Abstract
Abstract. A high-resolution gridded dataset of daily mean temperature and precipitation series spanning the period 1980–2018 was built for Trentino-South Tyrol, a mountainous region in north-eastern Italy, starting from an archive of observation series from more than 200 meteorological stations and covering the regional domain and surrounding countries. The original station data underwent a processing chain including quality and consistency checks, homogeneity tests, with the homogenization of the most relevant breaks in the series, and a filling procedure of daily gaps aiming at maximizing the data availability. Using the processed database, an anomaly-based interpolation scheme was applied to project the daily station observations of mean temperature and precipitation onto a regular grid of 250 m × 250 m resolution. The accuracy of the resulting dataset was evaluated by leave-one-out station cross-validation. Averaged over all sites, interpolated daily temperature and precipitation show no bias, with a mean absolute error (MAE) of about 1.5 ∘C and 1.1 mm and a mean correlation of 0.97 and 0.91, respectively. The obtained daily fields were used to discuss the spatial representation of selected past events and the distribution of the main climatological features over the region, which shows the role of the mountainous terrain in defining the temperature and precipitation gradients. In addition, the suitability of the dataset to be combined with other high-resolution products was evaluated through a comparison of the gridded observations with snow-cover maps from remote sensing observations. The presented dataset provides an accurate insight into the spatio-temporal distribution of temperature and precipitation over the mountainous terrain of Trentino-South Tyrol and a valuable support for local and regional applications of climate variability and change. The dataset is publicly available at https://doi.org/10.1594/PANGAEA.924502 (Crespi et al., 2020).
Highlights
High-resolution gridded datasets of in situ climate observations are of increasing relevance for the studies on climate and its variability and for many applications, such as natural resource management, adaptation planning, modelling and risk assessment in a wide range of fields including hydrology, agriculture and energy (Haylock et al, 2008; Hofstra et al, 2008)
The punctual conditions at single station sites, especially the daily precipitation peaks, are smoothed after the spatialization so that the fine resolution of the daily grids does not correspond to the scales being effectively resolved, which are limited by the horizontal spacing of the station network
The reconstruction accuracy was computed by comparing the simulations and observations in terms of mean error (BIAS), mean absolute error (MAE), root mean square error (RMSE) and correlation
Summary
High-resolution gridded datasets of in situ climate observations are of increasing relevance for the studies on climate and its variability and for many applications, such as natural resource management, adaptation planning, modelling and risk assessment in a wide range of fields including hydrology, agriculture and energy (Haylock et al, 2008; Hofstra et al, 2008). For daily precipitation, the interpolation with a reference field and anomalies was proved to be less prone to errors than the direct interpolation of absolute values, such as systematic underestimations in high-mountain regions due to the prevalence of stations located in the low valleys (Isotta et al, 2014; Crespi et al, 2021) This concept was applied in a relevant number of studies (see, for example, Haylock et al, 2008; Brunetti et al, 2012; Chimani et al, 2013; Hiebl and Frei, 2018; Longman et al, 2019).
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