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Poster
in
Workshop: Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges

Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

Qidong Yang · Paula Harder · Venkatesh Ramesh · Alex Hernandez-Garcia · Daniela Szwarcman · Prasanna Sattigeri · Campbell Watson · David Rolnick

Keywords: [ Computer vision and remote sensing ] [ Earth observations and monitoring ] [ generative modeling ] [ Climate science and climate modeling ]


Abstract:

Running climate simulations informs us of future climate change. However, it is computationally expensive to resolve complex climate processes numerically. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations. So far, all neural network downscaling models can only downscale input samples with a pre-defined upsampling factor. In this work, we propose a Fourier neural operator downscaling model. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high-resolutions. Evaluated on Navier-Stokes equation solution data and ERA5 water content data, our downscaling model demonstrates better performance than widely used convolutional and adversarial generative super-resolution models in both learned and zero-shot downscaling. Our model's performance is further boosted when a constraint layer is applied. In the end, we show that by combining our downscaling model with a low-resolution numerical PDE solver, the downscaled solution outperforms the solution of the state-of-the-art high-resolution data-driven solver. Our model can be used to cheaply and accurately generate arbitrarily high-resolution climate simulation data with fast-running low-resolution simulation as input.

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