Moderate Resolution Imaging Spectroradiometer MODIS, land surface temperature data, during daytime LSTday or night-time LSTnight, were employed for predicting maximum Tmax or minimum Tmin air temperature measured at ground stations, respectively, in order to be used as alternative inputs in minimum data-based reference evapotranspiration ET models in 28 stations in Greece during the growing season May–October. The deviations between daily LSTnight and Tmin were found to be small, but they were greater between LSTday and Tmax. Furthermore, the temperature vegetation index TVX method was employed for achieving more accurate Tmax values from LSTday, after determining the normalized difference vegetation index of a full canopy NDVImax. The TVX method was validated on ‘temporal’ basis, but when the method was tested spatially, the improvement on the Tmax estimates from LSTday was not encouraging, for being used operationally over Greece. Thus, LSTday or LSTnight MODIS data were used as inputs in three ET models [Hargreaves–Samani, Droogers–Allen, and Reference Evapotranspiration Model for Complex Terrains REMCT] and their estimations, as compared with ground-based Penman–Monteith estimates, indicated that the REMCT model achieved the most accurate ET predictions r = 0.93, mean bias error = 0.44 mm day–1 and root mean square error = 0.74 mm day–1, which can allow the spatial analysis of ET at higher spatial resolutions in areas with lack of ground temperature data.

This paper presents some of the results of a project that aimed at the design and implementation of a system for the spatial mapping and forecasting the temporal evolution of air pollution from dust transport from the Sahara Desert into the eastern Mediterranean and secondarily from anthropogenic sources, focusing over Cyprus. Monitoring air pollution (aerosols) in near real-time is accomplished by using spaceborne and in situ platforms. The results of the development of a system for forecasting pollution levels in terms of particulate matter concentrations are presented. The aim of the present study is to utilize the recorded PM 10 (particulate matter with aerodynamic diameter less than 10 μ m) ground measurements, Aerosol Optical Depth retrievals from satellite, and the prevailing synoptic conditions established by Artificial Neural Networks, in order to develop regression models that will be able to predict the spatial and temporal variability of PM 10 in Cyprus. The core of the forecasting system comprises an appropriately designed neural classification system which clusters synoptic maps, Aerosol Optical Depth data from the Aqua satellite, and ground measurements of particulate matter. By exploiting the above resources, statistical models for forecasting pollution levels were developed.

Satellite imagery has been considered as an add-on tool to air quality and pollution monitoring due to its extensive spatial and temporal coverage of the Earth's surface and atmosphere. Aerosol Optical Depth (AOD) has been extensively used to evaluate and enhance the satellite-based estimates of ground-level particulate matter (PM) as well as to reduce uncertainties in the studies of global health applications. This study attempts to identify correlations between AOD values retrieved from the new MODIS/Aqua high resolution 3km aerosol product and ground-based PM10 measurements obtained within the period 2002-2012 in the area of Athens, Greece. In parallel, it attempts to assess the applicability of the so called mixed effects models which take into account both the spatial and temporal variability of the underlying uncertainties in the estimation of PM10 levels from MODIS AOD measurements. The ground PM10 recordings were acquired from the archive of the in-situ operational air quality monitoring network of Athens. Results indicated that the new AOD product of 3km estimated better PM10 values against the AOD 10km product. Thus, the new 3km product may be better at characterizing aerosol distributions on local scale although bias was observed.

Land Surface Temperature (LST) imagery is necessary for the assessment of the urban thermal environment, an issue of increasing scientific interest due to climate change and urbanization. The problem of the exploitation of the large satellite-derived LST archive is of spatio-temporal nature. The LST input datasets are acquired from various sensors with Thermal Infrared (TIR) bands onboard geostationary (e.g. Meteosat Second Generation MSG viewing Europe and Africa) and near polar orbit (e.g. Terra and Aqua) satellites. The problem of directing and downloading the vast volumes of quarterly-hour datasets to the local users timely and accurately is not solved yet. In particular the geostationary dataset is large. The daily European LST dataset (96 images) volume is 624MB/day, that is 222.4 GB/yr. In total the decadal archive to be searched will be of the order of TerraByte. We investigate the possibility to deploy Space-Data Routers to deliver this task. This is of primary importance for the future development of a global urban observatory with hundreds of users.

