Exploring the use of automatic weather stations for climate monitoring: The Basque Country case study

Effective climatic studies and climate monitoring rely heavily on access to extensive, high-resolution, and long-term instrumental climate data. However, our ability to comprehend, detect, predict, and address climate variability and change at finer spatial scales than global ones is currently hindered by the limited availability and accessibility of long-term, high-quality climate records and datasets. In this context, it is important to note the ongoing transition from manual observation networks to automatic systems, with fully automatic observation networks predominating in much of Europe, including the Basque Country.

In this contribution, we present some actions taken with the aim of establishing under what conditions and circumstances automatic stations deployed throughout our territory can be used in the context of climate monitoring. Particularly, we focus on the automatic measurement network operated by the Basque Meteorological Agency (Euskalmet), which currently consists of approximately 130 automatic weather stations (AWS) delivering real-time data on different variables across the territory 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.

Nevertheless, some stations have been operational since the early 1990s, providing relatively long data series spanning 20-30 years at various locations, potentially offering valuable local climate data. However, it is well known that many factors affects usability and value of such data not only the length of data availability or quality but also its overall representativeness, which may be compromised by environmental conditions at specific locations

Our final goal is to provide insights regarding the use of AWS data from the analyzed network for climate monitoring at local scale. To achieve this, we identify stations closest to WMO climate observation criteria, select those with the best data quality characteristics possible, implement procedures for constructing essential climatic data series, including data curation and homogenization, generate derived climatic indicators for anomaly and trend comparison purposes, and conduct validation studies to draw conclusions.