Theoretical and Applied Climatology

, Volume 136, Issue 3–4, pp 849–862 | Cite as

1990–2016 surface solar radiation variability and trend over the Piedmont region (northwest Italy)

  • Veronica ManaraEmail author
  • Manuela Bassi
  • Michele Brunetti
  • Barbara Cagnazzi
  • Maurizio Maugeri
Original Paper


A new surface solar radiation database of 74 daily series is set up for the Piedmont region (northwest Italy) for the 1990–2016 period. All the series are subjected to a detailed quality control, homogenization and gap-filling procedure and are transformed into relative annual/seasonal anomaly series. Finally, a gridded version (0.5°×0.5°) of the database is generated. The resulting series show an increasing tendency of about + 2.5% per decade at annual scale, with strongest trend in autumn (+ 4% per decade). The only exception is winter, showing a negative but not significant trend. Considering the plain and mountain mean series, the trends are more intense for low than for high elevations with a negative vertical gradient of about − 0.03% per decade per 100 m at annual scale and values up to − 0.07% per decade per 100 m in spring. Focusing on clear days only (selected by CM SAF ClOud fractional cover dataset from METeosat first and second generation—Edition 1 satellite data over the 1991–2015 period), trend significance strongly increases and both low and high elevation records exhibit a positive trend in all seasons. However, the trends result slightly lower than for all-sky days (with the only exception of winter). The differences observed under clear-sky conditions between low and high elevations are more pronounced in winter, where the trend shows a negative vertical gradient of about − 0.1% per decade every 100 m. Overall, this paper shows how a high station density allows performing a more detailed quality control thanks to the higher performances in detecting the inhomogeneities with higher data availability and capturing regional peculiarities otherwise impossible to observe.



This work was supported by the Special Project HR-CIMA within the frame of the Project of National Interest NextData.


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Atmospheric Sciences and Climate, ISAC-CNRBolognaItaly
  2. 2.Department of Forecasting SystemsRegional Agency for Environmental Protection of PiedmontTurinItaly
  3. 3.Department of Environmental Science and PolicyUniversità degli Studi di MilanoMilanItaly

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