Observational dataset provides daily temperature measurements in north-central Italy, suggesting new insights into climate patterns.
A new observational gridded climatological dataset for daily minimum and maximum temperatures across north-central Italy is described. It spans from 1991 to present, is meant to be operationally updated and covers the territory with a regular grid of about 5 km resolution. The dataset is built starting from observational data collected and quality controlled by the local regional meteorological services. All data are first analyzed to identify those contributing to historical series, covering at least 80% of the total period and passing the SNH test. Interpolation uses in input only data from stations contributing to historical series or from stations representative of meteorological conditions over a large area. The study area is divided into sub-areas, each representing local climatological and geographical characteristics. Interpolation is carried out using a semi-local approach, applied on a daily basis and within each sub-area, using as geographical predictors elevation, urban fraction and water fraction. First, the elevation dependence is analyzed using a non-linear (piecewise) lapse rate model. Then a linear multi-regression model is used to analyze the dependence with respect to the other predictors. Finally residuals are interpolated using a modified Shepard inverse distance weighting algorithm. The dataset climatology is compared with that of ERA5-Land and E -OBS. Furthermore the dataset is described showing daily maps for specific weather events, monthly anomalies, climatological values and trends at annual and seasonal scale, paying particular attention to specific climate indices, derived from daily minimum and maximum temperature data, which can be considered proxies of the frequency of extreme events over the study area.
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Pavan et al. (2026) studied this question.
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