WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
Researchers have introduced WindINR, a novel latent-state implicit neural representation framework designed to provide fast, high-resolution wind estimates in complex terrains. Unlike traditional methods that generate dense forecast fields on fixed grids, WindINR allows for continuous queries at specific user-defined locations and heights. The system maps static terrain descriptors and low-resolution background fields to high-resolution wind states via a latent-conditioned decoder. A key innovation is the separation of reusable representation learning from sample-specific latent-state correction. During inference, network weights remain fixed while only the compact latent state is updated using sparse observations and their uncertainties. This approach significantly accelerates processing, achieving a 2.6x speedup over full-network fine-tuning in CPU benchmarks. Validated through controlled Observing System Simulation Experiments (OSSEs) in the Senja region, including UAV-aided scenarios, WindINR demonstrates improved accuracy and robustness. It serves as a practical interface between kilometer-scale background products and sparse local observations, offering a efficient solution for downstream decisions requiring precise local wind data.
Wire timeline
WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
Researchers have introduced WindINR, a novel latent-state implicit neural representation framework designed to provide fast, high-resolution wind estimates in complex terrains. Unlike traditional methods that generate dense forecast fields on fixed grids, WindINR allows for continuous queries at specific user-defined locations and heights. The system maps static terrain descriptors and low-resolution background fields to high-resolution wind states via a latent-conditioned decoder. A key innovation is the separation of reusable representation learning from sample-specific latent-state correction. During inference, network weights remain fixed while only the compact latent state is updated using sparse observations and their uncertainties. This approach significantly accelerates processing, achieving a 2.6x speedup over full-network fine-tuning in CPU benchmarks. Validated through controlled Observing System Simulation Experiments (OSSEs) in the Senja region, including UAV-aided scenarios, WindINR demonstrates improved accuracy and robustness. It serves as a practical interface between kilometer-scale background products and sparse local observations, offering a efficient solution for downstream decisions requiring precise local wind data.
cs.AI updates on arXiv.org