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This study focuses on the assessment of surface solar radiation (SSR) based on operational Neural Network (NN) and Multi-Regression Function (MRF) modelling techniques that produce instantaneous (in less than one minute) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring Service (CAMS), respectively, these models are capable of calculating SSR in high resolution (1 nm, 0.05 degrees, 15 min) that can be used for spectrally-integrated irradiance maps, databases and various applications related with energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15-min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range -15 to 15 % for the NN that produces spectral irradiances (NNS), 5-6 % underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from -100 to 40 W/m2 and -20 to 20 W/m2, for the 15-min and monthly mean Global Horizontal Irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10% as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications, which are related with the solar energy production planning and use. [ABSTRACT FROM AUTHOR]

ERA-PLANET is a wide European network comprised of 118 researchers from 35 partner institutions located in 18 countries, aiming to strengthen the European Research Area in the domain of Earth Observation (EO) in coherence with the European participation to the Group on Earth Observation (GEO) and the program for the establishment of a European capacity for Earth Observation, COPERNICUS. It will provide more accurate, comprehensive, and authoritative information to policy and decision-makers in key societal benefit areas (SBAs), under the umbrellas of dedicated projects in the topics of: smart cities and resilient societies; resource efficiency and environmental management; global changes and environmental treaties; polar areas and natural resources. ERA-PLANET will provide advanced decision support tools and technologies aimed to better monitor our global environment and share the information and knowledge in different domains of EO by launching joint transnational calls along the above four strands. The concept of the project that tackles with strand 1, as well as an example of a specific application fitting in, are described, aspiring to promote and coordinate the "smart-city" approach into a European network of cities and non-European follower cities, serving the need for a common approach to enhance environmental and societal resilience to air pollution, urban growth, and urban heat islands, as well as other natural/manmade stresses and relevant impacts. This is achieved through the synergy among technology, government, and society, while at the same time creating bridges between local/national initiatives with GEO/GEOSS, COPERNICUS, and other smart cities and GEO relevant projects. The project addresses initiatives in European cities but also specific issues dealing with air quality management in other parts of the world. Finally, it places major emphasis on fully exploiting key-enabling technologies and firmly addressing interoperability issues, in the context of big "smart city" data, and open science. [ABSTRACT FROM AUTHOR]

The downscaling of geostationary diurnal thermal data can ease the lack of land surface temperature (LST) datasets that combine high spatial and temporal resolution. However, the downscaling of diurnal LST data is more demanding than single scenes. This is because the spatiotemporal interrelationships of the original LST data have to be preserved and accurately reproduced by the downscaled LST (DLST) data. To that end, LST disaggregation kernels/predictors that provide information about the spatial distribution of LST during different times of a day can prove especially useful. Such LST predictors are the LST Annual Cycle Parameters (ACPs). In this work, multitemporal ACPs are employed for downscaling daytime and nighttime ~4 km geostationary LST from SEVIRI (Spinning Enhanced Visible and Infrared Imager) down to 1 km. The overall goal is to assess if the use of the ACPs can improve the estimation of the diurnal range of DLST (daytime DLST minus nighttime DLST). The evaluation is performed by comparing the DLST diurnal range maps with reference data from MODIS (Moderate Imaging Spectroradiometer) and also with data retrieved from a modified version of the TsHARP (Thermal Sharpening) algorithm. The results suggest that the ACPs increase the downscaling performance, improve the estimation of diurnal DLST range and produce more accurate spatial patterns. [ABSTRACT FROM AUTHOR]