56 citations as recorded by crossref.

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8. A Place‐Based Approach to Drought Forecasting in South‐Central Oklahoma R. McPherson et al. 10.1029/2022EA002315
9. Winter Surface Air Temperature Prediction over Japan Using Artificial Neural Networks J. Ratnam et al. 10.1175/WAF-D-20-0218.1
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11. The efficacy of tropical and extratropical predictors for long‐lead El Niño‐Southern Oscillation prediction: A study using a machine learning algorithm W. Song et al. 10.1002/joc.8241
12. Reservoir Computing with Delayed Input for Fast and Easy Optimisation L. Jaurigue et al. 10.3390/e23121560
13. A New Hybrid Machine Learning Model for Short-Term Climate Prediction by Performing Classification Prediction and Regression Prediction Simultaneously D. Li et al. 10.1007/s13351-022-1214-3
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17. A Neural Network‐Based Scale‐Adaptive Cloud‐Fraction Scheme for GCMs G. Chen et al. 10.1029/2022MS003415
18. Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier J. Meng et al. 10.1073/pnas.1917007117
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21. Spatio-temporal data generation based on separated attention for ENSO prediction L. Lin et al. 10.1007/s10489-024-05547-2
22. Committor Functions for Climate Phenomena at the Predictability Margin: The Example of El Niño–Southern Oscillation in the Jin and Timmermann Model D. Lucente et al. 10.1175/JAS-D-22-0038.1
23. Feature Selection and Spatial-Temporal Forecast of Oceanic Niño Index Using Deep Learning J. Jonnalagadda & M. Hashemi 10.1142/S0218194022500048
24. Unpacking the black box of deep learning for identifying El Niño-Southern oscillation Y. Sun et al. 10.1088/1572-9494/ace17d
25. The Added Value of Statistical Seasonal Forecasts F. Krikken et al. 10.3390/cli12060083
26. Multidimensional forecasting of precipitation and potential evapotranspiration in the Paranapanema river basin using neural network time series C. Mendez Vallejo & R. Lilla Manzione 10.1016/j.jsames.2024.104961
27. CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation M. Sun et al. 10.1029/2023GL105175
28. A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses L. Zhou & R. Zhang 10.1007/s00376-021-1368-4
29. Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa O. Adisa et al. 10.3390/su12166516
30. Prediction of Synoptic-Scale Sea Level Pressure Over the Indian Monsoon Region Using Deep Learning A. Sinha et al. 10.1109/LGRS.2021.3100899
31. A machine learning based prediction system for the Indian Ocean Dipole J. Ratnam et al. 10.1038/s41598-019-57162-8
32. 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
33. Forecasting the El Niño southern oscillation: physics, bias correction and combined models G. Reikard 10.1007/s00703-024-01038-8
34. Evaluation of the real-time El Niño forecasts by the climate network approach between 2011 and present A. Bunde et al. 10.1007/s00704-024-05035-0
35. Short‐Term Precipitation Prediction for Contiguous United States Using Deep Learning G. Chen & W. Wang 10.1029/2022GL097904
36. Spatiotemporal Model Based on Deep Learning for ENSO Forecasts H. Geng & T. Wang 10.3390/atmos12070810
37. Analysis of a Predictive Mathematical Model of Weather Changes Based on Neural Networks B. Malozyomov et al. 10.3390/math12030480
38. Tipping points in river deltas R. van de Vijsel et al. 10.1038/s43017-024-00610-5
39. Confronting Grand Challenges in environmental fluid mechanics T. Dauxois et al. 10.1103/PhysRevFluids.6.020501
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41. Predicting maximum temperatures over India 10-days ahead using machine learning models J. Ratnam et al. 10.1038/s41598-023-44286-1
42. Forecasting the El Niño type well before the spring predictability barrier J. Ludescher et al. 10.1038/s41612-023-00519-8
43. Evaluating the predictability of eight Atmospheric-Oceanic signals affecting Iran’s Droughts, employing intelligence based and stochastic methods P. Aghelpour et al. 10.1016/j.asr.2022.10.047
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46. Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing R. Silini et al. 10.5194/esd-13-1157-2022
47. Prediction of ENSO Beyond Spring Predictability Barrier Using Deep Convolutional LSTM Networks M. Gupta et al. 10.1109/LGRS.2020.3032353
48. A review of artificial intelligence in marine science T. Song et al. 10.3389/feart.2023.1090185
49. A Transformer‐Based Deep Learning Model for Successful Predictions of the 2021 Second‐Year La Niña Condition C. Gao et al. 10.1029/2023GL104034
50. A New Hybrid Prediction Method of El Niño/La Niña Events by Combining TimesNet and ARIMA Y. Du et al. 10.1109/ACCESS.2023.3319395
51. 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
52. ENSO analysis and prediction using deep learning: A review G. Wang et al. 10.1016/j.neucom.2022.11.078
53. Temporal Convolutional Networks for the Advance Prediction of ENSO J. Yan et al. 10.1038/s41598-020-65070-5
54. Ensemble Empirical Mode Decomposition with Adaptive Noise with Convolution Based Gated Recurrent Neural Network: A New Deep Learning Model for South Asian High Intensity Forecasting K. Peng et al. 10.3390/sym13060931
55. Machine Learning Models for the Seasonal Forecast of Winter Surface Air Temperature in North America Q. Qian et al. 10.1029/2020EA001140
56. El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition Y. Guo et al. 10.3390/sym12060893