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Significant advancements in the development of machine learning (ML) models for weather forecasting have produced remarkable results.
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Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., Wei, F., Guo, B.: Swin Transformer V2: Scaling Up Capacity and Resolution (2022)
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Molnar, C., Casalicchio, G., Bischl, B.: Interpretable machine learning–a brief history, state-of-the-art and challenges. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 417–431 (2020). Springer
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Mamalakis, A., Ebert-Uphoff, I., Barnes, E.A.: Explainable artificial intelligence in meteorology and climate science: Model fine-tuning, calibrating trust and learning new science. In: International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, pp. 315–339 (2020). Springer
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2021
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Liu, L., He, G., Wu, M., Liu, G., Zhang, H., Chen, Y., Shen, J., Li, S.: Climate change impacts on planned supply–demand match in global wind and solar energy systems. Nature Energy, 1–11 (2023)
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Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., Tian, Q.: Accurate medium-range global weather forecasting with 3d neural networks. Nature (2023). https://doi.org/10.1038/s41586-023-06185-3
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Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., Li, H.: FuXi: A cascade machine learning forecasting system for 15-day global weather forecast (2023)
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Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russel, T., Sanchez-Gonzalez, A., Yang, V., Carver, R., Agrawal, S., Chantry, M., Bouallegue, Z.B., Dueben, P., Bromberg, C., Sisk, J., Barrington, L., Bell, A., Sha, F.: WeatherBench 2: A benchmark for the next generation of data-driven global weather models (2023)
2023
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Croitoru, F.-A., Hondru, V., Ionescu, R.T., Shah, M.: Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 45
2023
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Chen, L., Du, F., Hu, Y., Wang, F., Wang, Z.: SwinRDM: Integrate SwinRNN with Diffusion Model Towards High-Resolution and High-Quality Weather Forecasting. (2023). https://doi.org/10.48448/zn7f-fc64
2023
Closest in time.
Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., Li, H.: Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast (Version 1.0) [Dataset] [Software]. Zenodo. https://doi.org/10.5281/zenodo.8100201 (2023)
2023
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