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Application of Lidar Data to the Performance Evaluations of CMAQ Model
Kang, D., R. Mathur, C. Hogrefe, R. Gilliam, G. Pouliot, S. Roselle, W. Appel, R. Matichuk, AND G. Tonnesen. Application of Lidar Data to the Performance Evaluations of CMAQ Model. 97th AMS Conference, Seattle, WA, January 22 - 26, 2017.
The Tropospheric Ozone (O3) Lidar Network (TOLNet) provides time/height O3 measurements from near the surface to the top of the troposphere to describe in high-fidelity spatial-temporal distributions, which is uniquely useful to evaluate the temporal evolution of O3 profiles in air quality models. This presentation describes the application of the Lidar data to the performance evaluation of CMAQ simulated O3 vertical profiles during the summer, 2014. Two-way coupled WRF-CMAQ simulations with 12km and 4km domains centered over Boulder, Colorado were performed during this time period. The analysis on the time series of observed and modeled O3 mixing ratios at different vertical layers indicates that the model frequently underestimated the observed values, and the underestimation was amplified in the middle model layers (~1km above the ground). When the lightning strikes detected by the National Lightning Detection Network (NLDN) were analyzed along with the observed O3 time series, it was found that the daily maximum O3 mixing ratios correlated well with the lightning strikes in the vicinity of the Lidar station. The analysis on temporal vertical profiles of both observed and modeled O3 mixing ratios on episodic days suggests that the model resolutions (12km and 4km) do not make any significant difference for this analysis (at this specific location and simulation period), but high O3 levels in the middle layers were linked to lightning activity that occurred in the vicinity of the Lidar station and these upper-layer high O3 levels propagated to the surface and impacted ground-level air quality. Surface O3 mixing ratios could increase by up to 20 ppb following considerable lightning activity in the region, therefore lightning emissions should be included in air quality models in order to produce accurate O3 predictions.
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.
Record Details:Record Type: DOCUMENT (PRESENTATION/SLIDE)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY
COMPUTATIONAL EXPOSURE DIVISION