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Impacts of WRF lightning assimilation on offline CMAQ simulations
Citation:
Heath, N., Jon Pleim, R. Gilliam, D. Kang, M. Woody, K. Foley, AND W. Appel. Impacts of WRF lightning assimilation on offline CMAQ simulations. 2016 CMAS Conference, Chapel Hill, NC, October 24 - 26, 2016.
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:
Deep convective clouds vertically redistribute trace gases and aerosols and also provide a source for scavenging, aqueous phase chemistry, and wet deposition, making them important to air quality.? Regional air quality simulations are typically driven by meteorological models that use relatively coarse resolution and require a convective parameterization.? Unfortunately, convective parameterizations generally do not represent the timing and location of deep convection accurately.? Additionally, positive rainfall biases commonly exist during summer months due to overactive parameterizations.? These shortcomings adversely impact air quality simulations.? In this study, lightning assimilation was applied in the Kain-Fritsch (KF) cumulus parameterization to improve the simulation of deep convection in the Weather Research and Forecasting (WRF) model.? The method has a straightforward approach: Force KF deep convection where lightning is observed and, optionally, suppress deep convection where lightning is absent.? WRF simulations with and without lightning assimilation were made for July 2011.? Major improvements were seen in WRF 6-h precipitation accumulations when lightning assimilation was used.? For example, when compared to Stage-IV observations, the monthly averaged spatial correlations more than doubled from 0.22 to 0.47 and the mean absolute error was reduced from 0.83 to 0.57 mm.? The two WRF simulations were then used to drive offline CMAQ simulations and here we present the impacts of the lightning assimilation on surface ozone and particulate matter concentrations evaluated against routine monitoring networks.
