G
George Kallos
Researcher at National and Kapodistrian University of Athens
Publications - 215
Citations - 7935
George Kallos is an academic researcher from National and Kapodistrian University of Athens. The author has contributed to research in topics: Mineral dust & Atmospheric model. The author has an hindex of 42, co-authored 215 publications receiving 7265 citations. Previous affiliations of George Kallos include Georgia Institute of Technology & United States Naval Academy.
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A model for prediction of desert dust cycle in the atmosphere
TL;DR: In this article, an integrated modeling system has been developed to accurately describe the dust cycle in the atmosphere, based on the SKIRON/Eta modeling system and the Eta/NCEP regional atmospheric model.
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Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain
TL;DR: The analysis of PM10 and TSP levels recorded in rural areas from Southern and Eastern Spain (1996-1999) shows that most of the peak events are simultaneously recorded at monitoring stations up to 1000 km apart as mentioned in this paper.
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Photooxidant dynamics in the Mediterranean basin in summer: Results from European research projects
TL;DR: In this paper, the authors present a summary of the documented, as well as the postulated, processes involved in the MECAPIP and RECAPMA projects, and show that stacked layer systems form along the Spanish Mediterranean coasts, 2-3 km deep and more than 300 km wide, with the most recent layers at the top and the older ones near the sea.
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African dust contributions to mean ambient PM10 mass-levels across the Mediterranean Basin
Xavier Querol,Jorge Pey,Marco Pandolfi,Andrés Alastuey,Michael Cusack,Noemí Pérez,Teresa Moreno,Mar Viana,Nikos Mihalopoulos,George Kallos,S. Kleanthous +10 more
TL;DR: In this article, mass-levels of PM 10 measured at regional background sites across the Mediterranean Basin, available from Airbase (European Environmental Agency) and from a few aerosol research sites, are compiled.
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Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering
P. Louka,P. Louka,George Galanis,George Galanis,N. Siebert,Georges Kariniotakis,Petros Katsafados,Ioannis Pytharoulis,Ioannis Pytharoulis,George Kallos +9 more
TL;DR: The results obtained showed a remarkable improvement in the model forecasting skill, with a significant reduction of the required CPU time in the case of wind power prediction.