Kostas Philippopoulos

Wind Speed Distribution Modeling in the Greater Area of Chania, Greece

The current study presents a wind speed regional assessment for the greater area of a Mediterranean coastal valley in the island of Crete, Greece. Wind speed and direction experimental data are employed from six sites, appropriately located to incorporate the effect of the main topographical features. The mean wind speed and direction assessment is performed for the overall and seasonal periods and distinct wind speed patterns are identified. The wind power density is estimated for each site and regarding the development of wind energy applications, two areas with different characteristics are established. The Weibull, Rayleigh, Lognormal, Gamma, and Inverse Gaussian distributions are assessed for their ability to model the experimental wind speed frequency distributions for the monthly and overall period. Their goodness-of-fit is assessed using the coefficient of determination and the χ2 hypothesis test. Additionally, the visual inspection of their fits to the corresponding histograms is done and the error on the estimated mean wind speed and its variance is examined. The Gamma probability distribution function is proposed as an alternative to the Weibull distribution for the area under study.

Spatial Interpolation Methodologies in Urban Air Pollution Modeling: Application for the Greater Area of Metropolitan Athens, Greece

Air pollution in urban environments has serious health and quality of life implications. A wide variety of anthropogenic air pollution sources increase the levels of background air pollutant concentrations, leading to the deterioration of the ambient air quality. Principal sources of urban air pollution are vehicular traffic, industrial activity and in general fossil fuel combustion, introducing a mixture of chemical components, particulate matter and biological material into the atmosphere. The deterioration of urban air quality is considered worldwide one of the primary environmental issues and current scientific evidence associate the exposure to ambient air pollution with a wide spectrum of health effects like cardiopulmonary diseases, respiratory related hospital admissions and premature mortality (Analitis et al. 2006; Ito et al., 2005; Samet et al., 2000). Direct measurements of sensitive population groups’ exposure to air pollution are scarce and therefore methods of accurate point and areal air quality estimations are prerequisite. This fact highlights the importance of generating accurate fields of air pollution for quantifying present and future health related risks. In the field of air pollution modeling, two different approaches have been adopted by the scientific community, differentiated by their applied fundamental principles. The first approach involves the numerical simulation of atmospheric dispersion based on the current understanding of physics and chemistry that govern the transport, dispersion and transformation of pollutants in the atmosphere. The modeling process typically requires a set of parameters such as meteorological fields, terrain information along with a comprehensive description of pollution sources. An alternative approach is based on statistical analysis of pollutant concentrations collected from air quality monitoring networks commonly deployed in urban areas. The reasoning of the statistical approach is that physical processes are likely to induce correlations in air quality data collected over space and time. Statistical models generate predictions by exploiting these spatio-temporal patterns, enabling the estimation of pollutant concentrations in unmonitored locations. The chapter’s main objective is to present and review the statistical spatial interpolation methodologies which are commonly employed in the field of air pollution modeling. An additional scope of the chapter is to compare and evaluate the accuracy of the interpolation methods for point estimations, using data from a real urban air quality monitoring network located at the greater area of metropolitan Athens in Greece.

The impact of sea breeze under different synoptic patterns on air pollution within Athens basin

Air quality in densely populated urban coastal areas is directly related to the coupling of the synoptic and the local scale flows. The dispersion conditions within Athens basin, under the influence of different meteorological forcings, lead to distinct spatio-temporal air pollution patterns. The aim of the current observational research is to identify and examine the effect of sea breeze under different atmospheric circulation patterns on air pollution levels for a one-year study period (2007). The study employs surface pressure maps, routine meteorological observations at two coastal sites and nitrogen monoxide (NO), nitrogen dioxide (NO(2)) and ozone (O(3)) concentrations from a network of four air quality stations within the Athens basin. A three-step methodology is applied that incorporates a set of criteria for classifying atmospheric circulation and identifying sea breeze events under each circulation pattern. Two types of sea breeze development are identified (pure sea breeze-PSB and modified sea breeze-MSB) with distinct characteristics. Sea breeze is found to develop more frequently under offshore compared to onshore and parallel to the shoreline background flows. Poor dispersion conditions (high nitrogen oxides-NO(x) and O(3) concentrations) are connected to the pure sea breeze cases and to those cases where sea breeze interacts with a moderate northerly flow during the warm period. The levels of NO(x) and O(3) for the northern Athens basin area are found to be significantly higher during the sea breeze days compared to the Etesian days. Regarding the diurnal variation of ozone for the sea breeze days, peak concentrations and higher intra-daily ranges are observed. Day-to-day pollution accumulation (build-up effect) is measured for O(3) at the northern stations in the Athens basin.

