Understanding the dynamics of climate variables is paramount for numerous sectors, like energy and environmental monitoring. This study focuses on the critical need for a precise mapping of environmental variables for national or regional monitoring networks, a task notably challenging when dealing with skewed data. To address this issue, we propose a novel data fusion approach, the \textit{warped multifidelity Gaussian process} (WMFGP). The method performs prediction using multiple time-series, accommodating varying reliability and resolutions and effectively handling skewness. In an extended simulation experiment the benefits and the limitations of the methods are explored, while as a case study, we focused on the wind speed monitored by the network of ARPA Lombardia, one of the regional environmental agencies operting in Italy. ARPA grapples with data gaps, and due to the connection between wind speed and air quality, it struggles with an effective air quality management. We illustrate the efficacy of our approach in filling the wind speed data gaps through two extensive simulation experiments. The case study provides more informative wind speed predictions crucial for predicting air pollutant concentrations, enhancing network maintenance, and advancing understanding of relevant meteorological and climatic phenomena.

翻译：理解气候变量的动态变化对于能源和环境监测等诸多领域至关重要。本研究聚焦于国家或区域监测网络对环境变量进行精确测绘的迫切需求，这一任务在处理偏态数据时尤为困难。为解决此问题，我们提出了一种新颖的数据融合方法——\textit{扭曲多保真度高斯过程}（WMFGP）。该方法利用多个时间序列进行预测，能够适应不同的可靠性与分辨率，并有效处理数据偏态。通过扩展的模拟实验，我们探讨了该方法的优势与局限性；同时以意大利区域环境机构ARPA伦巴第监测网络的风速数据作为案例研究对象。ARPA面临数据缺失问题，且由于风速与空气质量间的关联性，其空气质量管理工作面临挑战。我们通过两项大型模拟实验，展示了所提方法在填补风速数据缺失方面的有效性。该案例研究提供了信息更丰富的风速预测，这对于预测空气污染物浓度、加强监测网络维护以及深化对相关气象与气候现象的理解具有关键意义。