In this paper, we analyse three sites of great astronomical importance: Mt Graham, Paranal and La Silla. In recent years, with the development of new telescopes, the study of cloud cover is getting more and more important for the selection of new sites as well as for the development of existing telescopes. At the moment there is discussion on the techniques used to study climatic conditions. We have mainly two large data sets: satellite data and ground data. The two sets have advantages and disadvantages. We study in detail the various data available and we compare these data and analyse the correlations between them. In particular, we focus on the long-term statistics for the trends in climate change. We use two satellites: GOES (Geostationary Operational Environmental Satellite) and Aqua. In particular, we use the GOES camera data and MODIS (Moderate Resolution Imaging Spectroradiometer) data, which is a key instrument aboard the Aqua satellite. Finally, we use the heliograph ground data of the Columbine weather station to validate the two families of satellite data. The use of such data allows a mutual validation of the results, which allows the analysis to be extended to other sites. We obtained a mean night cloud cover for the 10 yr analysed (2003–2012) of 12 per cent at Paranal, 22 per cent at La Silla and 37 per cent at Mt Graham. We also get a punctual correlation of 96 per cent between the two satellites and of 92 per cent between the satellite and the heliograph data at Mt Graham for 2009.

A very wide-used instrument for the measurement of the Night Sky Brightness (NSB) is the Sky Quality Meter (SQM). One of its important issues is tracking NSB for long time and connecting its variations to changes in outdoor lighting. The stability of these radiometers is fundamental; variation on the instrument behaviour could be confused with changes of the sky brightness. The SQMs of the network of the Veneto Region (Italy) and the SQM installed at La Silla (Chile) are analysed by using the twilight method considering both sunset and dawn measurements, which allows to compensate for shifts in the SQM internal clock. The slope of the observed long-term trends ranges between 29 ± 5 and 86 ± 22 mmag_SQM arcsec⁻² year⁻¹. These high values require a correction of the measurements to continue to track NSB by those instruments. The correction is presented for an Italian site, for example: raw measures show an apparent trend towards darker sky (30 ± 5 mmag_SQM arcsec⁻² year⁻¹), after the correction a clear tendency towards a brighter polluted sky appears (−21 ± 8 mmag_SQM arcsec⁻² year⁻¹), in agreement with the estimated trend of the installed luminous flux of outdoor lighting for that area.

The most widely used radiance sensor for monitoring Night Sky Brightness (NSB) is the Sky Quality Meter (SQM), making its measurement stability fundamental. A method using the Sun as a calibrator was applied to analyse the quality of the measures recorded in the Veneto Region (Italy) and at La Silla (Chile). The analysis mainly revealed a tendency toward reductions in measured NSB due to both instrument ageing and atmospheric variations. This work compared the component due to instrumental ageing with the contribution of atmospheric conditions. The spectral responsivity of two SQMs working outdoors were analysed in a laboratory after several years of operation, revealing a significant decay, but not enough to justify the measured long-term trends. The contribution of atmospheric variations was studied through the analysis of solar irradiance at the ground, considering it as an indicator of air transparency, and values of the aerosol optical depth obtained from satellite measurements. The long-term trends measured by weather stations at different altitudes and conditions indicated an increase in solar irradiance in the Italian study sites. The comparison among the daily irradiance increase, the reduction in the aerosol optical depth, and the NSB measurements highlighted a darker sky for sites contaminated by light pollution (LP) and a brighter sky for sites not affected by LP, showing a significant and predominant role of atmospheric conditions in relation to NSB change. In the most significant case, the fraction of the variation in NSB explained by AOD changes exceeded 75%.

The Stave Churches (SCs) are one of the most iconic heritages in Norway, and only 28 medieval SCs have survived till our age in this country. They are built on wood with staves and wooden sleepers standing off the ground on foundation stones which have the primary purpose of guaranteeing structural support, ensuring integrity of the leveled foundation, and avoid ground water or rainwater penetration up to the valuable wooden structures. This paper aims to analyze the risk of Freeze-Thaw (F-T) decay on the foundation stones of the 28 SCs using an algorithm with a single climate input parameter i.e., the land surface temperature, extracted from the Global Land Data Assimilation System GLDAS which integrates satellite- and ground-based observational data products. The novel proposed algorithm estimates a climate-based index of F-T risk on foundation stones starting from the analysis of the variability of a 70-year (i.e., 1950 to 2020) land surface temperature datasets at these 28 locations. The outcome is further used to evaluate the average lifetime (half-life time) of foundation stones as well as the number of interventions necessary to guarantee their structural soundness thus providing a quantitative, predictive, and timely effective information to SC churches managers, and conservators on the climate change effect on foundation stones degradation at support of prioritization of maintenance interventions.

In this article, we describe a new algorithm for short-time satellite-based forecasts for seeing and photometric quality at the European Extremely Large Telescope (E-ELT) site (Armazones) and we analyse the correlation between the Paranal and Armazones sites. The algorithm uses data from the polar satellite Aqua's Moderate Resolution Imaging Spectroradiometer (MODIS) and the Geostationary Operational Environmental Satellite (GOES 13). We have analysed 13 years (2003–2015) of cloud coverage data from MODIS in order to obtain the cyclical perturbations through Fourier analysis. Then we have developed the forecast model using GOES 13 d data (2015). Monthly calibration atmospheric-layer temperature thresholds have been achieved through the daily temperature range detected by the satellite. The algorithm works through conditional probability. This allowed us to extrapolate the main frequency of the cloud-coverage perturbations, achieving three results: there are two major seasonal meteorological frequencies at Armazones and a short one of 14 days. This result allows us to improve the rate of the prediction algorithm by introducing a new threshold function. The correlation of 98 per cent found between the pixel above Paranal and the pixel above Armazones allows us to use the Paranal ground data to validate the prediction model. We analysed the 2015 data at Armazones and reached a correlation of 97 per cent for the short-time photometry and seeing quality forecast.