Title: High-resolution temperature climatology for Italy: interpolation method intercomparison
Citation: JOURNAL OF CLIMATOLOGY vol. 34 no. 4 p. 1278-1296
Publication Year: 2014
JRC N°: JRC83585
ISSN: 0196-1748
URI: http://onlinelibrary.wiley.com/doi/10.1002/joc.3764/abstract
DOI: 10.1002/joc.3764
Type: Articles in periodicals and books
Abstract: 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)
JRC Directorate:Sustainable Resources

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