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A Performance Evaluation of Lightning-NO Algorithms in CMAQ
Citation:
Kang, D., David-C Wong, K. Foley, G. Pouliot, S. Roselle, AND P. Lee. A Performance Evaluation of Lightning-NO Algorithms in CMAQ. 16th Annual CMAS Conference, Chapel Hill, North Carolina, October 23 - 25, 2017.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
In the Community Multiscale Air Quality (CMAQv5.2) model, we have implemented two algorithms for lightning NO production; one algorithm is based on the hourly observed cloud-to-ground lightning strike data from National Lightning Detection Network (NLDN) to replace the previous monthly NLDN based algorithm and the other is a parameterization scheme based on linear relationship between historically observed NLDN data and model predicted convective precipitation. To evaluate the impact of these algorithms on model performance, four model simulation cases for the time period from April to September in 2011 were conducted. The four cases are: 1) no lightning NO, 2) the monthly NLDN based algorithm, 3) hourly NLDN based algorithm, and 4) the linear regression algorithm. Ground-level O3 and NOX from Air Quality System (AQS) and nitrate (NO3) from the National Atmospheric Deposition Program (NADP) are used to assess the model performances in time and space.
