Articles | Volume 15, issue 23
Model experiment description paper
13 Dec 2022
Model experiment description paper |  | 13 Dec 2022

Temperature forecasting by deep learning methods

Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz

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Cited articles

Ayzel, G., Heistermann, M., and Winterrath, T.: Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1), Geosci. Model Dev., 12, 1387–1402,, 2019. a
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644,, 2020. a
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Bihlo, A.: A generative adversarial network approach to (ensemble) weather prediction, arXiv [physics, stat], arXiv:, 2020. a, b, c, d, e
Brenowitz, N. D., Beucler, T., Pritchard, M., and Bretherton, C. S.: Interpreting and stabilizing machine-learning parametrizations of convection, J. Atmos. Sci., 77, 4357–4375, 2020. a
Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.