Grantee Research Project Results
1999 Progress Report: Investigation of Four-Dimensional Data Assimilation Methodologies for Air Quality Models
EPA Grant Number: R827113Title: Investigation of Four-Dimensional Data Assimilation Methodologies for Air Quality Models
Investigators: Xiu, Aijun , Hanna, Adel , Mathur, Rohit , Zou, Xiaolei
Institution: MCNC / North Carolina Supercomputing Center , Florida State University
EPA Project Officer: Hahn, Intaek
Project Period: October 1, 1998 through September 30, 2001 (Extended to November 8, 2003)
Project Period Covered by this Report: October 1, 1998 through September 30, 1999
Project Amount: $371,737
RFA: Exploratory Research - Physics (1998) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Land and Waste Management , Air , Safer Chemicals
Objective:
Comprehensive atmospheric chemistry-transport models have played a central role in both research and policy development. Emerging multipollutant issues place additional demands on the role of such models in the design of effective abatement strategies. Although data assimilation methods have been used widely to improve the skill of meteorological models, the application of such methods to tropospheric chemistry problems has been rather limited. Data assimilation of chemical species observations within such models could allow knowledge of pollutant transport and chemistry to be incorporated in the analysis procedure and facilitate propagation of information from asynoptic observations to model predictions, consequently improving the model's skill. The primary objectives of the proposed research are to: (1) develop useful insights into the relatively unexplored, though potentially beneficial, area of chemical data assimilation; and (2) improve the predictive capability of atmospheric chemistry-transport models through the systematic investigation of the potential use of chemical data assimilation methods.Progress Summary:
The implementation of chemical data assimilation, like meteorological data assimilation, requires a detailed understanding of the physics, the error mechanisms (both in the process representations and the observed data), and the possible algorithmic simplifications to reduce computational demands of the methodology. The assimilation of chemical trace species observations, however, poses new problems and challenges. To a great extent, these challenges arise from the fact that atmospheric chemistry is a highly nonlinear system that exhibits significant coupling between various species. To evaluate different four-dimensional data assimilation (FDDA) methodologies currently available and to understand the problems and issues related to data assimilation in air quality models, all the investigators from MCNC and Florida State University met during the summer of 1999. We discussed the formulation of the variational assimilation technique for atmospheric chemistry problems. One outcome of this meeting was to perform preliminary evaluation of the method for selected box-model test cases, for which the tangent linear and adjoint models of the tropospheric chemistry box-model have first to be developed.A comprehensive literature review on data assimilation techniques and their application to meteorological and air quality modeling has been performed. As a first step of the project, a tropospheric chemistry box-model has been developed and tested to provide a base model for investigation of different FDDA methodologies. The development of the tangent linear and adjoint models as well as the nudging method is under way.
We have started a survey of possible observational data that can be used in the air quality FDDA investigations. Data available from the Aerometric Information Retrieval System (AIRS) provide good coverage of the continental United States, which represents most of the domain of our air quality modeling investigation. Measurements of ambient air quality for the past 5 years from 4,000 monitoring sites across the Nation are available. The AIRS data are updated monthly with new information. With the hourly raw data available, we created a test database for August 1995, for air quality parameters such as CO, O3, SO2, NO, NO2, and NOx.
Future Activities:
We will continue to work on the development of the nudging method and the tangent linear and adjoint models based on the chemistry box-model. We will investigate the AIRS data further to learn the error mechanisms in the observations, which are critical for data assimilation. Such analyses will provide insight on appropriate methodologies for air quality data that have different characteristics from meteorological data especially related to mass conservation and the spatial and temporal distributions.Journal Articles on this Report : 1 Displayed | Download in RIS Format
Other project views: | All 5 publications | 1 publications in selected types | All 1 journal articles |
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Mathur R, Young JO, Schere KL, Gipson GL. A comparison of numerical techniques for solution of atmospheric kinetic equations. Atmospheric Environment 1998;32(9):1535-1553. |
R827113 (1999) R827113 (Final) |
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Supplemental Keywords:
meteorology, photochemistry, data nudging, adjoint method, air quality, modeling., RFA, Scientific Discipline, Air, Physics, Environmental Chemistry, Atmospheric Sciences, tropospheric ozone, Engineering, Chemistry, & Physics, air quality modeling, air pollution modeling system, pollutant transport, assimilation methodologies, air pollution models, chemical transport model, photochemistry, chemical kinetics, four dimensional data, meterology, real time monitoring, atmospheric chemistry transport modelsProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.