A high density observational dataset of daily meteorological data for the Extended Alpine Region

Several observational products of key climate variables have been widely used to evaluate the extent of the ongoing effects of climate change in the Alpine area, one of the most vulnerable and sensitive regions to the continuous warming of climate. However, a 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. Even though 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 variety of meteorological and hydrological services within the extended Alpine region. Data collected are recorded up to 2020, with a temporal extent of about 200 years in the longest time series. The observational network consists of about 10000 in-situ weather stations, resulting in an extended and homogeneous coverage, both in space and elevation. Data collected are screened, inspecting the presence of most important critical issues in terms of data quality. A deep quality control of collected time series has been performed by checking internal, temporal and spatial consistency of time series, exploiting the problem of outlier removal. Data homogeneity was assessed by a cross-comparison of results obtained with Climatol, Acmant and RH Test methods. Quantile matching was used to adjust inhomogeneous periods in time series. The present dataset addresses the most important issues affecting state-of-the-art observational products and it represents a powerful tool for better understanding Alpine climate changes over the last decades and improving the reliability of future scenarios.