Machine Learning Methods for Weather Forecasting: A Survey

Attention Is All You Need

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Long Short-Term Memory

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Deep learning

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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The ERA5 global reanalysis

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Generative adversarial networks

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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

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High-Resolution Image Synthesis with Latent Diffusion Models

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works

Visualizing and understanding convolutional networks

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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17325 · Cited by 7,752 · see citing works

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709 · see citing works

Improved Techniques for Training GANs

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Graph neural networks: A review of methods and applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works

Deep learning and process understanding for data-driven Earth system science

Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat (2019) · Nature · doi:10.1038/s41586-019-0912-1 · Cited by 5,673 · see citing works

SwinIR: Image Restoration Using Swin Transformer

Liang, J., Cao, J., Sun, G., Zhang, K., Gool, L. V., & Timofte, R. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00210 · Cited by 5,150 · see citing works

Generative Adversarial Networks: An Overview

Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540 · see citing works

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works

Axiomatic attribution for deep networks

Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works

SlowFast Networks for Video Recognition

Feichtenhofer, C., Fan, H., Malik, J., & He, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00630 · Cited by 4,007 · see citing works

The quiet revolution of numerical weather prediction

Bauer, P., Thorpe, A., & Brunet, G. (2015) · Nature · doi:10.1038/nature14956 · Cited by 2,729 · see citing works

Uformer: A General U-Shaped Transformer for Image Restoration

Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., & Li, H. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01716 · Cited by 2,424 · see citing works

Random Forests for land cover classification

Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.08.011 · Cited by 2,267 · see citing works

Diffusion Models in Vision: A Survey

Croitoru, F.-A., Hondru, V., Ionescu, R. T., & Shah, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3261988 · Cited by 1,965 · see citing works

Learning to Simulate Complex Physics with Graph NetworksPreprint

Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., & Battaglia, P. (2020) · International Conference on Machine Learning · doi:10.48550/arxiv.2002.09405 · Cited by 1,739 · see citing works

Deep learning in environmental remote sensing: Achievements and challenges

Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., et al. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111716 · Cited by 1,697 · see citing works

Accurate medium-range global weather forecasting with 3D neural networks

Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., & Tian, Q. (2023) · Nature · doi:10.1038/s41586-023-06185-3 · Cited by 1,670 · see citing works

Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications

Samek, W., Montavon, G., Lapuschkin, S., Anders, C. J., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3060483 · Cited by 1,477 · see citing works

Learning skillful medium-range global weather forecasting

Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., et al. (2023) · Science · doi:10.1126/science.adi2336 · Cited by 1,466 · see citing works

Generative Modeling by Estimating Gradients of the Data Distribution

Song, Y., & Ermon, S. (2019) · arXiv · doi:10.48550/arxiv.1907.05600 · Cited by 1,453 · see citing works

On the interpretation of weight vectors of linear models in multivariate neuroimaging

Haufe, S., Meinecke, F. C., Görgen, K., Dähne, S., Haynes, J., Blankertz, B., & Bießmann, F. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.067 · Cited by 1,367 · see citing works

Deep multi-scale video prediction beyond mean square errorPreprint

Mathieu, M., Couprie, C., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05440 · Cited by 1,182 · see citing works

Skilful precipitation nowcasting using deep generative models of radar

Ravuri, S., Lenc, K., Willson, M., Kangin, D., Lam, R., Mirowski, P., Fitzsimons, M., Athanassiadou, M., Kashem, S., Madge, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03854-z · Cited by 1,024 · see citing works

Changes in the Onset of Spring in the Western United States

Cayan, D. R., Dettinger, M. D., Kammerdiener, S. A., Caprio, J. M., & Peterson, D. H. (2001) · Bulletin of the American Meteorological Society · doi:10.1175/1520-0477(2001)082 · Cited by 906 · see citing works

The future intensification of hourly precipitation extremes

Prein, A., Rasmussen, R., Ikeda, K., Liu, C., Clark, M., & Holland, G. (2016) · Nature Climate Change · doi:10.1038/nclimate3168 · Cited by 885 · see citing works

Deep Learning for Precipitation Nowcasting: A Benchmark and A New ModelPreprint

Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D., Wong, W. K., & Woo, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.03458 · Cited by 830 · see citing works

Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods

Camps‐Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2013) · IEEE Signal Processing Magazine · doi:10.1109/msp.2013.2279179 · Cited by 786 · see citing works

High-Resolution Image Synthesis with Latent Diffusion Models

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021) · arXiv · doi:10.48550/arxiv.2112.10752 · Cited by 737 · see citing works

PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs

Wang, Y., Long, M., Wang, J., Gao, Z., & Yu, P. S. (2017) · neural information processing systems · Cited by 734 · see citing works

Physics-informed machine learning: case studies for weather and climate modelling

Kashinath, K., Mustafa, M., Albert, A., Wu, J., Jiang, C.-H., Esmaeilzadeh, S., Azizzadenesheli, K., Wang, R., Chattopadhyay, A., Singh, A., et al. (2021) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2020.0093 · Cited by 675 · see citing works

WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting

Rasp, S., Dueben, P. D., Scher, S., Weyn, J. A., Mouatadid, S., & Thuerey, N. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002203 · Cited by 663 · see citing works

Cross-city matters: A multimodal remote sensing benchmark dataset for cross-city semantic segmentation using high-resolution domain adaptation networks

Hong, D., Zhang, B., Li, H., Li, Y., Yao, J. T., Li, C., Werner, M., Chanussot, J., Zipf, A., & Zhu, X. X. (2023) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113856 · Cited by 561 · see citing works

Multimodal Classification of Remote Sensing Images: A Review and Future Directions

Gómez‐Chova, L., Tuia, D., Moser, G., & Camps‐Valls, G. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2449668 · Cited by 510 · see citing works

PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive LearningPreprint

Wang, Y., Gao, Z., Long, M., Wang, J., & Yu, P. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.06300 · Cited by 472 · see citing works

Skilful nowcasting of extreme precipitation with NowcastNet

Zhang, Y., Long, M., Chen, K., Xing, L., Jin, R., Jordan, M. I., & Wang, J. (2023) · Nature · doi:10.1038/s41586-023-06184-4 · Cited by 465 · see citing works

Extreme Weather Events and the Critical Importance of Anticipatory Adaptation and Organizational Resilience in Responding to Impacts

Linnenluecke, M. K., Griffiths, A., & Winn, M. I. (2011) · Business Strategy and the Environment · doi:10.1002/bse.708 · Cited by 463 · see citing works

Neural general circulation models for weather and climate

Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., et al. (2024) · Nature · doi:10.1038/s41586-024-07744-y · Cited by 454 · see citing works

Probabilistic weather forecasting with machine learning

Price, I., Sanchez-Gonzalez, A., Alet, F., Andersson, T. R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., et al. (2025) · Nature · doi:10.1038/s41586-024-08252-9 · Cited by 434 · see citing works

Earthformer: Exploring Space-Time Transformers for Earth System ForecastingPreprint

Gao, Z., Li, M., Shi, X., Wang, H., Wang, Y., Yeung, D.-Y., & Zhu, Y. (2022) · Neural Information Processing Systems · doi:10.48550/arxiv.2207.05833 · Cited by 431 · see citing works

Perceiver: General Perception with Iterative Attention

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Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Beck, H. E., Pan, M., Miralles, D. G., Reichle, R. H., Dorigo, W. A., Hahn, S., Sheffield, J., Karthikeyan, L., Balsamo, G., Parinussa, R., et al. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-17-2021 · Cited by 412 · see citing works

Learning Mesh-Based Simulation with Graph NetworksPreprint

Pfaff, T., Fortunato, M., Sánchez‐González, Á., & Battaglia, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.03409 · Cited by 400 · see citing works

FuXi: a cascade machine learning forecasting system for 15-day global weather forecast

Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., & Li, H. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00512-1 · Cited by 367 · see citing works

Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500‐hPa Geopotential Height From Historical Weather Data

Weyn, J. A., Durran, D. R., & Caruana, R. (2019) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001705 · Cited by 351 · see citing works

Computing receptive fields of convolutional neural networks

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Weather Forecasting Model using Artificial Neural Network

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Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate DatasetsPreprint

Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D. A., Kunkel, K. E., Wehner, M. F., & Collins, W. D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.01156 · Cited by 300 · see citing works

Improving Precipitation Estimation Using Convolutional Neural Network

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Deep Learning-Based Weather Prediction: A Survey

