The question of thermal comfort is becoming relevant for the Central Europe cities, given the growing impacts of urban heat islands in a period of climate change (Geletič and Vysoudil, 2012). Planning adaptation measures in urban public space is an important issue for urban planners (Luca, 2017; Slach and Ježek, 2015). Outdoor restaurant spaces represent special areas that should be planned with regard to the thermal comfort of visitors (Égerházi et al., 2009). The notion of the thermal comfort of a person, or thermal well-being, expresses a certain degree of satisfaction based on a subjective evaluation of thermal conditions. In addition to individual personal preferences, the evaluation is influenced by physical environmental factors (ASHRAE, 2004). In particular, factors such as air temperature and humidity, wind Centre for Research on Settlements and Urbanism Journal of Settlements and Spatial Planning J o u r n a l h o m e p a g e: http://jssp.reviste.ubbcluj.ro Due to climate change, the question of thermal comfort in cities is becoming more important in Central Europe. The aim of the study is to design and experimentally verify the typology of outdoor areas of restaurants in terms of their thermal comfort factors, based on a case study of Pilsen´s city centre. The research carried out in 2017 in the city centre of Pilsen investigated what means are used by restaurants to improve thermal conditions and how the location of an outdoor restaurant in an urban area affects its thermal comfort. There were forty-three outdoor areas of city centre restaurants included in the proposed typology, based upon their location. The relevance of typology was verified using a selected set of fourteen outdoor restaurant areas. They were evaluated according to the availability of means for comfort enhancement and experimental measurements of relevant meteorological elements. Type 1 (Square restaurant) exhibited the highest air and surface temperatures. In contrast, Type 2 (Courtyard restaurant) showed the lowest air and surface temperatures and Type 3 (Park restaurant) showed the highest air humidity. Type 4 (Street canyon restaurant) had similar temperatures to Type 1, and air humidity was closer to Type 2. According to the number of Beergarden Days, the days when the air temperature at 9 p.m. was higher than 20 °C, the best conditions for sitting outside throughout the year occur from the second half of June to the end of August. The results of the study can contribute to an improvement in the thermal comfort of customers of outdoor restaurants in cities, especially with regard to the choice of locations of outdoor restaurants and the use of elements that influence thermal comfort.

<p>Different types of climate datasets (station, gridded, reanalyses) and even individual datasets have been shown to differ in how they capture statistical properties of climate variables. Here we compare trends in precipitation totals in Europe between station data (taken from the ECA&D database), gridded data (E-OBS and CRU TS), and reanalyses (20CR, JRA-55, and NCEP/NCAR) for period 1961-2011, both annually and for individual seasons. Theil-Sen non-parametric trend estimator is used for the quantification of the trend magnitude; Mann-Kendall test is used to evaluate the significance of trends.</p><p>On the annual basis, station data indicate precipitation increases in northern Europe and decreases in southern and southeastern Europe. Whereas trends in the gridded datasets roughly agree with station data, although tend to overestimate them, reanalyses provide much more negative trends with a different geographical distribution. There is a tendency for reanalyses to overestimate precipitation in the beginning of the period at some places, whereas they underestimate precipitation near the end of the period elsewhere. Particularly notable is an excessive, and likely unrealistic, drying trend in central, southwestern, and southeastern Europe in NCEP/NCAR in most seasons. Reanalyses thus do not appear to be suitable data sources for estimation of precipitation trends.  </p><p>Reasons for the disagreement are identified by a detailed examination of local or regional time series. The reasons are varied and depend on the specific type of dataset: Station series may suffer from inhomogeneities; gridded data may be affected by different sets of stations entering the interpolation procedure at different times; while reanalyses may be affected by different kinds of data being assimilated into them in different periods.</p>

<p>It is already a well known fact that different types of climate datasets (station, gridded, reanalyses) and even individual datasets differ in how they describe statistical properties of climate variables. Here we compare precipitation trends in Europe between station data (taken from the ECA&D database), gridded data (E-OBS and CRU TS), and reanalyses (JRA-55 and NCEP/NCAR) for period 1961-2011, both annually and for individual seasons. Theil-Sen non-parametric trend estimator is used for the quantification of the trend magnitude; Mann-Kendall test is used to evaluate the significance of trends.</p><p>On the annual basis, station data indicate precipitation increases in northern Europe and decreases in southern and southeastern Europe. Whereas trends in the gridded datasets roughly agree with station data, reanalyses provide much more negative trends with a different geographical distribution. There is a tendency for reanalyses to overestimate precipitation in the beginning of the period at some places, whereas they underestimate precipitation near the end of the period elsewhere. The disagreement among different data types and datasets is larger in all seasonal analyses except winter. Particularly notable is an excessive drying trend in central, southwestern, and southeastern Europe in NCEP/NCAR in most seasons. Reanalyses thus do not appear to be suitable data sources for estimation of precipitation trends.  </p><p>Reasons for the disagreement are varied and are conjectured by a detailed examination of station / point or regional time series: station series may suffer from inhomogeneities; gridded data may be affected by different sets of stations entering the interpolation procedure at different times; while reanalyses may be affected by different kinds of data being assimilated into them in different periods.</p>