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.

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