Ren, X., Li, X., Ren, K., Song, J., Xu, Z., Deng, K., & Wang, X. (2020) · Big Data Research · doi:10.1016/j.bdr.2020.100178 · Cited by 274 · see citing works

FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators

Kurth, T., Subramanian, S., Harrington, P., Pathak, J., Mardani, M., Hall, D., Miele, A., Kashinath, K., & Anandkumar, A. (2023) · Proceedings of the Platform for Advanced Scientific Computing Conference · doi:10.1145/3592979.3593412 · Cited by 268 · see citing works

Towards 100% renewable energy systems: Uncapping power system flexibility

Papaefthymiou, G., & Dragoon, K. (2016) · Energy Policy · doi:10.1016/j.enpol.2016.01.025 · Cited by 260 · see citing works

Deep learning for twelve hour precipitation forecasts

Espeholt, L., Agrawal, S., Sønderby, C. K., Kumar, M., Heek, J., Bromberg, C., Gazen, C., Carver, R. W., Andrychowicz, M., Hickey, J. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32483-x · Cited by 258 · see citing works

WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models

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Weather Forecasting for Renewable Energy System: A Review

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Machine Learning for Precipitation Nowcasting from Radar ImagesPreprint

Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., & Hickey, J. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12132 · Cited by 228 · see citing works

Improving the accuracy of rainfall rates from optical satellite sensors with machine learning — A random forests-based approach applied to MSG SEVIRI

Kühnlein, M., Appelhans, T., Thies, B., & Nauß, T. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.10.026 · Cited by 227 · see citing works

Predicting weather forecast uncertainty with machine learning

Scher, S., & Messori, G. (2018) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3410 · Cited by 225 · see citing works

Big data in precision agriculture: Weather forecasting for future farming

Bendre, M. R., Thool, R. C., & Thool, V. R. (2015) · International Conference on Next Generation Computing Technologies · doi:10.1109/ngct.2015.7375220 · Cited by 197 · see citing works

A hybrid CNN-LSTM model for typhoon formation forecasting

Chen, R., Wang, X., Zhang, W., Zhu, X., Li, A., & Yang, C. (2019) · GeoInformatica · doi:10.1007/s10707-019-00355-0 · Cited by 188 · see citing works

An Efficient Weather Forecasting System using Artificial Neural Network

Baboo, S. S., & Shereef, I. K. (2010) · International Journal of Environmental Science and Development · doi:10.7763/ijesd.2010.v1.63 · Cited by 186 · see citing works

ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather eventsPreprint

Racah, E., Beckham, C., Maharaj, T., Kahou, S. E., Prabhat, & Pal, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02095 · Cited by 184 · see citing works

A Dynamic Convolutional Layer for short rangeweather prediction

Klein, B., Wolf, L., & Afek, Y. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299117 · Cited by 179 · see citing works

Monthly Rainfall Forecasting Using One-Dimensional Deep Convolutional Neural Network

Haidar, A., & Verma, B. (2018) · IEEE Access · doi:10.1109/access.2018.2880044 · Cited by 154 · see citing works

CONUS404: The NCAR–USGS 4-km Long-Term Regional Hydroclimate Reanalysis over the CONUS

Rasmussen, R., Chen, F., Liu, C. H., Ikeda, K., Prein, A. F., Kim, J., Schneider, T., Dai, A., Gochis, D., Dugger, A. L., et al. (2023) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-21-0326.1 · Cited by 149 · see citing works

Deep Uncertainty Quantification

Wang, B., Jie Lu, B., Yan, Z., Luo, H., Li, T., Zheng, Y., & Zhang, G. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330704 · Cited by 149 · see citing works

CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological and other applications

Keller, V. D. J., Tanguy, M., Prosdocimi, I., Terry, J. A., Hitt, O., Cole, S. J., Fry, M., Morris, D. G., & Dixon, H. (2015) · Earth system science data · doi:10.5194/essd-7-143-2015 · Cited by 142 · see citing works

Potential of Intelligent Transportation Systems in Mitigating Adverse Weather Impacts on Road Mobility: A Review

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DeepDownscale: A Deep Learning Strategy for High-Resolution Weather Forecast

