Adaptive Nested Models and Cloud Computing for Scientific Simulation

Both environmental and human challenges, such as natural disasters, require scientifically sound simulations of physical phenomena to better understand the past and predict future trends for improved decision support. However, such simulations pose great computing challenges to both Earth and computer sciences. This book addresses those challenges through a series of strategies. Adaptively-coupled nested models are used to resolve the computational challenges and enable the computability of dust storm forecasting by dividing the large geographical area into multiple subdomains with much small area. Cloud computing platforms are adopted and optimized through spatiotemporal patterns to support loosely-coupled nested model execution. This book also investigates and utilizes interoperability technologies to facilitate data access, model input integration, model coupling, and output dissemination and utilization. The book provides a guide to address computing and model interoperability issues that arise when performing scientific model simulation.

Autorentext

Qunying Huang obtained her Ph.D. degree in Geography and Geoinformation Science in August 2011 from George Mason University where she is currently a research assistant professor. She served as cloud platform consultant for enStratus Network Inc., a leading company providing cloud governance and management solutions.