1961–1990 high‐resolution monthly precipitation climatologies for Italy. (26th July 2017)

• Record Type: Journal Article
• Authors: Crespi, A.; Brunetti, M.; Lentini, G.; Maugeri, M.
• Is Part Of: International journal of climatology. Volume 38:Number 2(2018)
• Page Start: 878
• Page End: 895
• Publication Date: 2017-07-26
• Subjects: high‐resolution climatology -- precipitation -- Italy -- interpolation methods; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.5217
• Languages: English
• ISSNs: 0899-8418

Abstract

ABSTRACT: High‐resolution monthly precipitation climatologies for Italy are presented. They are based on 1961–1990 precipitation normals obtained from a quality‐controlled dataset of 6134 stations covering the Italian territory and part of the Northern neighbouring regions. The climatologies are computed by means of two interpolation methods modelling the precipitation‐elevation relationship at a local level, more precisely a local weighted linear regression (LWLR) and a local regression kriging (RK) are performed. For both methods, local optimisations are also applied in order to improve model performance. Model results are compared with those provided by two other widely used interpolation methods which do not consider elevation in modelling precipitation distribution: ordinary kriging and inverse distance weighting. Even though all the four models produce quite reasonable results, LWLR and RK show the best agreement with the observed station normals and leave‐one‐out‐estimated mean absolute errors ranging from 5.1 mm (July) to 11 mm (November) for both models. Their better performances are even clearer when specific clusters of stations (e.g. high‐elevation sites) are considered. Even though LWLR and RK provide very similar results both at station and at grid point level, they show some peculiar features. In particular, LWLR is found to have a better extrapolation ability at high‐elevation sites when data density is high enough, while RK is more robust in performing extrapolation over areas with complex orography and scarce data coverage, where LWLR may provide unrealistic precipitation values. However, by means of prediction intervals, LWLR provides a more straightforward approach to quantify the model uncertainty at any point of the study domain, which helps to identify the areas mainly affected by model instability. LWLR and RK high‐resolution climatologies exhibit a very heterogeneous and seasonal‐dependent precipitation distribution throughout the domain and allow to identify the main climatic zones of Italy. Abstract : The paper presents high‐resolution monthly precipitation climatologies for Italy. They are computed by means of two interpolation methods modelling the precipitation‐elevation relationship at a local level: local weighted linear regression (LWLR) and regression kriging (RK). The monthly errors turn out to range from 5 mm to 11 mm for both models. LWLR shows a better extrapolation ability at high‐elevated sites, while RK is preferable over areas with complex orography and scarce data coverage, where LWLR may provide unrealistic precipitation values.