Rodrigues, E. R., Oliveira, I., Cunha, R. L. F., & Netto, M. A. S. (2018) · IEEE International Conference on eScience · doi:10.1109/escience.2018.00130 · Cited by 139 · see citing works

Verification of National Weather Service Ensemble Streamflow Predictions for Water Supply Forecasting in the Colorado River Basin

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A Short-Term Rainfall Prediction Model Using Multi-task Convolutional Neural Networks

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Convolutional LSTM Networks for Subcellular Localization of Proteins

Sønderby, S. K., Sønderby, C. K., Nielsen, H., & Winther, O. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21233-3_6 · Cited by 122 · see citing works

STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for weather forecasting

Castro, R., Souto, Y. M., Ogasawara, E., Porto, F. A. M., & Bezerra, E. A. D. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.09.060 · Cited by 117 · see citing works

Forecasting the weather of Nevada: A deep learning approach

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Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in Meteorological Applications

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Iberia01: a new gridded dataset of daily precipitation and temperatures over Iberia

Herrera, S., Cardoso, R. M., Soares, P. M. M., Espírito-Santo, F., Viterbo, P., & Gutiérrez, J. M. (2019) · Earth system science data · doi:10.5194/essd-11-1947-2019 · Cited by 110 · see citing works

Rainformer: Features Extraction Balanced Network for Radar-Based Precipitation Nowcasting

Bai, C., Sun, F., Zhang, J., Song, Y., & Chen, S. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3162882 · Cited by 108 · see citing works

A machine learning model that outperforms conventional global subseasonal forecast models

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Ensemble Methods for Neural Network‐Based Weather Forecasts

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Analyzing spatial relationship between land use/land cover (LULC) and land surface temperature (LST) of three urban agglomerations (UAs) of Eastern India

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On the Calculation and Correction of Equitable Threat Score for Model Quantitative Precipitation Forecasts for Small Verification Areas: The Example of Taiwan

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Deep Neural Network Modeling for Big Data Weather Forecasting

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Deep neural network based feature representation for weather forecasting

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Improving Subseasonal‐To‐Seasonal Prediction of Summer Extreme Precipitation Over Southern China Based on a Deep Learning Method

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Nowcasting extreme rain and extreme wind speed with machine learning techniques applied to different input datasets

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SwinVRNN: A Data‐Driven Ensemble Forecasting Model via Learned Distribution Perturbation

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An, J., Liu, J., Wang, D., Li, H., Yao, C., Wu, R., & Wu, Z. (2026) · Climate · doi:10.3390/cli14010024 · Cited by 1 · see citing works

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Kah, T. A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5377562 · Cited by 1 · see citing works

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Alihodžić, A. (2026) · arXiv · doi:10.48550/arxiv.2605.16435

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Wang, Y., Lyu, J., Singh, B., Dey, A., & Robertson, A. W. (2025) · Research Square · doi:10.21203/rs.3.rs-6486998/v1

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Lateef, M. B. (2025) · Research Square · doi:10.21203/rs.3.rs-7199339/v1

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Xie, W., Du, M., Li, C., & Du, G. (2025) · Atmosphere · doi:10.3390/atmos16101175

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Arasa, R., Ojeda, M. G., Picanyol, M., & Codina, B. (2025) · Earth · doi:10.3390/earth6040132

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Kabir, M. S., Chowdhury, A. A. N., Rahman, M., Rasel, M., & Ali, M. H. (2025) · Journal of Information Technology Cybersecurity and Artificial Intelligence · doi:10.70715/jitcai.2025.v2.i2.018

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Delina, M., Sepriando, A., & Nurdiansyah, R. C. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3139/1/012049

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Mzyece, L., Phiri, J., & Nyirenda, M. (2025) · doi:10.38124/ijisrt/25nov873

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Selvam, A. P., Saud, S. N., & Naronglerdrit, P. (2025) · IEEE International Conference on System Engineering and Technology (ICSET) · doi:10.1109/icset65917.2025.11284064

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Nath, A., Ishaan, A., Bhattacharjee, B., & Sarkar, S. (2025) · Research Square · doi:10.21203/rs.3.rs-8391655/v1

