Grantee Research Project Results
2000 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, 1999 through September 30, 2000
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 design of effective abatement strategies. Although 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 this research project 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:
There are two four-dimensional data assimilation (FDDA) methodologies for assimilating wind, temperature, moisture, and other variables in meteorological models, such as in MM5. One of them is the nudging or Newtonian relaxation technique that relaxes the model state toward observations during the assimilation period, by adding to the prognostic equations non-physical diffusive-type terms based on the difference between the model and observations. Another technique is the four-dimensional variational assimilation (4DVAR) scheme that uses the variational method to define the data assimilation problem as an optimization problem with the numerical model itself as a strong constraint. Due to 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 can be initial conditions and/or model parameters.To identify implementation issues associated with the development of different chemical FDDA methodologies, we developed the nudging scheme and the adjoint model for a chemical box model, that is used as a test-bed for initial testing and investigations. In the chemical box model, the Carbon Bond IV mechanism and the Modified Quasi Steady State Approximation (MQSSA) solver are used. The initial conditions are based on the IPCC Photochemical Model Inter-comparison Study.
The nudging scheme for the box model is developed based on modification to the observation nudging scheme currently used in MM5. We performed sensitivity tests within the chemical box model with different data assimilation strategies, such as perturbing different species or their combinations, nudging different species or their combinations, and testing different nudging coefficients. Sensitivity tests of the nudging scheme in the chemistry box model show that for the given set of chemical conditions, assimilating only O3 improved predictions for O3 but not the overall quality of simulations with respect to other species (e.g., NOy). Assimilating primary species, NO and NO2, improves predictions for all species considered. The accuracy of the nudging method is sensitive to the nudging coefficient.
In the course of developing the tangent linear and adjoint model for the chemistry box model, we have chosen both chemical and meteorological variables as active variable to facilitate more flexible applications. The chemical variables include species concentration, reaction rates and other related variables. The adjoint model is a very useful tool for data assimilation, sensitivity analysis, and model parameter estimation. Developing an adjoint model generally includes the following procedures: (1) analyze thoroughly the entire model and decide active variables and active routines; (2) develop tangent linear code for each active routine; (3) develop adjoint code for each active routine; and (4) perform tangent linear and adjoint code correctness check for each active routine and the whole model system.
Future Activities:
We will perform additional box model calculations and assimilation to identify the set of species critical for success of chemical data assimilation under varying chemical conditions and the optimal set of parameters (e.g., nudging coefficient, assimilation window). We will incorporate the data assimilation techniques in a three-dimensional comprehensive air quality model. We also will test the viability of both nudging and 4DVAR approaches. We will document and present this work in conferences and journal articles.Journal Articles:
No journal articles submitted with this report: View all 5 publications for this projectSupplemental Keywords:
meteorology, photochemistry, data nudging, adjoint method., 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.