Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds

• URL: http://arxiv.org/abs/2510.03364v1
• Date: Fri, 03 Oct 2025 03:38:58 GMT
• Title: Diffusion-Based, Data-Assimilation-Enabled Super-Resolution of Hub-height Winds
• Authors: Xiaolong Ma, Xu Dong, Ashley Tarrant, Lei Yang, Rao Kotamarthi, Jiali Wang, Feng Yan, Rajkumar Kettimuthu,
• Abstract summary: WindSR is a diffusion model with data assimilation for super-resolution downscaling of hub-height winds.<n>WindSR integrates sparse observational data with simulation fields during downscaling using state-of-the-art diffusion models.<n>Our data assimilation reduces WindSR's model bias by approximately 20% relative to independent observations.
• Score: 15.944027638997675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality observations of hub-height winds are valuable but sparse in space and time. Simulations are widely available on regular grids but are generally biased and too coarse to inform wind-farm siting or to assess extreme-weather-related risks (e.g., gusts) at infrastructure scales. To fully utilize both data types for generating high-quality, high-resolution hub-height wind speeds (tens to ~100m above ground), this study introduces WindSR, a diffusion model with data assimilation for super-resolution downscaling of hub-height winds. WindSR integrates sparse observational data with simulation fields during downscaling using state-of-the-art diffusion models. A dynamic-radius blending method is introduced to merge observations with simulations, providing conditioning for the diffusion process. Terrain information is incorporated during both training and inference to account for its role as a key driver of winds. Evaluated against convolutional-neural-network and generative-adversarial-network baselines, WindSR outperforms them in both downscaling efficiency and accuracy. Our data assimilation reduces WindSR's model bias by approximately 20% relative to independent observations.

Related papers

• Turbulence Regression [0.4779196219827507]
  This paper introduces a NeuTucker decomposition model utilizing discretized data.<n>It constructs a low-rank Tucker decomposition model based on a Tucker neural network to capture latent interactions within the three-dimensional wind field data.<n>In estimating missing observations in real datasets, this discretized NeuTucF model demonstrates superior performance compared to various common regression models.
  arXiv  Detail & Related papers  (2025-12-05T07:20:44Z)
• Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator [0.0]
  We introduce the first autoregressive machine learning surrogate for steady-state solar wind radial velocity.<n>The model improves accuracy in distant regions compared to a single-step approach.<n>Compared with the numerical HUX surrogate, SFNO demonstrates superior or comparable performance.
  arXiv  Detail & Related papers  (2025-11-25T20:30:03Z)
• WindMiL: Equivariant Graph Learning for Wind Loading Prediction [0.21847754147782886]
  WindMiL is a new machine learning framework that combines systematic dataset generation with symmetry-aware graph neural networks (GNNs)<n>By pairing a systematic dataset with an equivariant surrogate, WindMiL enables efficient, scalable, and accurate predictions of wind loads on buildings.
  arXiv  Detail & Related papers  (2025-11-03T04:55:08Z)
• Wind Speed Forecasting Based on Data Decomposition and Deep Learning Models: A Case Study of a Wind Farm in Saudi Arabia [0.0]
  Wind power generation is always accompanied by uncertainty due to the wind speed's volatility.<n>Wind speed forecasting (WSF) is essential for power grids' dispatch, stability, and controllability.<n>This study proposes a novel WSF framework for stationary data based on a hybrid decomposition method.
  arXiv  Detail & Related papers  (2024-12-17T22:04:46Z)
• Super Resolution for Renewable Energy Resource Data With Wind From Reanalysis Data and Application to Ukraine [0.0]
  There is an expanding global need for historically accurate, hourlyly continuous, high-resolution wind data.<n>We present a deep learning-based downscaling method using adversarial networks (GANs)<n>This GAN-based downscaling method reduces computational costs over dynamical downscaling by two orders of magnitude.
  arXiv  Detail & Related papers  (2024-07-26T21:07:17Z)
• Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales [5.427841765899196]
  We demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather.<n>By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained.<n>It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.
  arXiv  Detail & Related papers  (2024-06-19T10:28:11Z)
• 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)
• 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)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Physics Informed Shallow Machine Learning for Wind Speed Prediction [66.05661813632568]
  We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in Italy. We train supervised learning algorithms using the past history of wind to predict its value at a future time. We find that the optimal design as well as its performance vary with the location.
  arXiv  Detail & Related papers  (2022-04-01T14:55:10Z)
• 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)

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.