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 of Temperature versus Elevation
(LWLR), 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 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)
BRUNETTI Michele;
MAUGERI Maurizio;
NANNI Teresa;
SIMOLO Claudia;
SPINONI Jonathan;
2014-09-18
JOHN WILEY & SONS LTD.
JRC83585
0196-1748,
http://onlinelibrary.wiley.com/doi/10.1002/joc.3764/abstract,
https://publications.jrc.ec.europa.eu/repository/handle/JRC83585,
10.1002/joc.3764,
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