Prediction of Maximum Solar Radiation Using Artificial Neural Networks

The prediction of solar radiation is very important for many solar applications. Due to the very nature of solar radiation, many parameters can influence both its intensity and its availability and therefore it is difficult to employ analytical methods for such predictions. For this reason, multivariate prediction techniques are more suitable. In the present research, artificial neural networks are utilised due to their ability to be trained with past data in order to provide the required predictions. The input data that are used in the present approach are those which influence mostly the availability and intensity of solar radiation, namely, the month, day of month, Julian day, season, mean ambient temperature and mean relative humidity (RH). A multilayer recurrent architecture employing the standard back-propagation learning algorithm has been applied. This methodology is considered suitable for time series predictions. Using the hourly records for one complete year, the maximum value of radiation and the mean daily values of temperature and relative humidity (RH) were calculated. The respective data for 11 months were used for the training and testing of the network, whereas the data for the remaining one month were used for the validation of the network. The training of the network was performed with adequate accuracy. Subsequently, the “unknown” validation data set produced very accurate predictions, with a correlation coefficient between the actual and the ANN predicted data of 0.9867. Also, the sensitivity of the predictions to ±20% variation in temperature and RH give correlation coefficients of 0.9858 to 0.9875, which are considered satisfactory. This is considered as an adequate accuracy for such predictions

Downscaling CHIRPS precipitation data: an artificial neural network modelling approach

The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) is a high-resolution climatic database of precipitation embracing monthly precipitation climatology, quasi-global geostationary thermal infrared satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 product, atmospheric model rainfall fields from National Oceanic and Atmospheric Administration–Climate Forecast System (NOAA CFS), and precipitation observations from various sources. The key difference with all other existing precipitation databases is the high-resolution of the available data, since the inherent 0.05° resolution is a rather unique threshold. Monthly data for the period from January 1999 to December 2012 were processed in the present research. The main aim of this article is to propose a novel downscaling method in order to attain high resolution (1 km × 1 km) precipitation datasets, by correlating the CHIRPS dataset with altitude information and the normalized difference vegetation index from satellite images at 1 km × 1 km, utilizing artificial neural network models. The final result was validated with precipitation measurements from the rain gauge network of the Cyprus Department of Meteorology

Comparison of aerosol optical thickness with in situ visibility data over Cyprus

The monitoring of aerosol concentrations comprises a high environmental priority, particularly in urban areas. Remote sensing of atmospheric aerosol optical thickness (AOT) could be used to assess particulate matter levels at the ground. However, such measurements often need further validation. In this study, aerosol data retrieved from satellite and sun-photometer, on the one hand, and visibility data at various locations in Cyprus, on the other hand, for the period from January to June 2009 are contrasted. The results obtained by the direct comparison between MODIS and handheld sun-photometer AOT data exhibited a significant correlation (r=0.83); these results are in agreement with those reported by the National Aeronautics and Space Administration (NASA). The correlation between sun-photometer AOT and that estimated from visibility measurements was also significant (r=0.76). A direct and significant relationship between MODIS AOT and AOT estimated from visibility values was also found for all the locations used (the correlation coefficient was found to vary from 0.80 to 0.84). It is concluded that MODIS AOT data provide accurate information on the aerosol content in Cyprus, while in the absence of such data, visibility measurements could be used as a secondary source of aerosol load information, in terms of aerosol optical thickness, and provide useful information on a near-real time basis, whenever data are availabl

Flood mapping of Yialias river catchment area in Cyprus using Alos Palsar radar images

This study strives to highlight the potential of flood inundation monitoring and mapping in a catchment area in Cyprus (Yialias river) with the use of radar satellite images. Due to the lack of satellite data acquired during dates flood inundation events took place, the research team selected specific images acquired during dates that severe precipitation events were recorded from the rain gauge station network of Cyprus Meteorological Service in the specific study area. The relationship between soil moisture and precipitation was thoroughly studied and linear regression models were developed to predict future flood inundation events. Specifically, the application of fully polarimetric (ALOS PALSAR) and data acquired over different dates for soil moisture mapping is presented. The PALSAR (Phased Array type L-band Synthetic Aperture Radar) sensor carried by the ALOS (Advanced Land Observing Satellite) have quadruple polarizations (HH, VV, HV, VH). The amount of returned radiation (as backscatter echoes) that dictates the brightness of the image depends on factors such as the roughness, size of the target relative to the signal's wavelength, volumetric and diffused scattering. The variation in soil moisture pattern during different precipitation events is presented through soil moisture maps obtained from ALOS PALSAR and data acquired during different dates with different precipitation rates. Soil moisture variation is clearly seen through soil moisture maps and the developed regression models are used to simulate future inundation events. The results indicated the considerable potential of radar satellite images in soil moisture and flood mapping in catchments areas of Mediterranean region. 2012 SPIE

GIS and remote sensing techniques for the assessment of land use change impact on flood hydrology: the case study of Yialias basin in Cyprus

Floods are one of the most common natural disasters worldwide, leading to economic losses and loss of human lives. This paper highlights the hydrological effects of multi-temporal land use changes in flood hazard within the Yialias catchment area, located in central Cyprus. A calibrated hydrological model was firstly developed to describe the hydrological processes and internal basin dynamics of the three major subbasins, in order to study the diachronic effects of land use changes. For the implementation of the hydrological model, land use, soil and hydrometeorological data were incorporated. The climatic and stream flow data were derived from rain and flow gauge stations located in the wider area of the watershed basin. In addition, the land use and soil data were extracted after the application of object-oriented nearest neighbor algorithms of ASTER satellite images. Subsequently, the cellular automata (CA)–Markov chain analysis was implemented to predict the 2020 land use/land cover (LULC) map and incorporate it to the hydrological impact assessment. The results denoted the increase of runoff in the catchment area due to the recorded extensive urban sprawl phenomenon of the last decade

