  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.