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Nacak, M. A., Sıçrar, E., Kızıltepe, A., Aksakallı, B., Katipoğlu, D., & Bayğın, N. (2025) · Journal of studies in advanced technologies. · doi:10.63063/jsat.1799427

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Ali, I. I., Nur, M. A., Mohamed, A. L., Gaal, I. M., Hussein, Z. A., Mohamud, A. A., & Said, M. A. (2026) · Springer eBooks · doi:10.1007/978-981-95-5214-6_20

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Li, J., Ding, D., Zhang, N., Zhu, X., & Aela, P. (2026) · Journal of Transportation Engineering Part A Systems · doi:10.1061/jtepbs.teeng-9326

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Gadelha, A. D. L. P., Drummond, L. M. A., & de Araújo, L. S. (2025) · doi:10.5753/eradse.2025.17119

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Liu, J., An, J., Yao, C., Wu, Z., Li, H., Zhou, X., Liu, G., & Wu, R. (2026) · Frontiers in Climate · doi:10.3389/fclim.2026.1776202

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Barnor, N., Kampouridis, M., & Kanellopoulos, P. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11539-0

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Mir, B. H., Eslamian, S., & Rather, N. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29811-0.00012-1

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Haseeb, M., Tahir, Z., Mehmood, S. A., Arif, H., Kousar, S., Ghafoor, S., & Mehmood, K. (2026) · Atmosphere · doi:10.3390/atmos17040411

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Nazir, A. A., Babar, J. U. M., Saiful, I., & Sohan, M. F. A. A. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11491226

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Dayanand, H. K., Agarwal, S., Dash, S. K., Rattanaudompisut, A., Puhan, P. S., & Mishra, S. (2025) · IEEE International Conference on Recent Trends in Computing and Smart Mobility (RCSM) · doi:10.1109/rcsm67767.2025.11506721

Evaluation of Machine Learning Algorithms for Weather Forecasting in Two Climatic Zones for Solar PV Modeling

Carlak, H. F., & Karabanova, K. (2026) · International Journal of Photoenergy · doi:10.1155/ijph/1979582

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Sahu, P., Arora, M., Raza, A. A., Singh, B., Sidhu, K. S., & Kumar, U. (2026) · International Conference on Smart Futuristic Technology (SMARTTECH) · doi:10.1109/icsft66733.2026.11506631

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Seidu, J., Ampomah, E. K., Asante, E., & Frimpong, K. A. (2026) · Frontiers in Climate · doi:10.3389/fclim.2026.1845902

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Bao, S.-S., Zheng, L.-Q., Wang, Z., Du, N., Li, Y.-G., & Zhang, T. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110547

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Al-Omari, H., Yaghi, M. A., & Alrifai, L. (2026) · Algorithms · doi:10.3390/a19050394

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Lu, Y., Mo, X., & Li, H. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00823-2

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Muntaha, S., & Dey, A. (2025) · IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) · doi:10.1109/wiecon-ece69386.2025.11526039

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Haris, M. F., Norwawi, N. M., Isa, M. H. M., Rahman, M. Z. H. A., Ghani, N. F. A., & Zain, M. R. M. (2026) · Modeling Earth Systems and Environment · doi:10.1007/s40808-026-02824-6

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Jiang, P., Guan, B., & Zheng, X. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133042

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Xiao, J., Chen, X., Wang, B., & Pan, X. (2026) · Atmosphere · doi:10.3390/atmos17060549

Inversion of Sound Speed Profile Controlled by Sparse Observations: Research Background, Current Status and Technical Analysis

Fan, H., Xie, S., & Xue, S. (2026) · Oceans · doi:10.3390/oceans7030045

A Deep Learning Framework for Multivariate Weather Prediction using Sequential and Hybrid Architectures

K.Praveena, Pavithra, P., Lakshmi, K., Rayudu, U. M., Babu, N. R., & Reddy, P. C. S. (2026) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei68323.2026.11541936

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Dureja, V., & Sharma, M. (2026) · Research Square · doi:10.21203/rs.3.rs-9327624/v1

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Kaan, A. R. I. K., & AĞGÜL, B. (2026) · Journal of Mathematical Sciences and Modelling · doi:10.33187/jmsm.1837729

hSmart Weather Forecasting for Agriculture Using Machine Learning and Multi-Source Data Integration

