The UV Index was established more than 20 years ago as a tool for sun protection and health care. Shortly after its introduction, UV Index monitoring started in several countries either by newly acquired instruments or by converting measurements from existing instruments into the UV Index. The number of stations and networks has increased over the years. Currently, 160 stations in 25 European countries deliver online values to the public via the Internet. In this paper an overview of these UV Index monitoring sites in Europe is given. The overview includes instruments as well as quality assurance and quality control procedures. Furthermore, some examples are given about how UV Index values are presented to the public. Through these efforts, 57% of the European population is supplied with high quality information, enabling them to adapt behaviour. Although health care, including skin cancer prevention, is cost-effective, a proportion of the European population still doesn't have access to UV Index information.
International audienceDaily estimates of the solar UV-A radiation (315–400 nm) at the surface, anywhere, anytime, are needed in many epidemiology studies. Satellite-derived databases of solar total irradiance, combined with empirical relationships converting totals into daily means of UV-A irradiance I UV , are a means to satisfy such needs. Four empirical relationships are applied to three different databases: HelioClim-3 (versions 4 and 5) and CAMS Radiation Service—formerly known as MACC-RAD—derived from Meteosat images. The results of these combinations are compared to ground-based measurements located in mid-latitude Europe, mostly in Belgium. Whatever the database, the relationships of Podstawczynska (2010) and of Bilbao et al. (2011) exhibit very large underestimation and RMSE on the order of 40%–50% of the mean I UV. Better and more acceptable results are attained with the relationships proposed by Zavodska and Reichrt (1985) and that of Wald (2012). The relative RMSE is still large and in the range 10%–30% of the mean I UV. The correlation coefficients are large for all relationships. Each of them captures most of the variability contained in the UV measurements and can be used in studies where correlation plays a major role
Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO2 concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.