Reconstructing high-resolution wind fields from sparse, low-resolution observations is a critical yet ill-posed problem in meteorological modeling. Classical approaches, such as Computational Fluid Dynamics (CFD), are often too computationally intensive to meet the demands of real- time or large-scale industrial applications. Meanwhile, conventional data-driven methods like Convolutional Neural Networks (CNNs) tend to produce overly smoothed outputs and struggle to recover fine-scale structures, especially under severe data sparsity.
This thesis explores the use of diffusion-based generative models for super-resolution in wind field reconstruction. A progressive SR3 (Super-Resolution via Repeated Refinement) frame- work is developed, combining a multi-stage architecture with stochastic denoising processes to gradually reconstruct high-resolution outputs. Extensive experiments demonstrate that the progressive SR3 consistently outperforms CNN-based baselines in terms of reconstruction accur- acy, perceptual quality, and robustness. Furthermore, a joint training strategy improves both performance and computational efficiency by enabling end-to-end optimization across stages.
The findings support the use of probabilistic diffusion models for meteorological super-resolution tasks and emphasize the effectiveness of progressive refinement in handling large upscaling factors. This approach provides a promising pathway for enhancing data-driven post-processing in atmospheric modeling.

In the coming decade, the Dutch government plans to broadly expand its wind farm real estate over the North Sea region. This is a significant undertaking because, from literature, it is understood that wind turbines can affect the local environment. These impacts include, but are not limited to, changes in variables such as wind speed and turbulent kinetic energy. To accurately capture these effects and improve power forecast accuracy, weather prediction models numerically represent the physical effects of a wind farm (i.e., a wind farm's behaviour as a momentum sink and a source of turbulent kinetic energy) by incorporating a wind farm parameterization in the model. This study looked at two simulations (with and without a wind farm parameterization) from HARMONIE-AROME, an operational weather model, and assessed the performance of the wind farm parameterization by using the simulation results for the Belgian wind farm zone in the North Sea, and comparing it with observational data from Sentinel-1 SAR, floating LiDARs, measured power as well as results from a large-eddy simulation. The data was also composited based on wind direction and atmospheric stability and analysed. Most importantly, it was found, after comprehensive analysis, that the wind farm parameterization caused a marked improvement in the results of the model especially in the region downwind of a wind farm where the effects are most severely experienced. Specifically, the simulation with the wind farm parameterization reduced the overall wind bias to -0.028 m/s from 0.602 m/s when compared with observational data for the selected region. The overall power bias was also found to be approximately 1.92\%. Several hypotheses based on existing literature were also tested, and along with the composite analysis, indicated that the simulation with the wind farm parameterization performed well in most stability regimes with the exception of stable conditions. It was recommended that this be further explored to determine the cause. Recommendations for expanding the study to look at wind farm effects on surface fluxes in offshore regions have also been made.