1961–1990 high‐resolution monthly precipitation climatologies for Italy

Abstract

High-resolution monthly precipitation climatologies for Italy are presented. They are based on 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.

1845–2016 gridded dataset of monthly precipitation over the upper Adda river basin: a comparison with runoff series

“…However, since a relevant fraction of stations has missing data in (about 40 % of stations in the study area have no more than 50 % of available data in this period), before computing their 1961-1990 monthly normals, series were subjected to the gap filling procedure presented in Crespi et al (2018). Station normals are then used both to transform the station records into anomaly records and as input data for the model applied to get the 30 arcsec monthly precipitation climatologies.…”

“…Station normals are then used both to transform the station records into anomaly records and as input data for the model applied to get the 30 arcsec monthly precipitation climatologies. Monthly climatological fields are obtained, as described in Crespi et al (2018), for a smoothed version of 30 arcsec resolution digital elevation model by means of a Local Weighted Linear Regression (LWLR) of precipitation versus elevation applied at each grid cell:…”

“…The main data providers were Regional Agency for Environmental Protection (ARPA) and Geological Monitoring Service (CMG) in Lombardy, MeteoTrentino for Autonomous Province of Trento, Autonomous Province of Bozen, MeteoSwiss and the former National Hydrographic Service for the most ancient data. Moreover, these data were integrated by those included in databases available from previous works, such as Brunetti et al (2006), Auer et al (2007), Brugnara et al (2012) and Crespi et al (2018). Data collection was focused both on maximising the spatial coverage over the study domain and closer areas, especially where strong orographic gradients occur, and on increasing the availability of long and homogeneous precipitation series over the entire considered period.…”

“…At this aim, precipitation data collected by the recent automatic weather station networks were recovered and integrated to those from the mechanical ones, and the most ancient data for some Italian records were digitised from hardcopy historical archives (Servizio Idrografico, 1920, 1957. Except for the series which were retrieved from already homogenised and quality-checked archives, all monthly precipitation series underwent a quality-check procedure (for details see Crespi et al, 2018) aiming at removing spurious records or digitisation oversights and at detecting the presence of relevant inhomogeneous periods. In addition, the longest records showing possible breaks during qualitycheck activity were considered for homogenisation by applying a procedure based on Craddock test (Craddock, 1979).…”

seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

“…OI combined with principal component analysis have been used to reconstruct historical climate datasets of precipitation in Switzerland (Schiemann et al, 2010b). In the paper by Crespi et al (2016), an interpolation approach based on local weighted linear regression (LWLR) has been compared with local regression Kriging (RK). This last method (RK) uses only geographical coordinates and elevation, while LWLR uses several additional geographical parameters, such as slope steepness, slope orientation and distance from the sea.…”