submesoscale J. Elken & A. Omstedt https://doi.org/10.3389/feart.2025.1598983
77. Scientometric review on multiple climate-related hazards indices E. Laino et al. https://doi.org/10.1016/j.scitotenv.2024.174004
78. Leveraging machine learning for accurate near-surface air temperature prediction to enhance climate adaptation in Ghana C. Oduro et al. https://doi.org/10.1016/j.jafrearsci.2025.105877
79. The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models H. Lean et al. https://doi.org/10.1002/qj.4858
80. Assessing machine learning and Physics-Informed models for Multi-Lead time sea surface temperature prediction in the Arabian sea A. Sunil et al. https://doi.org/10.1007/s00477-025-03049-2
81. Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast L. Olivetti & G. Messori https://doi.org/10.5194/gmd-17-7915-2024
82. SIGMAformer: a spatiotemporal Gaussian mixture correlation transformer for global weather forecasting D. Kim & H. Suk https://doi.org/10.1038/s41612-026-01385-w
83. Multi-Scale Fourier Temporal Network for Multi-Source Precipitation Nowcasting J. Huang et al. https://doi.org/10.3390/s26082303
84. Identification and correction of snow depth bias in ERA5 datasets over Central Europe using machine learning G. Stachura & Z. Ustrnul https://doi.org/10.5194/tc-20-2295-2026
85. A System of Systems for the Selection of Optimal Climate Change Decisions G. Piccoli et al. https://doi.org/10.1109/ACCESS.2026.3655092
86. LCZ4r package R for local climate zones and urban heat islands M. Anjos et al. https://doi.org/10.1038/s41598-025-92000-0
87. Tropical cyclone track prediction model for multidimensional features and time differences series observation P. Yang & G. Ye https://doi.org/10.1016/j.aej.2024.10.090
88. Evaluation of five global AI models for predicting weather in Eastern Asia and Western Pacific C. Liu et al. https://doi.org/10.1038/s41612-024-00769-0
89. Physics-Informed spatiotemporal deep learning for multivariate atmospheric forecasting H. Yu et al. https://doi.org/10.1016/j.eswa.2026.131721
90. Near-term Climate Prediction of Agricultural Thermal Conditions in East Asia J. Choi et al. https://doi.org/10.1007/s00376-025-4471-0