Generative Diffusion-based Downscaling for Climate

• URL: http://arxiv.org/abs/2404.17752v1
• Date: Sat, 27 Apr 2024 01:49:14 GMT
• Title: Generative Diffusion-based Downscaling for Climate
• Authors: Robbie A. Watt, Laura A. Mansfield,
• Abstract summary: Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling. We show how a generative, diffusion-based approach to downscaling gives accurate downscaled results. This research highlights the potential of diffusion-based downscaling techniques in providing reliable and detailed climate predictions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate model output. Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling. Here, we show how a generative, diffusion-based approach to downscaling gives accurate downscaled results. We focus on an idealised setting where we recover ERA5 at $0.25\degree$~resolution from coarse grained version at $2\degree$~resolution. The diffusion-based method provides superior accuracy compared to a standard U-Net, particularly at the fine scales, as highlighted by a spectral decomposition. Additionally, the generative approach provides users with a probability distribution which can be used for risk assessment. This research highlights the potential of diffusion-based downscaling techniques in providing reliable and detailed climate predictions.

Related papers

• Capturing Climatic Variability: Using Deep Learning for Stochastic Downscaling [0.0]
  Adapting to the changing climate requires accurate local climate information. Capturing variability while downscaling is crucial for estimating uncertainty and characterising extreme events. We propose approaches to improve the calibration of GANs in three ways.
  arXiv  Detail & Related papers  (2024-05-31T03:04:10Z)
• Rejection via Learning Density Ratios [50.91522897152437]
  Classification with rejection emerges as a learning paradigm which allows models to abstain from making predictions. We propose a different distributional perspective, where we seek to find an idealized data distribution which maximizes a pretrained model's performance. Our framework is tested empirically over clean and noisy datasets.
  arXiv  Detail & Related papers  (2024-05-29T01:32:17Z)
• Diffusion Model-based Probabilistic Downscaling for 180-year East Asian Climate Reconstruction [8.132450337453525]
  We introduce a diffusion probabilistic downscaling model (DPDM) into the meteorological field. This model can efficiently transform data from 1deg to 0.1deg resolution. We apply the model to generate a 180-year dataset of monthly surface variables in East Asia.
  arXiv  Detail & Related papers  (2024-02-02T01:34:33Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model [7.784934642915291]
  This paper presents a deep generative model for downscaling climate data, specifically precipitation on a regional scale. We employ a denoising diffusion probabilistic model conditioned on multiple LR climate variables. Our results demonstrate significant improvements over existing baselines, underscoring the effectiveness of the conditional diffusion model in downscaling climate data.
  arXiv  Detail & Related papers  (2023-12-12T09:39:52Z)
• Precipitation Downscaling with Spatiotemporal Video Diffusion [19.004369237435437]
  This work extends recent video diffusion models to precipitation super-resolution. We use a deterministic downscaler followed by a temporally-conditioned diffusion model to capture noise characteristics and high-frequency patterns. Our analysis, capturing CRPS, MSE, precipitation distributions, and qualitative aspects using California and the Himalayas, establishes our method as a new standard for data-driven precipitation downscaling.
  arXiv  Detail & Related papers  (2023-12-11T02:38:07Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts [0.5906031288935515]
  Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps.
  arXiv  Detail & Related papers  (2022-04-05T07:19:42Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation [92.96204497841032]
  Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. We propose a model-agnostic data augmentation method that allows us to exploit the prior knowledge of the conditional independence (CI) relations. We experimentally show that the proposed method is effective in improving the prediction accuracy, especially in the small-data regime.
  arXiv  Detail & Related papers  (2021-02-27T06:13:59Z)

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.