High-resolution temperature climatology for Italy: interpolation method intercomparison
Article first published online: 23 JUL 2013
© 2013 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
International Journal of Climatology
Volume 34, Issue 4, pages 1278–1296, March 2014
How to Cite
Brunetti, M., Maugeri, M., Nanni, T., Simolo, C. and Spinoni, J. (2014), High-resolution temperature climatology for Italy: interpolation method intercomparison. Int. J. Climatol., 34: 1278–1296. doi: 10.1002/joc.3764
- Issue published online: 17 MAR 2014
- Article first published online: 23 JUL 2013
- Manuscript Accepted: 10 MAY 2013
- Manuscript Revised: 9 MAY 2013
- Manuscript Received: 15 JAN 2013
- high-resolution climatology;
- mean temperature;
- interpolation methods
High-resolution monthly temperature climatologies for Italy are presented. They are based on a dense and quality-controlled observational dataset which includes 1484 stations and on three distinct approaches: multi-linear regression with local improvements (MLRLI), an enhanced version of the model recently used for the Greater Alpine Region, regression kriging (RK), widely used in literature and, lastly, local weighted linear regression (LWLR) of temperature versus elevation, which may be considered more suitable for the complex orography characterizing the Italian territory.
Dataset and methods used both to check the station records and to get the 1961–1990 normals used for the climatologies are discussed. Advantages and shortcomings of the three approaches are investigated and the results are compared.
All three approaches lead to quite reasonable models of station temperature normals, with lowest errors in spring and autumn and highest errors in winter. The LWLR approach shows slightly better performances than the other two, with monthly leave-one-out estimated root mean square errors ranging from 0.74 °C (April and May) to 1.03 °C (December). Further evidence in its favour is the greater reliability of local approach in modelling the behaviour of the temperature-elevation relationship in Italy's complex territory.
The comparison of the different climatologies is a very effective tool to understand the robustness of each approach. Moreover, the first two methods (MLRLI and RK) turn out to be important to tune the third one (LWLR), as they help not only to understand the relationship between temperature normals and some important physiographical variables (MLRLI) but also to study the decrease of station normals covariance with distance (RK).