H. Feidas

Trend analysis of air temperature time series in Greece and their relationship with circulation using surface and satellite data: 1955-2001

SummaryIn this study, trends of annual and seasonal surface air temperature time series were examined for 20 stations in Greece for the period 1955–2001, and satellite data for the period 1980–2001. Two statistical tests based on the least square method and one based on the Mann-Kendall test, which is also capable of detecting the starting year of possible climatic discontinuities or changes, were used for the analysis. Greece, in general, shows a cooling trend in winter for the period 1955–2001, whereas, summer shows an overall warming trend, however, neither is statistically significant. As a result, the overall trend of the annual values is nearly zero. Comparison with corresponding trends in the Northern Hemisphere (NH) shows that temperatures in Greece do not follow the intense warming trends. Satellite data indicate a remarkable warming trend in mean annual, winter and summer in Greece for the period 1980–2001, and a slight warming trend in annual, spring and autumn for the NH. Comparison with the respective trends detected in the surface air temperature for the same period (1980–2001) shows they match each other quite well in both Greece and the NH. The relationship between temperature variability in Greece and atmospheric circulation was also examined using correlation analysis with three circulation indices: the well-known North Atlantic Oscillation Index (NAOI), a Mediterranean Oscillation Index (MOI) and a new Mediterranean Circulation Index (MCI). The MOI and MCI indices show the most interesting correlation with winter temperatures in Greece. The behaviour of pressure and the height of the 500 hPa surface over the Mediterranean region supports these results.

The Meteorological Model BOLAM at the National Observatory of Athens: Assessment of Two-Year Operational Use

Abstract Since November 1999, the hydrostatic meteorological Bologna Limited-Area Model (BOLAM) has been running operationally at the National Observatory of Athens. The assessment of the model forecast skill during the 2-yr period included (a) calculation of the root-mean-square errors (model vs gridded analyses) of geopotential height and temperature at 850 and 500 hPa, (b) evaluation of the models quantitative precipitation forecast skill for the most important events, and (c) evaluation of the model skill in the prediction of surface winds in comparison with buoy observations. Comparison of the verification results with those provided in the literature showed that BOLAM has a high forecast skill, even for precipitation, which is the most difficult parameter to forecast. Especially for precipitation, the comparison between coarse (∼21 km) and fine (∼6.5 km) grid spacing forecasts showed that for the low and medium precipitation amounts, the finer-grid forecasts are not as good as the coarse-grid forec...

Evaluating remotely sensed rainfall estimates using nonlinear mixed models and geographically weighted regression

This article evaluates an infrared-based satellite algorithm for rainfall estimation, the Convective Stratiform technique, over Mediterranean. Unlike a large number of works that evaluate remotely sensed estimates concentrating on global measures of accuracy, this work examines the relationship between ground truth and satellit0e derived data in a local scale. Hence, we examine the fit of ground truth and remotely sensed data on a widely adopted probability distribution for rainfall totals - the mixed lognormal distribution - per measurement location. Moreover, we test for spatial nonstationarity in the relationship between in situ observed and satellite-estimated rainfall totals. The former investigation takes place via using recent algorithms that estimate nonlinear mixed models whereas the latter uses geographically weighted regression.

Comparison of atmospheric correction methods using ASTER data for the area of Crete, Greece

The purpose of atmospheric correction is to produce more accurate surface reflectance and to potentially improve the extraction of surface parameters from satellite images. To achieve this goal the influences of the atmosphere, solar illumination, sensor viewing geometry and terrain information have to be taken into account. Although a lot of information from satellite imagery can be extracted without atmospheric correction, the physically based approach offers advantages, especially when dealing with multitemporal data and/or when a comparison of data provided by different sensors is required. The use of atmospheric correction models is limited by the need to supply data related to the condition of the atmosphere at the time of imaging. Such data are not always available and the cost of their collection is considerable, hence atmospheric correction is performed with the use of standard atmospheric profiles. The use of these profiles results in a loss of accuracy. Therefore, site-dependent databases of atmospheric parameters are needed to calibrate and to adjust atmospheric correction methods for local level applications. In this article, the methodology and results of the project Adjustment of Atmospheric Correction Methods for Local Studies: Application in ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (ATMOSAT) for the area of Crete are presented. ATMOSAT aimed at comparing several atmospheric correction methods for the area of Crete, as well as investigating the effects of atmospheric correction on land cover classification and change detection. Databases of spatio-temporal distributions of all required input parameters (atmospheric humidity, aerosols, spectral signatures, land cover and elevation) were developed and four atmospheric correction methods were applied and compared. The baseline for this comparison is the spatial distribution of surface reflectance, emitted radiance and brightness temperature as derived by ASTER Higher Level Products (HLPs). The comparison showed that a simple image based method, which was adjusted for the study area, provided satisfactory results for visible, near infrared and short-wave infrared spectral areas; therefore it can be used for local level applications. Finally, the effects of atmospheric correction on land cover classification and change detection were assessed using a time series of ASTER multispectral images acquired in 2000, 2002, 2004 and 2006. Results are in agreement with past studies, indicating that for this type of application, where a common radiometric scale is assumed among the multitemporal images, atmospheric correction should be taken into consideration in pre-processing.

