Physically Constrained Generative Adversarial Networks for Improving
Precipitation Fields from Earth System Models
- URL: http://arxiv.org/abs/2209.07568v1
- Date: Thu, 25 Aug 2022 15:19:10 GMT
- Title: Physically Constrained Generative Adversarial Networks for Improving
Precipitation Fields from Earth System Models
- Authors: Philipp Hess, Markus Dr\"uke, Stefan Petri, Felix M. Strnad, and
Niklas Boers
- Abstract summary: Existing post-processing methods can improve ESM simulations locally, but cannot correct errors in modelled spatial patterns.
We propose a framework based on physically constrained generative adversarial networks (GANs) to improve local distributions and spatial structure simultaneously.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Precipitation results from complex processes across many scales, making its
accurate simulation in Earth system models (ESMs) challenging. Existing
post-processing methods can improve ESM simulations locally, but cannot correct
errors in modelled spatial patterns. Here we propose a framework based on
physically constrained generative adversarial networks (GANs) to improve local
distributions and spatial structure simultaneously. We apply our approach to
the computationally efficient ESM CM2Mc-LPJmL. Our method outperforms existing
ones in correcting local distributions, and leads to strongly improved spatial
patterns especially regarding the intermittency of daily precipitation.
Notably, a double-peaked Intertropical Convergence Zone, a common problem in
ESMs, is removed. Enforcing a physical constraint to preserve global
precipitation sums, the GAN can generalize to future climate scenarios unseen
during training. Feature attribution shows that the GAN identifies regions
where the ESM exhibits strong biases. Our method constitutes a general
framework for correcting ESM variables and enables realistic simulations at a
fraction of the computational costs.
Related papers
- UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather [55.95708988160047]
LiDAR semantic segmentation (LSS) is a critical task in autonomous driving.
Prior LSS methods are investigated and evaluated on datasets within the same domain in clear weather.
We propose UniMix, a universal method that enhances the adaptability and generalizability of LSS models.
arXiv Detail & Related papers (2024-04-08T02:02:15Z) - Conditional diffusion models for downscaling & bias correction of Earth system model precipitation [1.5193424827619018]
We propose a novel machine learning framework for simultaneous bias correction and downscaling.
Our approach ensures statistical fidelity, preserves large-scale spatial patterns and outperforms existing methods.
arXiv Detail & Related papers (2024-04-05T11:01:50Z) - Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System
Model Fields with Generative Foundation Models [0.0]
We develop a consistency model (CM) that efficiently and accurately downscales arbitrary Earth system model (ESM) simulations without retraining in a zero-shot manner.
We show that the CM outperforms state-of-the-art diffusion models at a fraction of computational cost while maintaining high controllability on the downscaling task.
arXiv Detail & Related papers (2024-03-05T08:41:41Z) - Spatial Attention-based Distribution Integration Network for Human Pose
Estimation [0.8052382324386398]
We present the Spatial Attention-based Distribution Integration Network (SADI-NET) to improve the accuracy of localization.
Our network consists of three efficient models: the receptive fortified module (RFM), spatial fusion module (SFM), and distribution learning module (DLM)
Our model obtained a remarkable $92.10%$ percent accuracy on the MPII test dataset, demonstrating significant improvements over existing models and establishing state-of-the-art performance.
arXiv Detail & Related papers (2023-11-09T12:43:01Z) - 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) - Learning Controllable Adaptive Simulation for Multi-resolution Physics [86.8993558124143]
We introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model.
LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening.
We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error.
arXiv Detail & Related papers (2023-05-01T23:20:27Z) - Deep learning for bias-correcting CMIP6-class Earth system models [0.0]
We show that a post-processing method based on physically constrained generative adversarial networks (cGANs) can correct biases of a state-of-the-art, CMIP6-class ESM.
While our method improves local frequency distributions equally well as gold-standard bias-adjustment frameworks, it strongly outperforms any existing methods in the correction of spatial patterns.
arXiv Detail & Related papers (2022-12-16T13:53:57Z) - Deep Learning Based Cloud Cover Parameterization for ICON [55.49957005291674]
We train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON simulations.
Globally trained NNs can reproduce sub-grid scale cloud cover of the regional simulation.
We identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained data.
arXiv Detail & Related papers (2021-12-21T16:10:45Z) - Reinforcement Learning for Adaptive Mesh Refinement [63.7867809197671]
We propose a novel formulation of AMR as a Markov decision process and apply deep reinforcement learning to train refinement policies directly from simulation.
The model sizes of these policy architectures are independent of the mesh size and hence scale to arbitrarily large and complex simulations.
arXiv Detail & Related papers (2021-03-01T22:55:48Z) - DeepClimGAN: A High-Resolution Climate Data Generator [60.59639064716545]
Earth system models (ESMs) are often used to generate future projections of climate change scenarios.
As a compromise, emulators are substantially less expensive but may not have all of the complexity of an ESM.
Here we demonstrate the use of a conditional generative adversarial network (GAN) to act as an ESM emulator.
arXiv Detail & Related papers (2020-11-23T20:13:37Z)
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.