Grantee Research Project Results
Final 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 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:
The objectives of this research project were to: (1) develop useful insights into the relatively unexplored, though potentially beneficial, area of chemical data assimilations; and (2) improve the predictive capability of comprehensive Eulerian atmospheric chemistry-transport models (CTMs) through the systematic investigation of the potential use of chemical data assimilation methods. Comprehensive atmospheric CTMs 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 these methods to tropospheric chemistry problems has been rather limited. Data assimilations of chemical species observations within such models could allow knowledge of pollutant transport and chemistry to be incorporated in the analysis procedures and facilitate propagation of information from asynoptic observations to model predictions, consequently improving the skill of the model.
Summary/Accomplishments (Outputs/Outcomes):
CTMs that treat, in detail, the various physical and chemical processes governing the fate of atmospheric pollutants in the troposphere, have played a central role in the design of environmental policies and in a variety of risk-assessment studies. Although significant strides have been made over the past 2 decades to improve the representation of various physical and chemical processes and numerical methods employed in such models, there still exists a need to improve their predictive capabilities, because the next generation of air quality simulation models needs to address the increasingly complex multipollutant issues emerging in new model applications.
Data assimilation techniques have proven very useful in predicting atmospheric dynamics, but their use in atmospheric chemistry models has been rather limited. The extension of data assimilation methods to atmospheric CTMs of the troposphere, however, is not a simple task. First, measurements of chemical constituents in the troposphere have been relatively sparse in comparison to the well-established observation networks for meteorological parameters. Second, tropospheric chemistry-transport calculations generally focus on short-term trends, involve species with relatively shorter atmospheric lifetimes as compared to longer-lived stratospheric species of interest, and are, to a greater extent, dependent on precursor emissions and deposition processes, as opposed to initial values. Finally, the nonlinear coupling of chemical species, combined with the lack of speciated measurements, poses additional difficulties and places further requirements on the assimilation methodology to maintain more stringent mass conservation and chemical equilibrium in the model. Despite the shortcomings discussed above, the data assimilation of chemical species in tropospheric CTMs holds tremendous potential for improving the predictive capability of the models, thereby improving our current understanding of the physical and chemical processes that interact to determine the fate of anthropogenic emissions in the atmosphere.
There are two most widely used four-dimensional data assimilation (FDDA) methodologies, the nudging or Newtonian relaxation scheme and the four-dimensional variational (4DVAR) assimilation scheme, for assimilating observation data into numerical models. The nudging scheme relaxes the model state towards observations during the assimilation period by adding nonphysical diffusive-type terms to the prognostic equations based on the differences between the model and the observations. The 4DVAR uses the variational method to define the data assimilation problem as an optimization problem, with the numerical model itself as a strong constraint. Because of the complex nature of the 4DVAR for dealing with large dimension problems, the adjoint technique is introduced to efficiently calculate the gradient of a cost or distance function with respect to a control variable, which could be a result of initial conditions and/or model parameters.
During this research project, we investigated and developed chemical data assimilation methodologies and gained useful insights into this relatively unexplored area of research. We first used a chemical box model as a test bed to develop the nudging scheme and the variational assimilation scheme. We then applied the nudging scheme to a three-dimensional atmospheric chemistry transport modeling system and tested the methodologies with pseudo-observations and real measurement data to illustrate how data assimilation techniques can reduce errors in model results.
A variety of experiments of data assimilations of O3 and its precursors using the nudging scheme in the box model indicated that: (1) assimilating only O3 concentration helps improve the O3 simulation, but does not help to improve secondary nitrogen oxidation products, such as HNO3 and plant available nitrogen; and (2) assimilating NOx can significantly improve the overall quality of the simulations. The development and application of the nudging scheme in the three-dimensional atmospheric chemistry modeling system provide the environment for us to investigate the impact of data assimilations on many aspects of model predictions. Under the conditions simulated for a case study during the summer of 1996, using a 4-km resolution modeling domain covering North Carolina, assimilation of surface O3 measurements had little impact on domain-wide daytime O3 predictions, but helped reduce nighttime over predictions in O3 concentrations. In addition, assimilating O3 measurements aloft helps to reduce the bias in predicted aloft levels of O3 and helps to reduce the bias in surface predictions as O3 aloft mixes down as the boundary layer grows in the morning and in the daytime. In several twin experiments, with the two important O3 precursors, NOx and isoprene, we learned that assimilating both NOx and isoprene results in reducing the bias and gross errors of predicted O3 and points to the potential benefits of having regional measurements of precursor species available. The availability of such measurements also can help estimate and reduce uncertainties in existing emission inventories of these precursor species through extensions to the chemical data assimilation methodologies.
The investigation and application of the 4DVAR in the chemical box model has given us useful insights into these mathematically and computationally complex and challenging methodologies.
The successful extension of data assimilation techniques to tropospheric chemistry problems represents a significant advancement in the science of data assimilation. Improvements in the predictive capability of the model gained through chemical data assimilation, coupled with indepth analysis of model results, help to identify deficiencies in process representations and physical parameterizations employed in the model. The development and application of the variational approach contributes directly to the advancement in the state of science in sensitivity analysis and parameter estimation. The ability to assimilate chemical species in air quality models also contributes toward the science of real-time forecasting of O3 air quality. The improvements in the predictive capability of the models help to reduce uncertainties associated with risk-assessment studies utilizing results from such models. Furthermore, the methodologies and results of this research project also can be extended to have significant impacts on the ongoing U.S. Environmental Protection Agency and National Oceanic and Atmospheric Administration program of real-time air quality forecasting. A journal article disseminating the results of the study will be forthcoming.
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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Type | Citation | ||
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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, real-time air quality forecasting, air pollution modeling system, air pollution models, air quality modeling, assimilation methodologies, atmospheric chemistry transport models, chemical kinetics, chemical transport models, four-dimensional data, pollutant transport., 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.
Project Research Results
- 2003
- 2002 Progress Report
- 2001 Progress Report
- 2000 Progress Report
- 1999 Progress Report
- Original Abstract
1 journal articles for this project