Exploring the use of the Basque Country AWS network for climate monitoring

Effective climate studies and monitoring heavily depend on access to extensive, high-resolution, and long-term instrumental climate data. However, our ability to understand, detect, predict, and address climate variability and change at finer spatial scales—beyond global ones—is currently limited by the scarcity and accessibility of high-quality, long-term climate records and datasets. In this context, it is important to highlight the ongoing transition from manual observation networks to fully automated systems, with such systems now predominating in much of Europe, including the Basque Country.

In this paper, we describe efforts undertaken to assess the conditions and circumstances under which automatic stations deployed across our region can be used for climate monitoring. Specifically, we focus on the automatic measurement network operated by the Basque Meteorological Agency (Euskalmet), which currently includes around 130 automatic weather stations (AWS) providing real-time data on various variables across the region at 10-minute intervals. While the network is primarily oriented towards real-time surveillance of severe weather events, including flooding, measurements are far from ideal from the climatic perspective.

Nonetheless, some stations have been operational since the early 1990s, providing relatively long data series spanning 20 to 30 years at various locations, which could offer valuable local climate data. However, it is well-known that many factors influence the usability and value of such data—not only the length and quality of the data, but also its overall representativeness, which can be affected by local environmental conditions.

Our ultimate goal is to provide insights into the use of AWS data from the analyzed network for local-scale climate monitoring. To achieve this, we identify stations that most closely align with WMO climate observation criteria, select those with the best possible data quality, implement procedures to construct essential climate data series—including data curation and homogenization—generate derived climate indicators for anomaly and trend analysis, and conduct validation studies to draw conclusions.