Wasserstein GAN-Based Precipitation Downscaling with Optimal Transport for Enhancing Perceptual Realism

• URL: http://arxiv.org/abs/2507.17798v1
• Date: Wed, 23 Jul 2025 15:29:34 GMT
• Title: Wasserstein GAN-Based Precipitation Downscaling with Optimal Transport for Enhancing Perceptual Realism
• Authors: Kenta Shiraishi, Yuka Muto, Atsushi Okazaki, Shunji Kotsuki,
• Abstract summary: This study proposes using Wasserstein Generative Adversarial Network (WGAN) to perform precipitation downscaling with an optimal transport cost.<n>In contrast to a conventional neural network trained with mean squared error, the WGAN generated visually realistic precipitation fields with fine-scale structures.<n>Case-based analysis revealed that large discrepancies in critic scores can help identify both unrealistic WGAN outputs and potential artifacts in the reference data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-resolution (HR) precipitation prediction is essential for reducing damage from stationary and localized heavy rainfall; however, HR precipitation forecasts using process-driven numerical weather prediction models remains challenging. This study proposes using Wasserstein Generative Adversarial Network (WGAN) to perform precipitation downscaling with an optimal transport cost. In contrast to a conventional neural network trained with mean squared error, the WGAN generated visually realistic precipitation fields with fine-scale structures even though the WGAN exhibited slightly lower performance on conventional evaluation metrics. The learned critic of WGAN correlated well with human perceptual realism. Case-based analysis revealed that large discrepancies in critic scores can help identify both unrealistic WGAN outputs and potential artifacts in the reference data. These findings suggest that the WGAN framework not only improves perceptual realism in precipitation downscaling but also offers a new perspective for evaluating and quality-controlling precipitation datasets.

Related papers

• RainScaleGAN: a Conditional Generative Adversarial Network for Rainfall Downscaling [0.0]
  We introduce RainScaleGAN, a conditional deep convolutional Generative Adrial Network (GAN) for precipitation downscaling.<n>RainScaleGAN's capabilities are tested in a perfect-model setup, where the spatial resolution of a precipitation dataset is artificially degraded.<n>The developed model outperforms one of the leading precipitation downscaling method found in the literature.
  arXiv  Detail & Related papers  (2025-03-17T15:54:20Z)
• The Merit of River Network Topology for Neural Flood Forecasting [3.731618046702812]
  Climate change exacerbates riverine floods, which occur with higher frequency and intensity than ever. Forecasting systems typically rely on accurate river discharge predictions. incorporating the known topology of the river network into the prediction model has the potential to leverage the adjacency relationship between gauges.
  arXiv  Detail & Related papers  (2024-05-30T08:45:45Z)
• CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [93.65319031345197]
  We propose CasCast, a cascaded framework composed of a deterministic and a probabilistic part to decouple predictions for mesoscale precipitation distributions and small-scale patterns. CasCast significantly surpasses the baseline (up to +91.8%) for regional extreme-precipitation nowcasting.
  arXiv  Detail & Related papers  (2024-02-06T08:30:47Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• 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)
• Unmasking Bias in Diffusion Model Training [40.90066994983719]
  Denoising diffusion models have emerged as a dominant approach for image generation. They still suffer from slow convergence in training and color shift issues in sampling. In this paper, we identify that these obstacles can be largely attributed to bias and suboptimality inherent in the default training paradigm.
  arXiv  Detail & Related papers  (2023-10-12T16:04:41Z)
• 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)
• Towards replacing precipitation ensemble predictions systems using machine learning [0.0]
  We propose a new approach to generating ensemble weather predictions for high-resolution precipitation. The method uses generative adversarial networks to learn the complex patterns of precipitation. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions.
  arXiv  Detail & Related papers  (2023-04-20T12:20:35Z)
• 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)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall [21.399707529966474]
  We present TRU-NET, an encoder-decoder model featuring a novel 2D cross attention mechanism between contiguous convolutional-recurrent layers. We use a conditional-continuous loss function to capture the zero-skewed %extreme event patterns of rainfall. Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction.
  arXiv  Detail & Related papers  (2020-08-20T17:27:59Z)
• From Rain Generation to Rain Removal [67.71728610434698]
  We build a full Bayesian generative model for rainy image where the rain layer is parameterized as a generator. We employ the variational inference framework to approximate the expected statistical distribution of rainy image. Comprehensive experiments substantiate that the proposed model can faithfully extract the complex rain distribution.
  arXiv  Detail & Related papers  (2020-08-08T18:56:51Z)

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.