High-Resolution Atlas of Daily Maximum and Minimum Air Temperatures in Sicily

Air temperature data are widely used in climatological and hydrological applications. In a data-rich era (e.g., satellites and reanalysis datasets), ground station data still provide a much more accurate estimate of this variable. However, in situ measurements are only representative of a single point in space, and instruments are often replaced or relocated, creating spatial and/or temporal discontinuities that prevent their direct use, and for this reason it is increasingly difficult to obtain long-term observational series.

This problem is evident in the island of Sicily, Italy, where two different air temperature measurement networks exist: the first, provided by the Osservatorio delle Acque – Agenzia Regionale per i Rifiuti e le Acque (OA-ARRA), covers the period from 1980 to 2012, while the second, provided by the Servizio Informativo Agrometeorologico Siciliano (SIAS), has continuously recorded data since 2002. From these two measurement networks, which overlap for a 10-year period, we tested and validated a methodology based on the spatial analysis techniques of interpolation of daily maximum and minimum temperature data. Specifically, we combined Ordinary Kriging with the Near Surface Lapse Rate (NSLR-OK) to account for the altitude effect in the interpolation process.

The datasets provided by the aforementioned networks were used to identify some criticalities due to the different measurement instruments used, by applying a methodology aimed at reducing biases between the two datasets. First, we interpolated the daily maximum and minimum temperature datasets from the OA-ARRA on the SIAS stations for the overlap period. The results of the interpolation procedure were compared with the data recorded at the SIAS stations returning accurate results. We then extended the interpolation from 1980 onwards using a high spatial resolution grid (2x2 km) which allowed us to create the T-Atlas for Sicily, which is a useful tool for detecting possible signals of climate change and their potential spatial patterns across the island.