WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain

Many decisions in complex terrain require fast wind estimates at specific locations and heights rather than dense forecasts on fixed grids. We present WindINR, a latent-state implicit neural representation framework for continuous high-resolution local wind queries and correction using sparse observations. A latent-conditioned decoder maps terrain descriptors, a low-resolution background field, and continuous query coordinates to local wind estimates. WindINR separates reusable representation learning from sample-specific latent correction. During training, a privileged encoder infers reference latent states from high-resolution supervision, while a deployable predictor estimates initial states from inference-time inputs. Their discrepancies define a dataset-adaptive Gaussian prior over latent corrections. At inference time, network weights remain fixed, and only the latent state is updated through a regularized objective incorporating sparse observations and their uncertainty. We train WindINR on the large-scale FuXi-CFD dataset and evaluate it using controlled observing system simulation experiments, simulated helicopter trajectories, and real UAV measurements over Tromsø. Across these settings, sparse-observation assimilation consistently improves local wind predictions, with WindINR outperforming several baselines, including WindSeer. Updating only a low-dimensional latent state yields a 14-fold speedup over full-network fine-tuning while preserving continuous queries at arbitrary spatial coordinates. WindINR thus provides an efficient interface between kilometer-scale background products, sparse local observations, and local wind queries in complex terrain.