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

Many downstream decisions in complex terrain require fast wind estimates at a small number of user-specified locations and heights for a given forecast valid time, rather than another dense forecast field on a fixed grid. We present WindINR, a latent-state implicit neural representation framework for continuous high-resolution local wind query and sparse-observation correction. WindINR maps static terrain descriptors, a low-resolution background field, and continuous query coordinates to a high-resolution wind state through a latent-conditioned decoder. To enable rapid inference-time correction, WindINR separates reusable representation learning from sample-specific latent-state correction. During training, a privileged encoder infers a reference latent state from high-resolution supervision, a deployable latent predictor estimates an initial latent state from inference-time inputs alone, and their discrepancies are summarized into a dataset-adaptive Gaussian prior over latent corrections. At inference time, within the WindINR module, network weights remain fixed and only the latent state is updated by minimizing a regularized correction objective using sparse observations and their uncertainty. We train WindINR on the large-scale FuXi-CFD dataset spanning diverse terrain and evaluate it across complementary observation settings, including controlled OSSEs, simulated helicopter trajectories, and real UAV measurements over Tromsø. Across these settings, assimilating sparse observations consistently improves local wind-field predictions, with WindINR outperforming several baselines, including WindSeer. Moreover, by performing online correction only in a low-dimensional latent space, WindINR achieves a 14$\times$ speedup over full-network fine-tuning. The resulting representation remains continuously queryable at arbitrary spatial coordinates, making WindINR a promising and efficient correction framework that provides a practical interface between kilometer-scale background products, sparse local observations, and wind queries in complex terrain.