Determination of surface layer parameters at the edge of a suburban area

Vertical wind and air temperature profile related parameters in the surface layer at the edge of suburban area of Zagreb (Croatia) have been considered. For that purpose, adopted Monin–Obukhov similarity theory and a set of observations of wind and air temperature at 2 and 10?m above ground, recorded in 2005, have been used. The root mean square differences (errors) principle has been used as a tool to estimate the effective roughness length as well as standard deviations of wind speed and wind gusts. The results of estimation are effective roughness lengths dependent on eight wind direction sectors unknown before. Gratefully to that achievement, representativeness of wind data at standard 10-m height can be clarified more deeply for an area of at least about 1?km in upwind direction from the observation site. Extrapolation of wind data for lower or higher levels from standard 10-m height are thus properly representative for a wider inhomogeneous suburban area and can be used as such in numerical models, flux and wind energy estimation, civil engineering, air pollution and climatological applications.     

Performance of ENSEMBLES regional climate models over Central Europe using various metrics

We show the evaluation of ENSEMBLES regional climate models (RCMs) driven by reanalysis ERA40 over a region centered at the Czech Republic. Attention is paid especially to the model ALADIN-CLIMATE/CZ, being used as the basis of the new climate change scenarios simulation for the Czech Republic. The validation criteria used here are based on monthly or seasonal mean air temperature and precipitation. We concentrate not only on spatiotemporal mean values but also on temporal standard deviation, inter-annual variability, the mean annual cycle, and the skill of the models to represent the observed spatial patterns of these quantities. Model ALADIN-CLIMATE/CZ performs quite well in comparison to the other RCMs; we find its performance satisfactory for further use for impact studies. However, it is also shown that the results of evaluation of the RCMs’ skill in simulating observed climate strongly depend on the criteria incorporated for the evaluation.     

The climate tourism potential of Alpine destinations using the example of Sonnblick, Rauris and Salzburg

The climate tourism potential of a region can be described by methods used in human biometeorology and applied climatology. Frequency analyses based on complex thermal bioclimatic indices (e.g. physiologically equivalent temperature) and diagrams of precipitation patterns based on thresholds offer new approaches of visualisation. An integral approach for tourism climatologic analyses is provided by the climate?Ctourism/transfer?Cinformation?Cscheme that also bases on frequency distributions of relevant factors and parameters which are important for a destination. The knowledge about the vertical variability of tourism climatologic factors is of high importance because of the several kinds of tourism activities affected by weather. The same holds for a quantification of extreme events like heat waves because of their possible effects on health and recreation over a year's course. The results show that the vertical gradient of bioclimatic and tourism-related parameters can be of value when developing strategies of adaption to climate change.     

Some evidence on European monsoon existence

Large-scale European atmospheric circulation induced by temperature differences between the continent and the North Atlantic Ocean causes thermodynamic and climatic conditions that initiate a European monsoon. In Eastern Europe, the rainy season occurs in early summer, and the dry season occurs in winter. In Western Europe, the rainy season is in the early winter and the dry season is in the spring. This precipitation trend, as well as other climatic features, suggests the existence of a European monsoon.     

Estimating reliability and resolution of probability forecasts through decomposition of the empirical score

Proper scoring rules provide a useful means to evaluate probabilistic forecasts. Independent from scoring rules, it has been argued that reliability and resolution are desirable forecast attributes. The mathematical expectation value of the score allows for a decomposition into reliability and resolution related terms, demonstrating a relationship between scoring rules and reliability/resolution. A similar decomposition holds for the empirical (i.e. sample average) score over an archive of forecast–observation pairs. This empirical decomposition though provides a too optimistic estimate of the potential score (i.e. the optimum score which could be obtained through recalibration), showing that a forecast assessment based solely on the empirical resolution and reliability terms will be misleading. The differences between the theoretical and empirical decomposition are investigated, and specific recommendations are given how to obtain better estimators of reliability and resolution in the case of the Brier and Ignorance scoring rule.     

Analyses of possible changes in intense and extreme wind speeds over northern Europe under climate change scenarios

Dynamical downscaling of ECHAM5 using HIRHAM5 and RCA3 for a northern European domain focused on Scandinavia indicates sustained extreme wind speeds with long recurrence intervals (50?years) and intense winds are not likely to evolve out of the historical envelope of variability until the end of C21st. Even then, significant changes are indicated only in the SW of the domain and across the central Baltic Sea where there is some evidence for relatively small magnitude increases in the 50?year return period wind speed (of up to 15%). There are marked differences in results based on the two Regional Climate Models. Additionally, internal (inherent) variability and initial conditions exert a strong impact on projected wind climates throughout the twenty-first century. Simulations of wind gusts by one of the RCMs (RCA3) indicate some evidence for increased magnitudes (of up to +10%) in the southwest of the domain and across the central Baltic Sea by the end of the current century. As in prior downscaling of ECHAM4, dynamical downscaling of ECHAM5 indicates a tendency towards increased energy density and thus wind power generation potential over the course of the C21st. However, caution should be used in interpreting this inference given the high degree of wind climate projection spread that derives from the specific AOGCM and RCM used in the downscaling.     

Performance of a multi-RCM ensemble for South Eastern South America

The ability of four regional climate models to reproduce the present-day South American climate is examined with emphasis on La Plata Basin. Models were integrated for the period 1991–2000 with initial and lateral boundary conditions from ERA-40 Reanalysis. The ensemble sea level pressure, maximum and minimum temperatures and precipitation are evaluated in terms of seasonal means and extreme indices based on a percentile approach. Dispersion among the individual models and uncertainties when comparing the ensemble mean with different climatologies are also discussed. The ensemble mean is warmer than the observations in South Eastern South America (SESA), especially for minimum winter temperatures with errors increasing in magnitude towards the tails of the distributions. The ensemble mean reproduces the broad spatial pattern of precipitation, but overestimates the convective precipitation in the tropics and the orographic precipitation along the Andes and over the Brazilian Highlands, and underestimates the precipitation near the monsoon core region. The models overestimate the number of wet days and underestimate the daily intensity of rainfall for both seasons suggesting a premature triggering of convection. The skill of models to simulate the intensity of convective precipitation in summer in SESA and the variability associated with heavy precipitation events (the upper quartile daily precipitation) is far from satisfactory. Owing to the sparseness of the observing network, ensemble and observations uncertainties in seasonal means are comparable for some regions and seasons.     

Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26?C45°E and 36?C42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski?ehir, ?stanbul, ?zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST_ANN) and Becker and Li temperature(LST_B-L) method values have been found as 0.077?K and 0.091?K (training stations), 0.045?K and 0.003?K (testing stations), respectively.