Atmosphere, Volume 15, Issue 1
2024 January - 141 articles
Cover Story: The accurate estimation of systems from incomplete observations challenges atmospheric and oceanic applications, where the environment is characterized by multi-scale turbulent dynamics. This study addresses the data reconstruction problem in a rotating turbulent flow with spatial gaps, using advanced machine learning tools known as generative diffusion models (DMs). Our work compares the effectiveness of DMs with the previously best-performing method due to Generative Adversarial Networks and shows that DMs offer superior performance in terms of point-wise reconstruction and statistical accuracy. The inherent stochasticity of DMs results in probabilistic reconstructions providing a spectrum of predictions for the same data, thus enhancing uncertainty quantification and risk assessment in geophysical problems. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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