1. Title: Atmospheric Hazard Prediction
(An Emergency Response and Assessment System Coupling GIS with Atmospheric Hazard Prediction Models)

2. Lead Presenter: David Wong
Earth Systems and GeoInformation Sciences Program
School of Computational Sciences
George Mason University
4400 University Drive, 5C3
Fairfax, VA 22030
Tel: 703-993-1212
Fax: 703-993-1993
Email: dwong2@gmu.edu 

3. Project Description:

The ability to obtain accurate predictions of a variety of atmospheric releases (accidental or intentional, man-made or nature) relies on our understanding of the atmosphere/ocean/land-surface system, and the collection of data describing different components of the system. These datasets, in remote sensing, GIS or other formats (in-situ data), have to be assembled effectively together to provide a coherent picture of the relevant problems. Utilizing atmospheric dispersion models with new computational power, Earth observing data systems, and telecommunication capabilities to extract (in real time) current and archival remote sensing data available from a variety of means can support the development of highly accurate prediction systems. This modeling approach relies on coupling current modeling capabilities with GIS and remote sensing system environments.

The Comprehensive Atmospheric Modeling Program (CAMP) at GMU has the expertise in atmospheric dispersion and transport modeling, and has provided research support to a variety of agencies, including DTRA, in past six years. The Center for Earth Observing and Space Research (CEOSR) and the Earth Systems and GeoInformation Sciences Program (ESGS) at GMU have expertise in several research areas: data fusion of heterogeneous geospatial data in both GIS and remote sensing formats, almost “real-time” access to atmospheric remote sensing data, high-performance Internet GIS, and spatial statistical analysis and modeling. This project is our effort to integrate atmospheric modeling and high resolution CFD runs into remote sensing systems and GIS to develop an emergency response system with most up-to-date data, and offer a rapid impact assessment environment to support homeland defense initiatives.

In the system, we combine real-time remote sensing systems, existing remote sensing databases, conventional weather observational databases and regional vector GIS datasets. Data with reasonable temporal stability, such as that for population, landscape characteristics and regional infrastructure, are pre-processed and integrated within the GIS. High resolution datasets, such aerial photos, IKONOS, LIDAR are important. Local area terrain and man-made configuration information such as building shapes in the 3-D urban model are included. Data of more time sensitive nature, including atmospheric and meteorological conditions, are needed. Real-time remote sensing datasets and distributed online data information system are used to derive current parameters. These data will be fed to the GIS as “real-time” data and serve as the inputs to atmospheric dispersion models, which would be now coupled with GIS. The model outputs are visualized in GIS to show the spread of materials, biological or chemical, and can support more detailed impact assessment for effective responses. The results can be accessed through the Internet following a system-wide protocol. The final results could be visualized by a GIS based interface, tailored to particular users/agencies.

The system under development is extremely valuable to mitigate hazardous material releases into the atmosphere or regional natural hazardous response (such as the recent hurricane Isabel). The significance of the project is that different data resources, including “real-time” remote sensing data, can be integrated to provide model inputs. Analysis methodologies (such as statistical, topological, spatial-temporal methods) from atmospheric and dispersion research are integrated for providing logical assessment.

4. Funding Sources:
NASA, Defense Threat Reduction Agency/DOD
Collaboration with with Zafer Boybeyi, Chaowei Yang, Ruixin Yang, and Menas
Kafatos.