Tightly Coupling Environmental Models with Spatial Analyses in High-Performance Computing and Communications EnvironmentsEPA Grant Number: R825205
Title: Tightly Coupling Environmental Models with Spatial Analyses in High-Performance Computing and Communications Environments
Investigators: Karimi, Hassan A.
Institution: MCNC / North Carolina Supercomputing Center
EPA Project Officer: Saint, Chris
Project Period: October 28, 1996 through October 27, 1999 (Extended to October 27, 2001)
Project Amount: $581,082
RFA: High Performance Computing (1996) RFA Text | Recipients Lists
Research Category: Health , Ecosystems , Environmental Statistics
Description:A great deal of uncertainty in air quality modeling can be attributed to the limited resolution of the stationary grid. Since the size of the grid spanning the domain is limited by computational resources, other approaches are being investigated for finer scale modeling. An adaptive grid model, by clustering grid points to desired regions of the domain, can help reduce uncertainty. Geographic Information Systems (GISs) are used to intersect emissions data within grid cells. An adaptive grid air quality model imposes a time constraint on the emissions processing. In general, with the uniform grids, the emissions are processed once before running the model. On the other hand, with the adaptive grids, the processing at each time step is different and must be completed before the next time step. Another issue that impacts both the air quality modeling and the emissions processing is the method of integrating the adaptive air quality model with emissions processing and spatial analyses. Tight coupling of models with GIS functionality has benefits to the environmental community and can improve the modeling efforts.
This research proposes to investigate all these three areas (adaptive grids, emissions processing, and tight coupling models with GISs). The focus will be on the design and development of parallel algorithms for these activities on high-performance computing and communication (HPCC) resources. This will result in producing efficient algorithms for modeling complex environmental tasks such as the intersection of emissions data onto dynamically adapting grids. The adaptive grid technique will cluster grid points in certain regions of the domain by moving points according to a defined adaptation criteria. The adaptation criteria and parallel algorithm will be refined for air quality modeling by performing exploratory adaptive air quality modeling simulations and incorporating load balancing techniques. One of the unique aspects of this proposal is the consideration of both environmental models and GIS capabilities in HPCC environments. The overall objective in this research proposal is to reduce uncertainty in air quality modeling, develop fast intersection techniques, and provide an environment to make modeling simple and to take advantage of the analytical capabilities of GISs. It is anticipated that the results of this project will benefit the environmental community, the GIS community, and the modeling community at large. Finally, the proposed approach constitutes a great stride in developing techniques for cross-media problem solving.