# Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations

arXiv: https://arxiv.org/abs/2607.25298 · HTML: https://mlsanity.com/paper/2607.25298/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [Inductive Graph Neural Networks for Spatiotemporal Kriging](https://mlsanity.com/paper/2006.07527/) (2020)
- [Kriging Convolutional Networks](https://mlsanity.com/paper/2306.09463/) (2023)
- [DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction](https://mlsanity.com/paper/2007.11972/) (2020)
- [Deep Graph Convolutional Networks for Wind Speed Prediction](https://mlsanity.com/paper/2101.10041/) (2021)
- [Road Network Representation Learning with the Third Law of Geography](https://mlsanity.com/paper/2406.04038/) (2024)
- [Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures](https://mlsanity.com/paper/2208.13585/) (2022)
- [Reconstructing Historical Climate Fields With Deep Learning](https://mlsanity.com/paper/2311.18348/) (2023)
- [Rooftop Wind Field Reconstruction Using Sparse Sensors: From Deterministic to Generative Learning Methods](https://mlsanity.com/paper/2603.13077/) (2026)

## Abstract (arXiv)

The high proportion of wind power connected to the grid places higher demands on fine-grained knowledge of regional wind fields. Since the wind information directly obtainable in actual operations is mostly sparse, discrete, and irregularly distributed local observations, it is difficult to directly meet the needs of tasks such as wind power regulation, wind resource assessment, and low-altitude environmental perception of continuous regional wind fields. Therefore, we propose Zhinv, an end-to-end reconstruction framework that directly weaves sparse and irregular observations into a fine-grid wind field at hub-height. Experiments in Northeast China, Europe, and Southeast Asia demonstrate that Zhinv can accurately, robustly, and efficiently reconstruct fine-grid wind fields from sparse observations, reducing the error by about 66% compared with Kriging. With local wind-power observations as input, Zhinv enables wind power centers to bypass NWP and complex assimilation processes, supporting direct and real-time wind resource assessment from locally available data.