Mountains create complex meso- and microclimatic patterns that challenge our ability to model and predict environmental changes. To address this challenge, we present a comprehensive dataset from a six-year (2017-2022) continuous monitoring effort from the LTSER site IT25 Val Mazia/Matschertal (Italy). From a network of 24 climate stations distributed across the valley, we selected six comprehensively equipped stations along an elevation gradient (983-2705m) to capture climatic heterogeneity. Our monitoring infrastructure combines fifteen distinct climatic variables through high-precision sensors measuring air temperature, relative humidity, wind patterns, solar radiation, precipitation, snow height, and soil properties at multiple depths. Analysis of this dataset revealed distinct seasonal temperature lapse rates varying from -6.7°C/1000m in spring to -4.7°C/1000m in winter, with lower winter rates attributed to thermal inversions. Snow cover showed a strong non-linear elevation gradient, with maximum heights ranging from 30cm at 1000m to over 2m at 2700m, significantly influencing soil temperature dynamics. To ensure data quality and accessibility, we developed a data management system that employs an integrated workflow with automated quality control procedures, real-time data transmission, and standardized processing pipelines, achieving 92% dataset completeness. Our custom-developed monitoring system performs automated quality checks and generates alerts for potential sensor malfunctions. This approach addresses key challenges in environmental data collection, including sensor calibration across elevation gradients, automated error detection, and harmonization of heterogeneous data streams. Following FAIR principles, the meteorological time series are accessible through both the Matsch|Mazia Data Browser and the Pangaea repository with a persistent DOI (https://doi.org/10.1594/PANGAEA.964700). The complete source code of our monitoring system and data quality control procedures is freely available on GitLab and archived on Zenodo. The resulting time series support various applications, from validating regional climate models to supporting hydrological modeling and remote sensing products in mountain environments.

Harald Crepaz

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