Parameterization of urban sub-grid scale processes in global atmospheric chemistry models

Joint Program Reprint • Journal Article
Parameterization of urban sub-grid scale processes in global atmospheric chemistry models
Calbo, J., W. Pan, M. Webster, R.G. Prinn and G. McRae (1998)
Journal of Geophysical Research, 103(D3): 3437-3467

Reprint 1998-1 [Download]

Abstract/Summary:

We have derived a parameterization consisting of a set of analytical expressions that approximate the predictions by the CIT Urban Airshed Model for the net export to the environment (i.e., effective emissions) of several chemical species, as functions of fourteen input parameters. For each species, effective emissions are a function of actual urban emissions of this and other species and of other urban domain properties such as meteorology. Effective emissions may be "aged" emissions of primary pollutants or actual production of secondary pollutants. To develop the parameterization we have applied the probabilistic collocation method, which uses the probability density functions of the inputs to generate a set of orthogonal polynomials. These polynomials are then used as the basis for a polynomial chaos expansion that approximates the actual response of the CIT model to its inputs. We assume that seasonal variations can be represented by sinusoidal functions. The parameterization provides a computationally very efficient simulation of the actual model behavior. We have compared the outputs of the parameterization with the outputs of the CIT model, and we conclude that it gives a quite good approximation for effective emissions, at least in the regions of highest probability of the input parameters. This parameterization is applicable to detailed uncertainty and sensitivity analyses and enables computationally efficient inclusion of urban-scale processes as sub-grid scale phenomena in global-scale models.

© 2000 American Geophysical Union

Citation:

Calbo, J., W. Pan, M. Webster, R.G. Prinn and G. McRae (1998): Parameterization of urban sub-grid scale processes in global atmospheric chemistry models. Journal of Geophysical Research, 103(D3): 3437-3467 (http://www.agu.org/journals/jd/)
  • Joint Program Reprint
  • Journal Article
Parameterization of urban sub-grid scale processes in global atmospheric chemistry models

Calbo, J., W. Pan, M. Webster, R.G. Prinn and G. McRae

1998-1
103(D3): 3437-3467

Abstract/Summary: 

We have derived a parameterization consisting of a set of analytical expressions that approximate the predictions by the CIT Urban Airshed Model for the net export to the environment (i.e., effective emissions) of several chemical species, as functions of fourteen input parameters. For each species, effective emissions are a function of actual urban emissions of this and other species and of other urban domain properties such as meteorology. Effective emissions may be "aged" emissions of primary pollutants or actual production of secondary pollutants. To develop the parameterization we have applied the probabilistic collocation method, which uses the probability density functions of the inputs to generate a set of orthogonal polynomials. These polynomials are then used as the basis for a polynomial chaos expansion that approximates the actual response of the CIT model to its inputs. We assume that seasonal variations can be represented by sinusoidal functions. The parameterization provides a computationally very efficient simulation of the actual model behavior. We have compared the outputs of the parameterization with the outputs of the CIT model, and we conclude that it gives a quite good approximation for effective emissions, at least in the regions of highest probability of the input parameters. This parameterization is applicable to detailed uncertainty and sensitivity analyses and enables computationally efficient inclusion of urban-scale processes as sub-grid scale phenomena in global-scale models.

© 2000 American Geophysical Union

Supersedes: 

Parameterization of Urban Sub-grid Scale Processes in Global Atmospheric Chemistry Models