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There’s a pretty remarkable dataset available in the realm of AI for science: the ERA5 global reanalysis dataset, which contains hourly estimates of numerous 3D atmospheric, land, and ocean features at a horizontal resolution of 30 km over the past 80 years. This dataset was synthesized from up to 25M-per-day measurements from Earth-observing satellites and weather stations. Can machine learning be used to learn to forecast the weather better, either by more efficiently/effectively solving the weather PDEs or by learning a better model?
The FourCastNet paper [Pathak, 2022] trains an Adaptive Fourier Neural Operator (AFNO) network [Guibas, 2022] to predict a collection of atmospheric variables at the next time step (6 hours into the future) given the current readout for those variables. The AFNO is an interesting choice here: it takes inspiration from both neural operators, which are designed to efficiently solve PDEs, and Vision Transformers (ViT), which can learn complex functions from large image datasets. We know that weather is well-modeled by PDEs, and the ERA 5 dataset is converted into images where each pixel location corresponds to a \(.25^\circ \times .25^\circ\) latitude/longitude region, and each channel corresponds to a different atmospheric variable, so maybe some amalgam of neural operators and transformers makes sense.
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