Diofantos G. Hadjimitsis, Rodanthi-Elisavet Mamouri, Argyro Nisantzi, Natalia Kouremerti, Adrianos Retalis, Dimitris Paronis, Filippos Tymvios, Skevi Perdikou, Souzana Achilleos, Marios A. Hadjicharalambous, Spyros Athanasatos, Kyriacos Themistocleous, Christiana Papoutsa, Andri Christodoulou, Silas Michaelides, John S. Evans, Mohamed M. Abdel Kader, George Zittis, Marilia Panayiotou, Jos Lelieveld and Petros Koutrakis

In the frame of ‘AIRSPACE’ project, ground-based measurements were conducted in the four main cities of Cyprus. Limassol, comprises the main test site as Lidar and CIMEL sun photometer (NASA/AERONET network) are located at the premises of CUT, while the other cities (Nicosia, Larnaca and Paphos) are used as validation sites. During data collection campaign, measurements from handheld sun-photometers, DustTrak (PM10), Lidar and meteorological stations were used to extract an algorithm for relating satellite MODIS AOD retrievals and ground-based PM10 data for different types of geographical areas. For this purpose, the vertical distribution of atmosphere after the processing of daily lidar signals and meteorological parameters such as relative humidity, wind speed and direction were used.

Aerosol absorption properties are of high importance to assess aerosol impact on regional climate. This study presents an analysis of aerosol absorption products obtained over the Mediterranean basin or land stations in the region from multi-year ground-based AERONET observations with a focus on the Absorbing Aerosol Optical Depth (AAOD), Single Scattering Albedo (SSA) and their spectral dependence. The AAOD and Absorption Angström Exponent (AAE) dataset is composed of daily averaged AERONET level 2 data from a total of 22 Mediterranean stations having long time series, mainly under the influence of urban-industrial aerosols and/or soil dust. This dataset covers the 17-yr period 1996–2012 with most data being from 2003–2011 (~89% of level-2 AAOD data). Since AERONET level-2 absorption products require a high aerosol load (AOD at 440 nm > 0.4), which is most often related to the presence of desert dust, we also consider level-1.5 SSA data, despite their higher uncertainty, and filter out data with an Angström exponent < 1.0 in order to study absorption by carbonaceous aerosols. The SSA dataset includes AERONET level-2 products. Sun-photometer observations show that values of AAOD at 440 nm vary between 0.024 ± 0.01 (resp. 0.040 ± 0.01) and 0.050 ± 0.01 (0.055 ± 0.01) for urban (dusty) sites. Analysis shows that the Mediterranean urban-industrial aerosols appear "moderately" absorbing with values of SSA close to ~0.94–0.95 ± 0.04 (at 440 nm) in most cases except over the large cities of Rome and Athens, where aerosol appears more absorbing (SSA ~0.89–0.90 ± 0.04). The aerosol Absorption Angström Exponent (AAE, estimated using 440 and 870 nm) is found to be larger than 1 for most sites over the Mediterranean, a manifestation of mineral dust (iron) and/or brown carbon producing the observed absorption. AERONET level-2 sun-photometer data indicate a possible East-West gradient, with higher values over the eastern basin (AAEEast = 1.39/AAEWest = 1.33). The North-South AAE gradient is more pronounced, especially over the western basin. Our additional analysis of AERONET level-1.5 data also shows that organic absorbing aerosols significantly affect some Mediterranean sites. These results indicate that current climate models treating organics as nonabsorbing over the Mediterranean certainly underestimate the warming effect due to carbonaceous aerosols.

An urban heat island (UHI) is a phenomenon whereby an urban area experiences elevated air temperatures due to anthropogenic modification of the environment and is usually more evident at night. During heat waves the local effect of an UHI is superimposed on the re‐ gional temperature field and as a result heat stress is enhanced. Both the intensity and the spatial structure of the observed thermal contrast of the UHI depend on various parameters, such as the structure of the urban tissue, the population density and its associated heat re‐ lease, the land use patterns, the vegetation cover, the surface topography and relief etc. In general terms, the UHI is becoming more intense as city sizes increase. Traditional measure‐ ments of the near-surface UHI are based on measurements of the air temperature using ur‐ ban and rural weather stations or air temperature transects. Thermal satellite sensors, which primarily measure the radiance at the top of the atmosphere in the thermal infrared, retrieve the so called land surface temperature (LST) which is the temperature measured at the Earth’s surface and is regarded as its skin temperature. Given that LST is different from the surface air temperature, a distinction is made in remote sensing studies between surface ur‐ ban heat island (SUHI) and atmospheric heat island (e.g., Nichol, 1996).

