Day 2 :
Chapman University, USA
Time : 09:35-10:10
Dimitar Ouzounov is an Associate Professor at Chapman University. He works as a Research Scientist in geo-physics, satellite earth observations, and geo-computing. He conducts research on utilizing near-space earth observations for studying geodynamics processes. He contributed in validation of the new geophysical theory of lithosphere-atmosphere-ionosphere coupling in relation to earthquake processes. He has coordinated international initiatives on utilizing space-borne and ground observations for earthquake hazard risk assessments. He has won multiple NASA grants and has published more than 150 papers. He teaches satellite applications in natural hazards at Chapman University. He is a Keynote speaker at international conferences.
We study the impact and effects of different natural and anthropogenic events on atmosphere and ionosphere by using multi-instrument geospace observations. The coupling processes within the system Earth System of Atmosphere-Ionosphere attract more and more attention from the world scientific community. One of the most discussed recently topics in geophysical science is the coupling mechanism, which generates anomalies in different near-Earth shells starting from boundary layer of atmosphere up to magnetosphere of our planet, which was generalized in the form of the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC). We use multi-instrument space-borne observations including NASA/EOS, NOAA/POES, EUMETSAT, CNES/ DEMETER, FORMOSAT-3/COSMIC, as well as ground observations of GPS/TEC and meteorological monitoring data to study changes in Earth troposphere and plasma environment system under various geophysical conditions including natural and anthropogenic disasters. We have found that many of different natural and anthropogenic phenomena contain similarity of their behavior and effects on atmosphere and ionosphere. We are presenting few cases from our analyses, which show the synergetic behavior of different atmospheric and ionospheric parameters related to: (1) Phenomena preceding large earthquake (Wenchuan, China, 2008; L’Aquila, Italy 2009; Tohoku, Japan, 2011); (2) Effects associated with major hurricanes and dust storms; and (3) Radioactive pollution during technological disaster (Fukushima 2011).
Technical University of Cluj-Napoca, Romania
Time : 10:10-10:45
Dorian Gorgan is a Professor in Computer Science Department of the Technical University of Cluj-Napoca, PhD supervisor in Computers and Information Technology, and coordinator of the Computer Graphics and Interactive System Laboratory. The fields of interest involve parallel and distributed processing over HPC infrastructures such as Grid, Cloud, Multicore, and cluster, development of platforms and applications for spatial data processing and visualization, interdisciplinary research in the domains of Earth Sciences and Earth Observations. He has been involved as scientific coordinator and WP leader in national and international research projects such as BIGEARTH, PECSA, enviroGRIDS, IASON, SEE-GRID-SCI, GiSHEO, mEducator, iTRACE, MedioGrid, COMPLEXHPC, and KEYSTONE. He has been member of scientific and reviewing committees of many ISI Journals and international conferences, and gave more than 300 papers and presentations in journals and prestigious conferences in the domains of Computer Science and Earth Observation.
Earth Observation data repositories increase significantly each day, and operations such as storage, processing, management, and nevertheless understanding are nowadays challenges. The new concept of big data concerns with massive data of large diversity, received from heterogeneous sources, in various formats and contents, and requiring high performance computation. The scientific community works on developing performant algorithms for mining such huge repositories in order to classify data in expected categories. The high performance computation infrastructures such as cloud, grids, multicore, clusters, are able more or less to cover the computation requirements for huge distributed data. Moreover we are able to combine the performant software packages with high performance computation resources in order to transform, classify, and highlight significant data. Even so, the analytical capacity of the systems is still limited. Nevertheless the human brain has a much greater analytical and synthetic capacity. The presentation highlights and analyzes some related questions such as: How we could combine in an efficient manner the high computation capacity of the machine with the analytical capacity of the human in the context of multidimensional data? How we could comprehend the flow of data through multiple dimensions when the human is able to perceive data in just a few dimensions? How we could interact with the system in order to control the visual analytics? A few use cases will exemplify the concepts and notions.