Enhancing the Accuracy of Spatio‐Temporal Models for Wind Speed Prediction by Incorporating Bias‐Corrected Crowdsourced Data

Accurate high‐resolution spatial and temporal wind speed data is critical for estimating the wind energy potential of a location. For real‐time wind speed prediction, statistical models typically depend on high‐quality (near) real‐time data from official meteorological stations to improve forecasting accuracy. Personal weather stations (PWS) offer an additional source of real‐time data and broader spatial coverage than official stations. However, they are not subject to rigorous quality control and may exhibit bias or measurement errors. This article presents a framework for incorporating PWS data into statistical models for validated official meteorological station data via a two‐stage approach. First, bias correction is performed on PWS wind speed data using reanalysis data. Second, we implement a Bayesian hierarchical spatiotemporal model that accounts for varying measurement error in the PWS data. This enables wind speed prediction across a target area, and is particularly beneficial for improving predictions in regions sparse in official monitoring stations. Our results show that including bias‐corrected PWS data improves prediction accuracy compared with using meteorological station data alone, with a 5% reduction in prediction error on average across all sites. The results are comparable with popular reanalysis products, but unlike these numerical weather models our approach is available in real‐time and offers improved uncertainty quantification.