Investigation of Four-Dimensional Data Assimilation Methodologies for Air Quality ModelsEPA Grant Number: R827113
Title: 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: Shapiro, Paul
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 , Engineering and Environmental Chemistry
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 design of effective abatement strategies. While data assimilation methods have been widely used 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 (1) to develop useful insights into the relatively unexplored, though potentially beneficial, area of chemical data assimilation; and (2) to improve the predictive capability of atmospheric chemistry-transport models through the systematic investigation of the potential use of chemical data assimilation methods.
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; these to a great extent arise from the fact that atmospheric chemistry is a highly nonlinear system that exhibits significant coupling between various species. Recognizing these limitations in the science of chemical data assimilation, we intend to synthesize the current knowledge of modern data assimilation methods as used in meteorological modeling and identify potentially extensible methods for atmospheric chemistry-transport models; conduct a systematic investigation of the applicability of the potential methods to atmospheric chemistry models; and investigate the development of a computationally viable data assimilation methodology for practical applications.
The successful extension of data assimilation techniques to tropospheric chemistry problems will represent a significant advancement in the science of data assimilation. Improvements in predictive capability of the model gained through chemical data assimilation coupled with in-depth analysis of model results will help identify deficiencies in process representations and physical parameterizations employed in the model. The development of the variational approach will directly contribute to advancement in the state of science in sensitivity analysis and parameter estimation. The ability to assimilate chemical species in air quality models will contribute toward the science of real-time forecasting of O3 air quality.
Improvement in Risk Assessment and Management: The data assimilation methodologies which will be investigated in the proposed research will improve the predictive capability of air quality models. This will improve our current understanding of the physical and chemical processes that interact to determine the fate of anthropogenic emissions in the atmosphere. These improvements will help reduce uncertainties associated with risk assessment and management studies utilizing results from such models.