Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling

• URL: http://arxiv.org/abs/2305.14452v2
• Date: Tue, 30 May 2023 13:03:25 GMT
• Title: Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
• Authors: Qidong Yang, Alex Hernandez-Garcia, Paula Harder, Venkatesh Ramesh, Prasanna Sattegeri, Daniela Szwarcman, Campbell D. Watson, David Rolnick
• Abstract summary: We propose a downscaling method based on the Fourier neural operator. We show that our method significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models. Overall, our work bridges the gap between simulation of a physical process and low-resolution output.
• Score: 16.890326773246414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other.

Related papers

• Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling [3.8178633709015446]
  Given coarser-resolution projections from global climate models or satellite data, the downscaling problem aims to estimate finer-resolution regional climate data. This problem is societally crucial for effective adaptation, mitigation, and resilience against significant risks from climate change. We propose a novel Kriging-informed Conditional Diffusion Probabilistic Model (Ki-CDPM) to capture spatial variability while preserving fine-scale features.
  arXiv  Detail & Related papers  (2024-10-21T04:24:10Z)
• Causal Representation Learning in Temporal Data via Single-Parent Decoding [66.34294989334728]
  Scientific research often seeks to understand the causal structure underlying high-level variables in a system. Scientists typically collect low-level measurements, such as geographically distributed temperature readings. We propose a differentiable method, Causal Discovery with Single-parent Decoding, that simultaneously learns the underlying latents and a causal graph over them.
  arXiv  Detail & Related papers  (2024-10-09T15:57:50Z)
• Generative Adversarial Models for Extreme Geospatial Downscaling [0.0]
  This paper describes a conditional GAN-based geospatial downscaling method that can accommodate very high scaling factors. The method explicitly considers the uncertainty inherent to the downscaling process that tends to be ignored in existing methods. It produces a multitude of plausible high-resolution samples instead of one single deterministic result.
  arXiv  Detail & Related papers  (2024-02-21T18:25:04Z)
• Over-the-Air Federated Learning and Optimization [52.5188988624998]
  We focus on Federated learning (FL) via edge-the-air computation (AirComp) We describe the convergence of AirComp-based FedAvg (AirFedAvg) algorithms under both convex and non- convex settings. For different types of local updates that can be transmitted by edge devices (i.e., model, gradient, model difference), we reveal that transmitting in AirFedAvg may cause an aggregation error. In addition, we consider more practical signal processing schemes to improve the communication efficiency and extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes.
  arXiv  Detail & Related papers  (2023-10-16T05:49:28Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data [0.0]
  This work advances the recently developed PIESRGAN modeling approach to turbulent premixed combustion. The resulting model provides good results for a priori and a posteriori tests on direct numerical simulation data of a fully turbulent premixed flame kernel.
  arXiv  Detail & Related papers  (2022-10-28T15:27:46Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations [1.4150517264592128]
  We develop a weakly-supervised approach to solve the steady-state Navier-Stokes equations under various boundary conditions. We achieve state-of-the-art results without any labeled simulation data. We train stacked models of increasing complexity generating the numerical solutions for N-S equations.
  arXiv  Detail & Related papers  (2021-12-13T05:08:55Z)
• Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer [56.34005280792013]
  We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
  arXiv  Detail & Related papers  (2021-01-19T16:14:02Z)
• A Near-Optimal Gradient Flow for Learning Neural Energy-Based Models [93.24030378630175]
  We propose a novel numerical scheme to optimize the gradient flows for learning energy-based models (EBMs) We derive a second-order Wasserstein gradient flow of the global relative entropy from Fokker-Planck equation. Compared with existing schemes, Wasserstein gradient flow is a smoother and near-optimal numerical scheme to approximate real data densities.
  arXiv  Detail & Related papers  (2019-10-31T02:26:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.