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Simulating lightning NO production in CMAQv5.2: evolution of scientific updates

Authors :
D. Kang
K. E. Pickering
D. J. Allen
K. M. Foley
D. C. Wong
R. Mathur
S. J. Roselle
Source :
Geoscientific Model Development, Vol 12, Pp 3071-3083 (2019)
Publication Year :
2019
Publisher :
Copernicus Publications, 2019.

Abstract

This work describes the lightning nitric oxide (LNO) production schemes in the Community Multiscale Air Quality (CMAQ) model. We first document the existing LNO production scheme and vertical distribution algorithm. We then describe updates that were made to the scheme originally based on monthly National Lightning Detection Network (mNLDN) observations. The updated scheme uses hourly NLDN (hNLDN) observations. These NLDN-based schemes are good for retrospective model applications when historical lightning data are available. For applications when observed data are not available (i.e., air quality forecasts and climate studies that assume similar climate conditions), we have developed a scheme that is based on linear and log-linear parameters derived from regression of multiyear historical NLDN (pNLDN) observations and meteorological model simulations. Preliminary assessment for total column LNO production reveals that the mNLDN scheme overestimates LNO by over 40 % during summer months compared with the updated hNLDN scheme that reflects the observed lightning activity more faithfully in time and space. The pNLDN performance varies with year, but it generally produced LNO columns that are comparable to hNLDN and mNLDN, and in most cases it outperformed mNLDN. Thus, when no observed lightning data are available, pNLDN can provide reasonable estimates of LNO emissions over time and space for this important natural NO source that influences air quality regulations.

Subjects

Subjects :
Geology
QE1-996.5

Details

Language :
English
ISSN :
1991959X and 19919603
Volume :
12
Database :
Directory of Open Access Journals
Journal :
Geoscientific Model Development
Publication Type :
Academic Journal
Accession number :
edsdoj.50f05145f46b583f4398893e1118a
Document Type :
article
Full Text :
https://doi.org/10.5194/gmd-12-3071-2019