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

• URL: http://arxiv.org/abs/2605.09511v1
• Date: Sun, 10 May 2026 12:44:38 GMT
• Title: WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
• Abstract summary: WindINR is a latent-state implicit neural representation framework for continuous high-resolution local wind query and sparse-observation correction.<n>WindINR maps static terrain descriptors, a low-resolution background field, and continuous query coordinates to a high-resolution wind state.<n>WindINR improves local high-resolution wind estimates by updating only a compact latent state rather than the full network.
• Score: 47.92351540935845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. In controlled OSSEs over the Senja region, including a UAV-aided approach scenario and random-observation robustness tests, WindINR improves local high-resolution wind estimates by updating only a compact latent state rather than the full network. The corrected representation remains continuously queryable at arbitrary coordinates and, in our CPU benchmark, yields about a $2.6\times$ online-correction speedup over full-network fine-tuning, suggesting a practical interface between kilometer-scale background products, sparse local observations, and wind queries in complex terrain.

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