Spatio-temporal variability of desert dust storms in Eastern Mediterranean (Crete, Cyprus, Israel) between 2006 and 2017 using a uniform methodology

The characteristics of desert dust storms (DDS) have been shown to change in response to climate change and land use. There is limited information on the frequency and intensity of DDS over the last decade at a regional scale in the Eastern Mediterranean. An algorithm based on daily ground measurements (PM10, particulate matter ≤10 μm), satellite products (dust aerosol optical depth) and meteorological parameters, was used to identify dust intrusions for three Eastern Mediterranean locations (Crete-Greece, Cyprus, and Israel) between 2006 and 2017. Days with 24-hr average PM10 concentration above ~30 μg/m3 were found to be a significant indicator of DDS for the background sites of Cyprus and Crete. Higher thresholds were found for Israel depending on the season (fall and spring: PM10 > 70 μg/m3, winter and summer: PM10 > 90 μg/m3). We observed a high variability in the frequency and intensity of DDS during the last decade, characterized by a steady trend with sporadic peaks. The years with the highest DDS frequency were not necessarily the years with the most intense episodes. Specifically, the highest dust frequency was observed in 2010 at all three locations, but the highest annual median dust-PM10 level was observed in 2012 in Crete (55.8 μg/m3) and Israel (137.4 μg/m3), and in 2010 in Cyprus (45.3 μg/m3). Crete and Cyprus experienced the same most intense event in 2006, with 24 h-PM10 average of 705.7 μg/m3 and 1254.6 μg/m3, respectively, which originated from Sahara desert. The highest 24 h-PM10 average concentration for Israel was observed in 2010 (3210.9 μg/m3) during a three-day Saharan dust episode. However, a sub-analysis for Cyprus (years 2000-2017) suggests a change in DDS seasonality pattern, intensity, and desert of origin. For more robust conclusions on DDS trends in relation to climate change, future work needs to study data over several decades from different locations

Responses of schoolchildren with asthma to recommendations to reduce desert dust exposure: Results from the LIFE-MEDEA intervention project using wearable technology

Current public health recommendations for desert dust storms (DDS) events focus on vulnerable population groups, such as children with asthma, and include advice to stay indoors and limit outdoor physical activity. To date, no scientific evidence exists on the efficacy of these recommendations in reducing DDS exposure. We aimed to objectively assess the behavioral responses of children with asthma to recommendations for reduction of DDS exposure. In two heavily affected by DDS Mediterranean regions (Cyprus & Crete, Greece), schoolchildren with asthma (6-11 years) were recruited from primary schools and were randomized to control (business as usual scenario) and intervention groups. All children were equipped with pedometer and GPS sensors embedded in smartwatches for objective real-time data collection from inside and outside their classroom and household settings. Interventions included the timely communication of personal DDS alerts accompanied by exposure reduction recommendations to both the parents and school-teachers of children in the intervention group. A mixed effect model was used to assess changes in daily levels of time spent, and steps performed outside classrooms and households, between non-DDS and DDS days across the study groups. The change in the time spent outside classrooms and homes, between non-DDS and DDS days, was 37.2 min (pvalue = 0.098) in the control group and -62.4 min (pvalue < 0.001) in the intervention group. The difference in the effects between the two groups was statistically significant (interaction pvalue < 0.001). The change in daily steps performed outside classrooms and homes, was -495.1 steps (pvalue = 0.350) in the control group and -1039.5 (pvalue = 0.003) in the intervention group (interaction pvalue = 0.575). The effects on both the time and steps performed outside were more profound during after-school hours. To summarize, among children with asthma, we demonstrated that timely personal DDS alerts and detailed recommendations lead to significant behavioral changes in contrast to the usual public health recommendations

Reviews and perspectives of high impact atmospheric processes in the Mediterranean

The Mediterranean region is a unique area characterized by a large spectrum of atmospheric phenomena, some of which have a high impact on many aspects of human activities, safety and wellbeing. The area is long considered as a hot spot of such atmospheric phenomena deserving multidisciplinary scientific attention. The scientific research that has been carried out on these high impact atmospheric processes that occur in the Mediterranean area is indeed widespread and the available international literature is very extensive. The paper touches initially the temperature and precipitation regimes, followed by a discussion of floods and droughts. The exciting cyclogenetic patterns of explosive cyclones and medicanes are presented in separate sections. The lightning activity and the presence of dust and other pollutants are also presented herein. The atmospheric chemistry of the region which is increasingly becoming of utmost importance for the area under study is distinctly discussed. Attempts to modify the weather (the precipitation, in particular) are outlined too. The effects of climatic change on various atmospheric processes are considered throughout this paper, in addition to a dedicated section on temperature and precipitation

Remote sensing of environment - integrated approaches

This book covers the latest developments in remote sensing theory and applications by numerous researchers, experts and collaborators of the Remote Sensing and Geo-Environment Lab of the Department of Civil Engineering and Geomatics of the Cyprus University of Technology. The main highlight of this book is combination of several techniques such as satellite remote sensing, field spectroscopy, smart sensors, ground techniques for achieving an integrated method for the systematic monitoring of the environment