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Qidong Yang

Alumni

Publications

Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad
Paula Harder
Qidong Yang
Prasanna Sattegeri
D. Szwarcman
Campbell Watson
David Rolnick
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding … (voir plus)and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types.
2024-07-16
ArXiv (prépublication)
doi.org
arxiv.org
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Qidong Yang
Alex Hernandez-Garcia
Paula Harder
Venkatesh Ramesh
Prasanna Sattegeri
D. Szwarcman
Campbell Watson
David Rolnick
Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computational… (voir plus)ly 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.
2023-05-22
ArXiv (prépublication)
doi.org
arxiv.org
Hard-Constrained Deep Learning for Climate Downscaling
Paula Harder
Venkatesh Ramesh
Alex Hernandez-Garcia
Qidong Yang
Prasanna Sattegeri
D. Szwarcman
Campbell Watson
David Rolnick
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and … (voir plus)mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and, therefore, often generate coarse-resolution predictions. Statistical downscaling, including super-resolution methods from deep learning, can provide an efficient method of upsampling low-resolution data. However, despite achieving visually compelling results in some cases, such models frequently violate conservation laws when predicting physical variables. In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics. We compare different constraining approaches and demonstrate their applicability across different neural architectures as well as a variety of climate and weather data sets. Besides enabling faster and more accurate climate predictions through downscaling, we also show that our novel methodologies can improve super-resolution for satellite data and natural images data sets.
2022-12-31
Journal of Machine Learning Research (publié)
arxiv.org
arxiv.org
Generating physically-consistent high-resolution climate data with hard-constrained neural networks
Paula Harder
Qidong Yang
Venkatesh Ramesh
Prasanna Sattegeri
Alex Hernandez-Garcia
Campbell Watson
D. Szwarcman
David Rolnick
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and … (voir plus)mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often can only make coarse resolution predictions. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often us-ing image super-resolution methods from computer vision. Despite achieving visually compelling results in some cases, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: A renor-malization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.
2021-12-31
arXiv.org (prépublication)
doi.org