Assimilation of Remote-Sensing Data Into a Coupled Hydrological/Meteorological Modeling System Using Parallel TechniquesEPA Grant Number: R825210
Title: Assimilation of Remote-Sensing Data Into a Coupled Hydrological/Meteorological Modeling System Using Parallel Techniques
Investigators: McHenry, John , Peters-Lidard, Christa
Institution: MCNC / North Carolina Supercomputing Center , Georgia Institute of Technology
Current Institution: MCNC / North Carolina Supercomputing Center
EPA Project Officer: Klieforth, Barbara I
Project Period: December 2, 1996 through December 1, 1999 (Extended to December 1, 2001)
Project Amount: $583,414
RFA: High Performance Computing (1996) RFA Text | Recipients Lists
Research Category: Health , Ecosystems , Environmental Statistics
Realistic representation of surface characteristics and associated processes is crucial because surface processes control the depth of the simulated atmospheric boundary layer in meteorological models, and the simulated surface and subsurface transport of water in hydrological models. Many air quality simulation models used for assessment studies rely on mesoscale meteorological models which oversimplify the near-surface exchange processes. Also, many hydrological models, those that drive surface or sub-surface water quality models, oversimplify these surface processes. We recognize the need for cross-media modeling efforts designed to overcome some of these limitations. In addition, methods of assimilating observational data into meteorological and hydrological models have become popular among the developers of these models. However, simplified representations of surface characteristics and associated processes have not allowed the models to make efficient use of much of the observational data that has and is becoming available, particularly remotely-sensed data. High Performance Computing (HPC) advances are now making it possible to explore some of these problems in a great detail. The combination of multi-processor vector machines, massively parallel processors, and parallelism can be exploited from networks of workstations creating an opportunity to couple single-media models and explore data assimilation issues at the same time.
Our goal is to assimilate remote-sensing data into a coupled hydrological/meteorological modeling system through the application of HPC parallel techniques, in order to improve the predictive capabilities and practical usability of the system. For this purpose we will use a Data Assimilation Version of the TOPMODEL-based Land-Atmosphere Transfer Scheme (TOPLATS/AM), a fine scale surface hydrology model. We will couple this to the Mesoscale Model Version 5 (MM5), in order to improve the representation of sub-grid scale heterogeneities that are responsible for significant errors in both models. We will then incorporate remote-sensing data into the weakest points of the coupled model system, targeting critical known predictive deficiencies, including precipitation and radiation. Further, we will use HPC parallel computational techniques to improve the approaches to data assimilation and coupling of the models on temporal and spatial scales. We will explore the possibility of Rscaling upS so that the techniques devised could be made applicable over a broad mesoscale simulation. We will use the hydrology model to demonstrate lateral land-surface transport which is valuable for cross-media environmental assessments, particularly when precipitation may contain potential environmental contaminants.
From a risk reduction standpoint, realistic modeling of the exchange of heat and moisture near the earthUs surface is critical Until recently, model-based environmental assessments have examined single-media issues; i.e., either air or water quality. Though coupled model projects are underway, they face serious challenges. Our research will significantly advance knowledge of this difficult problem. Further, we recognize that the holistic nature of eco-systems, or whole environments, requires that next-generation models address the assessment of risk from a holistic standpoint. This will allow the multiple, cross-media effects of deleterious anthropogenic activities on the environment to be assessed and understood from multiple vantage points, permitting more effective and lasting remediation with consequent reduction of risk to humans and ecosystems.