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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.

A study of the vertical distribution of the common tick Ixodes ricinus and tick-borne pathogens -tick-borne encephalitis virus (TBEV) and genospecies of Borrelia burgdorferi s.l. -was performed in the highest part of the Jeseníky mountain area (the Hrubý Jeseník Mts. with the highest summit Praděd, 1,491 m above see level). Altogether 1,253 specimens of all tick stages (607 larvae, 614 nymphs, 8 females and 24 males) were collected at the altitude 990-1,300 m above sea level on 12 collection sites by the flagging method. Altogether 1,207 ticks (8 females, 24 males, 568 nymphs and 607 larvae) were examined for the presence of tick-borne encephalitis virus and B. burgdorferi s.l. None of the samples contained TBEV, 35 samples (6% of adult ticks, 5% of nymphs, 0.7% of larvae) were positive for B. burgdorferi s.l. The most prevalent genospecies were B. afzelii (44%), B. garinii (28%), less frequent were B. burgdorferi sensu stricto (5%), B. valaisiana (3%). The rather large number of ticks (in absolute numbers as well as recounted to the index: average number of nymphs/worker/collection hour) and the presence of all developmental stages clearly demonstrate that there are viable local tick populations in all the sites, and that recorded ticks were not randomly individuals brought into higher altitudes by birds or game animals. The results are compared with the long-term (2002)(2003)(2004)(2005)(2006)(2007) monitoring of the tick altitudinal distribution in the Krkonoše Mts. and the conditions, which allow ticks to establish local populations up to the timberline in both mountain areas, are discussed. Simultaneously, changes in climatic conditions (especially the air temperature) monitored at 3 meteorological stations in the area of the Jeseníky Mts. were compared with the records from another 8 stations in other mountain areas in the Czech Republic. A very similar statistically significant trend of increasing mean air temperatures during the last three decades is found at all analyzed stations. The trend is most pronounced in the spring and summer months with the highest activity of I. ricinus ticks.

Several linear and non-linear statistical downscaling methods are compared for winter daily temperature at eight European stations. The linear methods include linear regression of gridpoint values (pointwise regression) and of predictors' principal components (PC regression). The non-linear methods are represented by artificial neural networks. The non-linearity is also achieved by a stratification of data by classification of circulation patterns and a linear regression conducted separately within each class. As predictors, gridded 500 hPa heights and 850 hPa temperature are used. The verification is conducted in the cross-validation framework. The downscaling methods are evaluated according to four criteria: (1) fit to observations (quantified by the correlation coefficient), (2) shape of the statistical distribution, namely its skewness and kurtosis, (3) temporal autocorrelations with 1 day lag, and (4) interstation correlations. Considering all the criteria together, the pointwise linear regression appears to be the best method. It achieves the best fit with the observations and possesses the best temporal structure. The deviations of statistical distributions from normality are only captured by the neural networks, while the classification methods yield the best spatial correlations.

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