Mujawar, R. Y., Pawar, A. A., Reddy, N. V., Bhaskar, A. P., Chaubey, N., & Selvan, R. S. (2025) · International Conference on Emerging Engineering Technologies and Applications (IC-EETA) · doi:10.1109/ic-eeta66496.2025.11548210

STADEN: A Dual‐Branch Deep Learning Framework With Multithreshold Loss for Robust Radar Echo Extrapolation

Luo, H., Zhou, H., Xie, N., Yang, K., & Zhang, W. (2026) · Advances in Meteorology · doi:10.1155/adme/6492939

A Scalable IoT-Cloud Architecture for Wind Speed Prediction Leveraging LightGBM

Poovizhi, P., Kanmani, P., Vinnarasi, J., V, Hemalatha, T., Nishanthi, A., & Priyadharshini, G. (2026) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca69024.2026.11570518

Enhancing Weather Forecasting Accuracy Using Machine Learning Models: Performance Evaluation of Coarse Tree Classifier

Boda, R., Das, S., Chaurasia, M. A., Sarfaraz, R., Narsimha, T. L., & Reddy, P. A. (2025) · IEEE India Geoscience and Remote Sensing Symposium (InGARSS) · doi:10.1109/ingarss67683.2025.11584035

Análise Comparativa de Modelos de Aprendizado de Máquina na Predição de Variáveis Climáticas para Aplicações Agrícolas

Ribeiro, F., Silveira, R., & Ferreira, C. (2026) · Revista Eletrônica de Iniciação Científica em Computação · doi:10.5753/reic.2026.8067

Maximum Temperature Forecasts in the Yangtze River Valley Using a Predictor‐Corrector Lightweight Deep Learning

Tong, X., & Zhou, W. (2026) · Geophysical Research Letters · doi:10.1029/2025gl117592

Assessment of Nowcasting Precipitation Schemes Initialized from LAPS Analysis Fields over the Attica Region

Pappa, A., Kalogiros, J. A., Tombrou, M., Παπαδόπουλος, Α., & Katsafados, P. (2026) · Atmosphere · doi:10.3390/atmos17080714

Machine Learning-Based Multivariate Weather Prediction for Agricultural Decision Support in Aceh Besar

Nurhanif, Yanti, Y., Maghfirah, G., Muliadi, Zakaria, & Surbakti, M. S. (2026) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1646/1/012019

The Lightweight Hybrid Deep Learning Approach for Capturing Long-Term and Short-Term Constraints for an Accurate Solar Radiation Forecast

Alkhaldi, N. S. (2026) · Processes · doi:10.3390/pr14152449

Data-driven design of carbon dots: Property prediction, optimization, and prospects for autonomous discovery

Kharma, Q. M., Abdulakimov, G., Adhyaru, Y. B. K., Johar, M. G. M., Mostafa, S. A., Parhi, M., Wasson, V., Mohammed, M. W., & Chavoushi, T. H. (2026) · Chemical Engineering Journal Advances · doi:10.1016/j.ceja.2026.101404

Leveraging Multi-Source Feature Extraction and STF-SOPAN for High-Accuracy Wind Speed Prediction

He, X., & Lan, L. (2026) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac71005.2026.11616477

Appraisal of the Difficulties and Interpretability of Machine Lerning Methods for Weather Forecasting

KUMAR, K. T. A. K., Madhusudhan, M., Divya, P., Bhushanam, S. S., & Komalavalli, D. (2026) · CRC Press eBooks · doi:10.1201/9781003739937-16

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Arakh, R., Gupta, Y., Kriplani, S., & Sharma, S. (2024) · International Journal of Recent Advances in Multidisciplinary Topics · doi:10.5281/zenodo.11387745

A Review Paper on Machine Learning Models for Weather Forecasting: Techniques, Trends, And Challenges

Jakhar, M., & Srivastava, V. (2025) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2025.v07i06.63715

Application of Machine Learning in Weather Forecasting

Wang, S. (2025) · Applied and Computational Engineering · doi:10.54254/2755-2721/2025.ld28521

Comprehensive Analysis of Weather Forecasting Techniques

Himanshu, -, G. S., -, S., & -, N. R. (2024) · International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences · doi:10.37082/ijirmps.v12.i5.231092 · Cited by 1 · see citing works