1961–1990 monthly high-resolution solar radiation climatologies for Italy

Spinoni, Jonathan; Brunetti, Michele; Maugeri, Maurizio; Simolo, C.

doi:10.5194/asr-8-19-2012

Cited by 7 publications

(8 citation statements)

References 25 publications

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“…In order to get a first estimation of global radiation trends, we estimated global irradiance data from the corresponding SD records by means of Ångström‐Prescott formula [ Ångström , ; Prescott , ]. Specifically, we used the coefficients reported by Spinoni et al [], which give a preliminary estimation of this formula for Italy. The resulting records were then transformed into anomalies with respect to the monthly averages of the 1984–2013 period and were used to get northern and southern Italy global radiation (expressed as mean irradiance, in W m −2 ) anomaly records.…”

Section: Resultsmentioning

confidence: 99%

Sunshine duration variability and trends in Italy from homogenized instrumental time series (1936–2013)

et al. 2015

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A data set of quality checked daily sunshine duration measurements was collected from 104Italian sites over the 1936 to 2013 period. Monthly mean values were homogenized, projected onto a grid, and subjected to principle component analysis, which identified two significantly different regions: North and South. Sunshine duration temporal evolution is presented, and possible reasons for differences between the two regions are discussed in the light of a comparison with the trends found in observations of total cloud cover and with results from two neighboring regions: the Alps and Spain. In addition, trends for irradiance records, estimated from sunshine duration records using the Ångström-Prescott formula, are presented too. The major feature of the trends, an increase in sunshine duration from the mid-1980s, was common to both northern and southern Italy; the decrease in the preceding 30 year period was not, as northern Italy had a lower rate of decrease than southern Italy. The few records available during the earliest period of the data set indicate that sunshine duration in Italy increased from the mid-1930s to the mid-1950s. The further steps needed to identify and quantify the mechanisms giving rise to the observed trends and to the reported regional differences in dimming and brightening are outlined.

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Section: Resultsmentioning

confidence: 99%

Sunshine duration variability and trends in Italy from homogenized instrumental time series (1936–2013)

et al. 2015

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“…Specifically, we calculated the absolute series for each grid point by multiplying each seasonal/annual anomaly series obtained under all-sky conditions by the corresponding seasonal/annual normals, and then we calculated the seasonal and annual regional records by averaging all corresponding grid point absolute series. The seasonal/annual normals were calculated by averaging the corresponding monthly normals obtained using the same data and the procedure explained by Spinoni et al (2012). In particular, the monthly normals were obtained starting from a database of SD normals for the Italian territory referring to flat and non-shaded sites.…”

Section: North Clear Sky Estimated 1959-2013mentioning

confidence: 99%

Detection of dimming/brightening in Italy from homogenized all-sky and clear-sky surface solar radiation records and underlying causes (1959–2013)

Manara

Brunetti

Celozzi³

et al. 2016

Atmos. Chem. Phys.

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Abstract. A dataset of 54 daily Italian downward surface solar radiation (SSR) records has been set up collecting data for the 1959-2013 period. Special emphasis is given to the quality control and the homogenization of the records in order to ensure the reliability of the resulting trends. This step has been shown as necessary due to the large differences obtained between the raw and homogenized dataset, especially during the first decades of the study period. In addition, SSR series under clear-sky conditions were obtained considering only the cloudless days from corresponding ground-based cloudiness observations. Subsequently, records were interpolated onto a regular grid and clustered into two regions, northern and southern Italy, which were averaged in order to get all-sky and clear-sky regional SSR records. Their temporal evolution is presented, and possible reasons for differences between all-sky and clear-sky conditions and between the two regions are discussed in order to determine to what extent SSR variability depends on aerosols or clouds. Specifically, the all-sky SSR records show a decrease until the mid1980s (dimming period), and a following increase until the end of the series (brightening period) even though strength and persistence of tendencies are not the same in all seasons. Clear-sky records present stronger tendencies than all-sky records during the dimming period in all seasons and during the brightening period in winter and autumn. This suggests that, under all-sky conditions, the variations caused by the increase/decrease in the aerosol content have been partially masked by cloud cover variations, especially during the dimming period. Under clear sky the observed dimming is stronger in the south than in the north. This peculiarity could be a consequence of a significant contribution of mineral dust variations to the SSR variability.

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“…It represents an extended and enhanced version of datasets presented in previous works (Spinoni, 2010;Brunetti et al, 2012) and it is in continuous progress. Information on data sources considered for the present work and the number of available series is listed in Table 1.…”

Section: Datamentioning

confidence: 99%

“…The dataset used to produce the 1961–1990 precipitation climatologies is the result of more than 10 years of activities carried out at the Institute of Atmospheric Sciences and Climate of the Italian National Research Council (ISAC‐CNR) and at the Department of Physics at Milan University to obtain the largest possible amount of precipitation records and metadata for Italy and the surrounding areas. It represents an extended and enhanced version of datasets presented in previous works (Spinoni, ; Brunetti et al, ) and it is in continuous progress. Information on data sources considered for the present work and the number of available series is listed in Table .…”

Section: Datamentioning

confidence: 99%

“…Very few climatological maps are available at a national scale and they are mostly in a non-digitized form, such as the hand-drawn precipitation maps for 1921-1950 produced by the Italian Hydrographical Service (Servizio Idrografico (SI), 1957). More recently, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale) provided monthly 5 km × 5 km precipitation climatologies for 1951-1980(ISPRA, 2014 and CREA-CMA (Research unit for Climatology and Meteorology applied to Agriculture) published national monthly climatologies for 1961-1990, 1971and 1981-2010(Esposito et al, 2015. However, in both cases, the rather low density of stations was not suitable to completely account for the complexity of the Italian physiography.…”

Section: Introductionmentioning

confidence: 99%

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1961–1990 high‐resolution monthly precipitation climatologies for Italy

Crespi

Brunetti

Lentini

et al. 2017

Intl Journal of Climatology

Self Cite

105

101

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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.

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1961–1990 monthly high-resolution solar radiation climatologies for Italy

Cited by 7 publications

References 25 publications

Sunshine duration variability and trends in Italy from homogenized instrumental time series (1936–2013)

Sunshine duration variability and trends in Italy from homogenized instrumental time series (1936–2013)

Detection of dimming/brightening in Italy from homogenized all-sky and clear-sky surface solar radiation records and underlying causes (1959–2013)

1961–1990 high‐resolution monthly precipitation climatologies for Italy

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