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 paper 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 spatio-temporal 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 to 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. are comparable with popular reanalysis products, but unlike these numerical weather models our approach is available in real-time and offers improved uncertainty quantification.

翻译：精确的高分辨率时空风速数据对于评估特定地点的风能潜力至关重要。在实时风速预测中，统计模型通常依赖来自官方气象站的高质量（近）实时数据以提高预报精度。个人气象站（PWS）相比官方站点提供了额外的实时数据源和更广阔的空间覆盖范围。然而，这些数据缺乏严格的质量控制，可能存在偏差或测量误差。本文提出一个框架，通过两阶段方法将PWS数据整合到针对已验证官方气象站数据的统计模型中。首先，利用再分析数据对PWS风速数据进行偏差校正。其次，我们构建了一个贝叶斯层次时空模型，该模型能够处理PWS数据中变化的测量误差。这使得目标区域内的风速预测成为可能，并特别有利于改善官方监测站点稀疏区域的预测效果。研究结果表明，与仅使用气象站数据相比，纳入偏差校正的PWS数据可提高预测精度，所有站点的预测误差平均降低5%。该结果与主流再分析产品性能相当，但不同于这些数值天气预报模型，我们的方法能够实现实时预测并提供更优的不确定性量化。