Abstract. The objective of this study is to investigate the contribution of biomass burning emissions to O 3 production during small-scale dry-grass fires over Western Russia (24 April–10 May 2006) as well as to quantify the effect of biogenic emissions in this environment. By using the Factor Separation methodology, we evaluate the pure contribution of each one of these two sources and we appoint the significance of their synergistic effect on O 3 production. The total (actual) contribution of each source is also estimated. Sensitivity simulations assess the effect of various fire emission parameters, such as chemical composition, emissions magnitude and injection height. The model results are compared with O 3 and isoprene observations from 117 and 9 stations of the EMEP network, respectively. Model computations show that the fire episode determines the sensitivity of O 3 chemistry in the area. The reference run which represents grass fires with high NO x /CO emission ratio (0.06) is characterized by VOC-sensitive O 3 production. In that case, the pure impact of fire emissions on surface O 3 is up to 40–45 ppb, while their synergistic effect with the biogenic emissions is proven significant (up to 8 ppb). Under a lower NO x /CO molar ratio (0.025, representative of agricultural residues), the area is characterized by NO x -sensitive chemistry and the maximum surface O 3 predictions are almost doubled due to higher O 3 production at the fire spots and lower fires' NO emissions.

Data distribution and access are major issues in space sciences as they influence the degree of data exploitation. The project “Space-Data Routers” (SDR) has the aim of allowing space agencies, academic institutes and research centres to share space data generated by single or multiple missions, in an efficient, secure and automated manner. The approach of SDR relies on space internetworking – and in particular on Delay-Tolerant Networking (DTN), which marks the new era in space communications, unifies space and earth communication infrastructures and delivers a set of tools and protocols for space-data exploitation. The project includes the definition of limitations imposed by typical space mission scenarios in which the National Observatory of Athens (NOA) is currently involved, including space exploration, planetary exploration and Earth observation missions. In this paper, we present the mission scenarios and the associated major SDR expected impact from the proposed space-data router enhancements.

Current satellite aerosol retrieval products could be complemented by contrast reduction methods to overcome limitations regarding highly reflective or heterogeneous surfaces such as urban, desert or snow covered areas. Algorithms based on the contrast reduction principle, define contrast loss in an image, inside a pre-determined window size, as an exponential function of the Aerosol Optical Thickness (AOT) difference between two images (a reference and a polluted) acquired under similar observation geometry conditions. This paper presents a contrast reduction algorithm designed for the MODIS sensor, based on the Differential Texture Analysis (DTA) approach. It focuses on algorithm optimization by: a) determining an optimal AOT spatial resolution; b) constraining the relative observation geometry differences between polluted and reference images; and c) assessing the influence of several land cover classes on the accuracy of the retrievals. A comparison of the results obtained for 192 images acquired for the year 2005 with data from five European AERONET stations is performed to assess overall algorithm accuracy as well as the impact of the proposed improvements. Comparative analysis of the results for the various sites showed an optimal algorithm performance for MODIS images using a 39pixel distance window, composed of only forest and urban pixels. Comparison with AERONET AOT data showed a good agreement with a correlation coefficient of 0.78. A similar correlation is found when comparing AERONET measurements and MODIS aerosol standard product. This research supports the establishment of contrast reduction methods as a potential complement to other aerosol retrieval methodologies. Future work will aim at removing the residual aerosol influence from reference images, including BRDFs to better reproduce surface heterogeneity and observation geometry influences and expanding the scope of this study to other AERONET sites so as to further test the algorithm at a global scale.

Abstract. The objective of this study is to investigate the contribution of biomass burning in the formation of tropospheric O3. Furthermore, the impact of biogenic emissions under fire and no fire conditions is examined. This is achieved by applying the CAMx chemistry transport model for a wild-land fire event over Western Russia (24 April–10 May 2006). The model results are compared with O3 and isoprene observations from 117 and 9 stations of the EMEP network, respectively. Model computations show that the fire episode altered the O3 sensitivity in the area. In particular, the fire emissions increased surface O3 over Northern and Eastern Europe by up to 80% (40–45 ppb). In case of adopting a high fire NOx/CO emission ratio (0.06), the area (Eastern Europe and Western Russia) is characterized by VOC-sensitive O3 production and the impact of biogenic emissions is proven significant, contributing up to 8 ppb. Under a lower ratio (0.025), total surface O3 is almost doubled due to higher O3 production at the fire spots and lower fires' NO emissions. In this case as well as in the absence of fires, the impact of biogenic emissions is almost negligible. Injection height of the fire emissions accounted for O3 differences of the order of 10%, both at surface and over the planetary boundary layer (PBL).

There is no widely accepted definition of heat wave. The definition recommended by the World Meteorological Organization and adopted in this research is “when the daily maximum temperature of more than 5 consecutive days exceeds the maximum temperature normal by 5°C”. These periods of abnormally and uncomfortably hot and (usually) humid weather are very common in the Eastern Mediterranean during summer and early autumn. Expert examination of the synoptic patterns on upper air charts can reveal the potential for a heat wave event. In this respect, the research presented here attempts to identify height patterns favorable for heat events by using a neural network classification method, namely, the Kohonen Self Organizing Maps (SOM).

