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

WindINR is a machine learning framework that enables fast, localized wind forecasting in complex terrain by using implicit neural representations to query wind conditions at specific user-defined locations rather than generating dense grid-based forecasts. The system achieves 2.6x speedup in corrections by updating only a compact latent state instead of retraining full networks, making it practical for real-time wind estimation applications.

WindINR addresses a fundamental challenge in meteorological forecasting: the gap between coarse-resolution background weather models and the need for precise wind estimates at specific locations in mountainous or complex terrain. Traditional approaches require either computationally expensive full-network fine-tuning or accepting lower accuracy from static models. This research separates the problem into reusable representation learning and sample-specific correction, allowing rapid inference-time updates without full model retraining.

The framework leverages implicit neural representations (INRs), a growing technique in AI where neural networks learn continuous mappings rather than discrete grid values. By conditioning on terrain descriptors, background forecasts, and query coordinates, WindINR provides continuously queryable wind fields—users can request estimates at arbitrary locations and heights, not just pre-computed grid points. The latent-state approach is particularly clever: it learns dataset-adaptive priors over corrections during training, then uses only sparse observations at inference time to update a small latent vector rather than millions of network parameters.

For wind energy operators, UAV-assisted surveying, or aviation safety applications, this represents meaningful operational improvement. The 2.6x speedup over traditional fine-tuning could enable real-time wind field corrections as observations arrive, critical for decision-making in rapidly changing conditions. The framework successfully handles robustness tests with random observations, suggesting practical deployment viability beyond controlled scenarios.

Future work likely involves testing on diverse terrain types, integrating multiple observation modalities, and scaling to operational forecast centers. The approach may inspire similar latent-correction methods across other physical prediction domains.

• →WindINR enables fast, localized wind forecasting by updating only latent representations rather than full neural networks, achieving 2.6x speedup
• →The framework supports continuous querying at arbitrary coordinates and heights, providing flexible wind estimates beyond fixed grid outputs
• →Separation of reusable representation learning from sample-specific correction allows rapid inference-time updates with sparse observations
• →System demonstrates practical robustness across controlled experiments including UAV scenarios and random-observation tests in complex terrain
• →Approach combines implicit neural representations with Bayesian latent-state optimization for operationally viable wind field correction