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Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models

Jan Martinů, Petr Šimánek

TL;DR

The results indicate that the ResDiff model significantly outperforms traditional SR3 methods in terms of Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR).

Abstract

This study investigates the application of deep-learning diffusion models for the super-resolution of weather data, a novel approach aimed at enhancing the spatial resolution and detail of meteorological variables. Leveraging the capabilities of diffusion models, specifically the SR3 and ResDiff architectures, we present a methodology for transforming low-resolution weather data into high-resolution outputs. Our experiments, conducted using the WeatherBench dataset, focus on the super-resolution of the two-meter temperature variable, demonstrating the models' ability to generate detailed and accurate weather maps. The results indicate that the ResDiff model, further improved by incorporating physics-based modifications, significantly outperforms traditional SR3 methods in terms of Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). This research highlights the potential of diffusion models in meteorological applications, offering insights into their effectiveness, challenges, and prospects for future advancements in weather prediction and climate analysis.

Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models

TL;DR

The results indicate that the ResDiff model significantly outperforms traditional SR3 methods in terms of Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR).

Abstract

This study investigates the application of deep-learning diffusion models for the super-resolution of weather data, a novel approach aimed at enhancing the spatial resolution and detail of meteorological variables. Leveraging the capabilities of diffusion models, specifically the SR3 and ResDiff architectures, we present a methodology for transforming low-resolution weather data into high-resolution outputs. Our experiments, conducted using the WeatherBench dataset, focus on the super-resolution of the two-meter temperature variable, demonstrating the models' ability to generate detailed and accurate weather maps. The results indicate that the ResDiff model, further improved by incorporating physics-based modifications, significantly outperforms traditional SR3 methods in terms of Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). This research highlights the potential of diffusion models in meteorological applications, offering insights into their effectiveness, challenges, and prospects for future advancements in weather prediction and climate analysis.
Paper Structure (18 sections, 9 equations, 4 figures, 1 table)

This paper contains 18 sections, 9 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Literature
  3. Methods
  4. SR3
  5. ResDiff
  6. FD Info Splitter
  7. HF-guided Cross-Attention
  8. ResDiff Enhanced with Physics-Inspired Convolutional Filters
  9. Motivation
  10. Convolutional Derivative Filters
  11. Integration into ResDiff
  12. Experiments
  13. Data
  14. Description of the experiments
  15. Results
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Architecture of Resdiff model used for Climate Variable downscaling.
  • Figure 2: Resdiff image sampling architecture.
  • Figure 3: Validation scores during training across models: SR3, ResDiff, ResDiff + Physics.
  • Figure 4: In the left column, the HR reference image and images generated by the models are displayed, annotated with their corresponding temperatures in Kelvin. The right column shows the absolute error between HR reference and super-resolution images.