An intense heat wave lasting for several days occurred in Cyprus during August 2010. Record high surface air temperatures were monitored. The aim of this study is to present the intensity and spatial extent of this event for four major districts, based on the analysis of MODIS Aqua satellite images along with surface air temperature data. Emphasis was given in the estimation of the urban heat island intensity and the differences from the respective mean intensity for August 2002–2008.

Interaction of anthropogenic and natural emission sources during a wild-land fire event – influence on ozone formation E. Bossioli, M. Tombrou, A. Karali, A. Dandou, D. Paronis, and M. Sofiev Division of Environmental Physics and Meteorology, Department of Physics, National and Kapodistrian University of Athens, Building PHYS-5, Panepistimioupolis, 15784 Athens, Greece Institute for Environmental Research and Sustainable Development, National Observatory of Athens, I. Metaxa & V. Pavlou, P. Penteli (Lofos Koufou) 15236, Athens, Greece Institute for Space Applications and Remote Sensing, National Observatory of Athens, I. Metaxa & V. Pavlou, P. Penteli (Lofos Koufou) 15236, Athens, Greece Finnish Meteorological Institute, Erik Palmenin aukio 1, P.O. Box 503, 00101 Helsinki, Finland

Atmospheric pollution due to particulate matter (PM) is a continuing issue in many parts of Cyprus. The AIRSPACE project aims at developing a novel methodology based on in-situ observations and multi-platform retrievals, as a tool for monitoring air particulate pollution. High quality PM monitoring at a fine spatial and temporal resolution is required by decision making authorities for taking proper measures and to inform the general public. Observations from Lidar and sun-photometer, satellite AOT retrievals and PM10 - PM2.5 concentrations are considered. Relations between AOT measured from sun-photometers and Lidar aand AOT retrieved from the MODIS sensor are established. A direct comparison between the AOT values retrieved from MODIS and the CIMEL sun-photometer (part of the AERONET NASA Network, which is established at the Cyprus University of Technology) are performed. Finally, the results are coupled with simulations performed by an atmospheric/ chemical model (WRFChem), in order to gain in-depth information of the air pollution situation in Cyprus.

Artificial Neural Networks (ANN) are widely used as diagnostic and predictive tools in atmospheric sciences. This Chapter presents how such practical applications of ANN can be employed in the study of various aspects of a quite complex atmospheric phenomenon as the atmospheric pollution by particulate matter, due to dust transport episodes. It is also discussed how ANN can be utilized in assembling a useful predictive tool for such events. The diagnosis and prediction of dust episodes is very important for human welfare: indeed, some severe health issues are related to the presence of particulate matter in the atmosphere. Also, several human operations are affected by widespread dust presence: indeed, transportation and the increasing use of renewable energy systems utilizing solar radiation are profoundly affected.

The absorption feature approach was used in CHRIS multiangular hyperspectral data in order to investigate its potential for ecosystem remote sensing. For that purpose, CHRIS images in mode 1 were acquired throughout a two-year period for a Mediterranean ecosystem dominated by the semi-deciduous shrub Phlomis fruticosa. During each acquisition, coincident in situ Leaf spectra and ecophysiological measurements (Leaf Area Index, leaf pigment content and leaf water potential) were conducted. After data preprocessing, absorption feature information was calculated for both CHRIS and Leaf spectra for the whole spectrum. Three common characteristic absorption features within the spectral areas 450–550nm, 550–750nm and 900–1000nm were detected. Each spectral area was then examined separately and four characteristic parameters were calculated that described the pattern, magnitude and position of the maximum absorption. Correlations between CHRIS and Leaf spectra for each date and viewing angle (VA) were then conducted. All correlations, either on full continuum removed spectra or on spectral areas, showed high coefficients of determination, especially (i) in higher observation angles (VA +55), (ii) during the wet season and (iii) in strong absorptions such as the “red absorption”. Subsequently, correlations between CHRIS and Leaf absorption parameters of selected spectral areas with field-measured ecophysiological parameters were examined. Ecophysiological parameters proved to be highly correlated to CHRIS and Leaf absorption parameters in magnitude and/or pattern of the absorption feature and less in wavelength of the maximum absorption. CHRIS VAs +/− 36 showed the highest correlations although the type of relation, linear or nonlinear, was not conclusive. Finally, a first comparison between narrowband spectral indices and absorption features in correlations with ecophysiological parameters showed that both methods provide significant and comparable results, with oblique angles showing best performance. However, ecophysiological parameters are generally better predicted linearly by narrowband spectral indices issued from CHRIS, with most significant differences appearing on pigments absorbing mainly within 450–550nm.