Wind characteristics and mapping for power production in the Island of Lesvos, Greece

This study investigated the wind characteristics of the island of Lesvos, Greece, with the objective of providing the necessary data for identifying the wind power production capabilities of the island. Weather patterns were examined using weather data from four Remote Automatic Weather Stations. Specific tools were used to produce the necessary windroses, Weibull curves and charts that helped to understand the prevailing wind characteristics. By using the tools of Geographic Information Systems (GIS) and the Wind Atlas Analysis and Application Program (WAsP) as the basic calculation platform, a wind map was produced portraying the wind speeds that prevail at a height of 10m above ground level. The results of the analysis were tested and evaluated with measurements from 15 wind turbine sites by creating six alternative scenarios. The optimum scenario was used to investigate the installation of a small wind farm with five wind turbines, of 3 MW total capacity.

Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data

This paper investigates the potential for developing schemes that classify convective and stratiform precipitation areas using the high infrared spectral resolution of the Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). Two different classification schemes were proposed that use the brightness temperature (BT) Τ10.8 along with the brightness temperature differences (BTDs) Τ10.8–Τ12.1, Τ8.7–Τ10.8, and Τ6.2–Τ10.8 as spectral parameters, which provide information about cloud parameters. The first is a common multispectral thresholding scheme used to partition the space of the spectral cloud parameters and the second is an algorithm based on the probability of convective rain (PCR) for each pixel of the satellite data. Both schemes were calibrated using as a reference convective\stratiform rain classification fields derived from 87 stations in Greece for six rainy days with high convective activity. As a result, one single infrared technique (TB10) and two multidimensional techniques (BTDall and PCR) were constructed and evaluated against an independent sample of rain gauge data for four daily convective precipitation events. It was found that the introduction of BTDs as additional information to a technique works in improving the discrimination of convective from stratiform rainy pixels compared to the single infrared technique BT10. During the training phase, BTDall performed slightly better than BT10 while PCR technique outperformed both threshold techniques. All techniques clearly overestimate the convective rain occurrences detected by the rain gauge network. When evaluating against the independent dataset, both threshold techniques exhibited the same performance with that of the dependent dataset whereas the PCR technique showed a notable skill degradation. As a result, BTDall performed best followed at a short distance by PCR and BT10. These findings showed that it is possible to apply a convective/stratiform rain classification algorithm based on the enhanced infrared spectral resolution of MSG-SEVIRI, for nowcasting or climate purposes, despite the highly variable nature of convective precipitation.

Monitoring Mesoscale Convective Cloud Systems Associated with Heavy Storms Using Meteosat Imagery

In this study, an automatic algorithm for monitoring areas of cold cloud tops within mesoscale convective systems that produced floods in Greece is developed. The technique is based on Meteosat infrared and water vapor images. The purpose of the algorithm is the estimation and monitoring of a variety of characteristics associated with propagating convective systems. The algorithm is capable of locating convective regions, tracking them until the point of dissipation, and taking into account the eventual splitting or merging of clouds that takes place during the lifetime of the system. The operability of the algorithm for the two most intense heavy rainfall events that occurred recently in Greece is examined and assessed.

Geographic distribution modelling for ruminant liver flukes (Fasciola hepatica) in south-eastern Europe.

