EEAR-Clim: A high density stations-based dataset of daily meteorological data for the Extended European Alpine Region

Several observational products of key climate variables have been widely used to evaluate the ongoing effects of climate change in the European Alps, one of the most vulnerable and sensitive regions to the continuous warming of climate. However, the limited spatial coverage in most observational products and quality issues of data may strongly impact climate and hydrological studies results in terms of reliability, accuracy and precision. Although the collection and management of meteorological data for the whole Alpine area is a challenging task due to strong fragmentation and diversity of data sources, further efforts need to be dedicated to produce new harmonised, high-quality and high-resolution products able to permit a more robust assessment of climate change and its impacts.  

Here, we present a new observational dataset gathering in-situ daily measurements of key climate variables provided by a wide range of meteorological and hydrological services within the Extended European Alpine Region (EEAR). The dataset, originally including air temperature and precipitation time series recorded up to 2020, in its newest version has been expanded to incorporate more recent observations (2021-2024), additional historical records, and data of other climate variables such as relative humidity, wind speed and direction, global radiation, snow depth and surface pressure. The updated observational network includes over 10000 in-situ weather stations, providing extensive and consistent coverage both in space and elevation. 

Data collected are screened applying a comprehensive and deep quality control procedure for the identification of the most important critical data issues. Time series were thoroughly checked for internal and temporal consistency, and spatial coherence, facing the problem of outlier removal. Data homogeneity was assessed by a cross-comparison of break points identified with Climatol, Acmant and RH Test methods. The resulting inhomogeneous periods were adjusted through the quantile matching techniques.

A quantitative assessment of the present dataset highlights its added value in addressing key issues affecting state-of-the-art observational products, providing a powerful tool for a better understanding of Alpine climate changes and improving the reliability of future climate scenarios.