Artificial Neural Network Based Methodologies for the Estimation of Wind Speed

Recent advances in artificial neural networks (ANN) propose an alternative promising methodological approach to the problem of time series assessment as well as point spatial interpolation of irregularly and gridded data. In the field of wind power sustainable energy systems ANNs can be used as function approximators to estimate both the time and spatial wind speed distributions based on observational data. The first part of this work reviews the theoretical background, the mathematical formulation, the relative advantages, and limitations of ANN methodologies applicable to the field of wind speed time series and spatial modeling. The second part focuses on implementation issues and on evaluating the accuracy of the aforementioned methodologies using a set of metrics in the case of a specific region with complex terrain. A number of alternative feedforward ANN topologies have been applied in order to assess the spatial and time series wind speed prediction capabilities in different time scales. For the temporal forecasting of wind speed ANNs were trained using the Levenberg–Marquardt backpropagation algorithm with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. For the spatial estimation of wind speed the nonlinear Radial basis function Artificial Neural Networks are compared versus the linear Multiple Linear Regression scheme.

Artificial Neural Network Modeling of Relative Humidity and Air Temperature Spatial and Temporal Distributions Over Complex Terrains

In this work we present a methodological approach of applying Artificial Neural Networks (ANN) for modeling of both the air temperature (AT) and relative humidity (RH) spatial and temporal distributions over complex terrains. A number of implementation issues are discussed, along with their relative advantages and limitations. Moreover, after the introduction of a set of metrics, the accuracy of the evaluation of ANN based spatial and time series AT and RH modeling in the case of a specific region is examined, by applying a number of alternative feed forward ANN topologies. The Levenberg-Marquardt back propagation algorithm was used for the ANNs training in the temporal forecasting of AT and RH, with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. The Radial Basis Function and the Multilayer Perceptrons non-linear Feed Forward ANNs schemes are compared for the spatial estimation of AT and RH. We found that the spatial and temporal AT and RH variability over complex terrains can be modeled efficiently by ANNs.

A self-organizing maps multivariate spatio-temporal approach for the classification of atmospheric conditions

This work demonstrates the potential of Self-Organizing Maps (SOM) as a multivariate clustering approach of spatio-temporal datasets in atmospheric physics. A comprehensive framework is proposed and the method is applied and assessed for its performance in the field of synoptic climatology within a specific region at southeastern Mediterranean. The results indicate that the SOM can be a powerful tool for the identification and classification of atmospheric conditions, allowing an analytical description of the principal atmospheric states. The coupling of sea level pressure (SLP) and 500hPa geopotential (Φ500) in a synoptic-scale domain with the wind, the specific humidity and the air and dew point temperature in the chosen mesoscale subdomain, allows the SOM algorithm to define the relevant atmospheric circulation patterns. The corresponding patterns are well documented and the method accounts for their seasonality. Furthermore, in the resulting two-dimensional lattice the similar patterns are mapped closer to each other, compared to more dissimilar ones.

Winter atmospheric circulation patterns and their relationship with the meteorological conditions in Greece

This study presents an analysis of the relationship between winter large-scale circulation and surface meteorological conditions over Greece for the period 1979–2009. The adopted methodology involves the application of an automated atmospheric circulation classification scheme based on the self-organizing map approach. The impact of each of the identified relevant 19 winter atmospheric circulation patterns on local meteorological condition is examined at seven sites by calculating the corresponding differences from the mean meteorological conditions. The conditional transition probabilities of circulation patterns indicate the existence of increased 1-day persistence, especially for the anticyclonic and the pattern related to Genoa depressions. Positive temperature anomalies are observed for the cyclonic patterns, while negative anomalies are attributed to the effect of anticyclonic circulation.

A Comparative Study of Three Spatial Interpolation Methodologies for the Analysis of Air Pollution Concentrations in Athens, Greece

Spatial interpolation in air pollution modeling is the procedure for estimating ambient air pollution concentrations at unmonitored locations based on available observations. The selection of the appropriate methodology is based on the nature and the quality of the interpolated data. In this paper, an assessment of three widely used interpolation methodologies is undertaken in order to estimate the errors involved. For this purpose, air quality data from January 2001 to December 2005, from a network of seventeen monitoring stations, operating at the greater area of Athens in Greece, are used. The Nearest Neighbor and the Liner schemes were applied to the mean hourly observations, while the Inverse Distance Weighted (IDW) method to the mean monthly concentrations. The discrepancies of the estimated and measured values are assessed for every station and pollutant, using the correlation coefficient, the scatter diagrams and the statistical residuals. The capability of the methods to estimate air quality data...

Scaling Properties of Meteorological Time Series Using Detrended Fluctuation Analysis

Meteorological parameters depend on a diversity of natural processes and show random fluctuations on different temporal and spatial scales as a result of the relevant complex natural processes. A powerful tool for examining these fluctuations is the Detrended Fluctuation Analysis (DFA), which detects long-term correlations in nonstationary time series. In this study, we apply the DFA method to daily meteorological time series (i.e. temperature, pressure, relative humidity and wind speed) for the Thessaloniki surface weather station from January 1973 to December 2014. By examining long-range correlations, we detect if the time series exhibit long and/or short range “memory”. Moreover, we compare the behavior of these time series from the aspect of DFA, focusing on the observed similarities or differences of the relevant findings.