Maximum entropy ecological niche modelling was utilised to predict the geographic range for fluke genotypes and haplotypes in south-eastern Europe, using the Maxent program. The lowest (0.832) and the highest (0.947) area under the curve values were observed in the models for the haplotypes CtCmt1 and CtCmt2.2, respectively. Precipitation and temperature contribute equally to model building of the genotypes based on the 28S rDNA gene. In regard to the mtDNA gene region, precipitation is the most important factor in modelling the CtCmt1 haplotype range, while temperature appears to be the most important factor in modelling the CtCmt2.1 and CtCmt2.2 haplotype ranges. The highest level of probability for the geographic distribution of Fasciola hepatica genotypes and haplotypes covered the regions of southern Bulgaria and central and northern Greece which contain a high concentration of potential ruminant hosts.

Seroprevalence and risk factors associated with zoonotic parasitic infections in small ruminants in the Greek temperate environment

A cross-sectional serological study was carried out to screen the sheep and goat population of Thessaly, Greece for evidence of infection with Toxoplasma, Toxocara, Leishmania, and Echinococcus and to determine the risk factors related to herd characteristics, herd management practices, farmer status, and the bioclimatic variables associated with these zoonotic parasitic infections. A total of 540 sheep and goat serum samples were examined. The seroprevalence of infection in all examined animals was 24.5% for Toxoplasma, 32% for Toxocara, 0% for Leishmania and 85.9% for Echinococcus. The final logistic regression model showed that the species of small ruminant, herd size, anthelmintic treatment, class of anthelmintic treatment, grazing with other herds, educational level of farmer, elevation of farm location, and generalized land cover were associated with Toxoplasma gondii infections, while the species of small ruminant, farm type, anthelmintic treatment, class of anthelmintic treatment, rotation of grazing, age of farmer, elevation of farm location, and generalized land cover were associated with Toxocara canis infections. Antibodies to T. gondii were detected in 102 (28.3%) of 360 sheep and in 30 (16.8%) of 179 goats. Animals in small flocks (150-300 animals) had an approximately 0.42-fold lower risk of having positive cases of T. gondii among animals compared with large flocks (>300 animals). Antibodies to T. canis were found in 155 (42.9%) of 361 sheep and 18 (10.1%) of 179 goats. The later finding constitutes the first report of seropositive goats to Toxocara. The risk of positivity for T. canis was 7.71-fold higher in sheep than in goats. Geographically, animals from plain areas had 2.9 and 2.01-fold higher risk of having positive cases of T. gondii and T. canis respectively. The significant bioclimatic variables (p<0.05) associated with the occurrence locations of T. gondii infection were related to higher temperature, lower precipitation, and lower elevation compared to the absence locations of T. gondii. The significant bioclimatic variables (p<0.05) associated with occurrence locations of T. canis infection were related to lower temperature and higher precipitation compared to absence locations of T. canis. These findings are useful to formulate appropriate control strategies for zoonotic parasites of sheep and goats in Greece and other areas with similar climatic conditions.

Modeling and mapping temperature and precipitation climate data in Greece using topographical and geographical parameters

This study presents a methodology for modeling and mapping the seasonal and annual air temperature and precipitation climate normals over Greece using several topographical and geographical parameters. Data series of air temperature and precipitation from 84 weather stations distributed evenly over Greece are used along with a set of topographical and geographical parameters extracted with Geographic Information System methods from a digital elevation model (DEM). Normalized difference vegetation index (NDVI) obtained from MODIS Aqua satellite data is also used as a geographical parameter. First, the relation of the two climate elements to the topographical and geographical parameters was investigated based on the Pearson’s correlation coefficient to identify the parameters that mostly affect the spatial variability of air temperature and precipitation over Greece. Then a backward stepwise multiple regression was applied to add topographical and geographical parameters as independent variables into a regression equation and develop linear estimation models for both climate parameters. These models are subjected to residual correction using different local interpolation methods, in an attempt to refine the estimated values. The validity of these models is checked through cross-validation error statistics against an independent test subset of station data. The topographical and geographical parameters used as independent variables in the multiple regression models are mostly those found to be strongly correlated with both climatic variables. Models perform best for annual and spring temperatures and effectively for winter and autumn temperatures. Summer temperature spatial variability is rather poorly simulated by the multiple regression model. On the contrary, best performance is obtained for summer and autumn precipitation while the multiple regression model is not able to simulate effectively the spatial distribution of spring precipitation. Results revealed also a relatively weaker model performance for precipitation than that for air temperature probably due to the highly variable nature of precipitation compared to the relatively low spatial variability of air temperature field. The correction of the developed regression models using residuals improved though not significantly the interpolation accuracy.