References & Citations
Computer Science > Machine Learning
Title: Towards probabilistic Weather Forecasting with Conditioned Spatio-Temporal Normalizing Flows
(Submitted on 12 Nov 2023 (v1), last revised 28 Mar 2024 (this version, v2))
Abstract: Generative normalizing flows are able to model multimodal spatial distributions, and they have been shown to model temporal correlations successfully as well. These models provide several benefits over other types of generative models due to their training stability, invertibility and efficiency in sampling and inference. This makes them a suitable candidate for stochastic spatio-temporal prediction problems, which are omnipresent in many fields of sciences, such as earth sciences, astrophysics or molecular sciences. In this paper, we present conditional normalizing flows for stochastic spatio-temporal modelling. The method is evaluated on the task of daily temperature and hourly geopotential map prediction from ERA5 datasets. Experiments show that our method is able to capture spatio-temporal correlations and extrapolates well beyond the time horizon used during training.
Submission history
From: Christina Winkler [view email][v1] Sun, 12 Nov 2023 20:52:14 GMT (104039kb,D)
[v2] Thu, 28 Mar 2024 11:13:33 GMT (0kb,I)
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