Grantee Research Project Results
2001 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, 2000 through September 30, 2001
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. 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 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:
In meteorological models there are two four-dimensional data assimilation (FDDA) methodologies, the nudging or Newtonian relaxation scheme and the four-dimensional variational assimilation (4DVAR) scheme, for assimilating wind, temperature, moisture, and other variables. The nudging scheme 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. 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. 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.
A chemical box model was used as a test bed for the investigation of the two data assimilation methods. This chemical box model has the Carbon Bond IV mechanism and uses the Modified Quasi Steady State Approximation (MQSSA) solver. The initial conditions are based on the IPCC Photochemical Model Intercomparison Study. The investigators have successfully developed the nudging scheme and the tangent linear model and adjoint model for the chemical box model.
Similar to the observation nudging scheme currently used in MM5, the nudging scheme for the chemical box model is developed by adding a distance and time weighted forcing term to the chemistry-transport equations of chemical species. For the box model, only the time weighting is needed and is computed with regard to the observation time and the nudging time window. Sensitivity tests were performed 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 illustrate the complexities of applying data assimilation to chemically reactive non-linear systems. In these isolated chemical kinetic experiments, assimilating O3 improved the predictions for O3, but not the overall quality of the simulation. However, assimilating primary species, such as NOx (NO and NO2) resulted in improved predictions for all species. In reality however, compared to O3, measurements of ambient NOx are quite limited. The accuracy of the nudging method also is sensitive to the nudging coefficient. These preliminary results indicate that the design of the chemical data assimilation system should carefully consider the feedbacks and interactions that arise from complex coupled chemical reactions between various atmospheric species.
The two co-PIs from MCNC and Florida State University have met together to discuss the progress of the development of the tangent linear model and adjoint model for the chemistry box model. Both chemical and meteorological variables are chosen as active variables to facilitate more flexible applications, while the tangent linear model and adjoint model are developed for the chemistry box model The chemical variables include species concentration, reaction rates and other related variables. The investigators tested the correctness of both the tangent linear and adjoint models and found the solution to the linearization problem caused by the use of varying time steps in the box model. They also reduced the computational cost of the adjoint model by increasing a limited amount of memory. The limited-memory quasi-Newton method for minimization was incorporated in the tropospheric chemistry box model.
Future Activities:
The investigators will continue the development of the tangent linear and adjoint models for the chemistry box model to make them more robust. They will test the minimization routine, the tangent linear model, and the adjoint model by performing the ?twin experiment.? A number of experiments will be preformed: (1) assimilating only the precursor species at each step; (2) assimilating the precursor species only during periods where sharp temporal gradients occur (e.g., sunrise and sunset); (3) assimilating only the pseudo-observations for O3 and examining the impact on other NOy species; and (4) assimilating a selective set of reactive hydrocarbon species and analyzing their impact on the quality of model predictions. The data assimilation techniques will be incorporated in a 3-D comprehensive air quality model. The viability of both nudging and 4DVAR approaches will be tested. This work will be documented and presented 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.