Status: this preprint is currently under review for the journal WES.
The Deep Wind Method: Physics-Informed Wind Field Reconstruction with Mass Consistency
Daniel Alejandro Cervantes Cabreraand Miguel Angel Moreles Vázquez
Abstract. We present the Deep Wind methodology, a physics-informed neural network (PINN) formulation for reconstructing three-dimensional wind fields from incomplete and noisy data. The approach embeds mass conservation and boundary conditions directly into the loss function, enabling physically consistent and stable reconstructions without mesh-based discretization. A series of synthetic benchmarks and real observations from Super Typhoon Kong-Rey (2024) demonstrate the robustness of the method compared to classical variational approaches. We show that Deep Wind consistently maintains stability and accuracy under sparse, irregular, or noisy observations. Overall, the results suggest that physics-informed deep learning is a promising framework for wind field recovery and data assimilation, particularly in meteorology and wind energy.
Received: 29 Aug 2025 – Discussion started: 15 Oct 2025
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We developed a new method to rebuild wind fields when data are missing or noisy. The model is trained with both observations and knowledge of physical laws and boundary conditions. Tests with real data from Typhoon Kong-Rey show that this physics-informed approach gives more reliable and accurate results than traditional techniques, offering a better way to understand wind behavior and support meteorology and wind energy applications.
We developed a new method to rebuild wind fields when data are missing or noisy. The model is...
