J19.4A
(Formerly Poster 237.) Use of ensemble WRF meteorological fields in July 2005 CMAQ simulations over the eastern U.S

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Wednesday, 26 January 2011: 2:15 PM
(Formerly Poster 237.) Use of ensemble WRF meteorological fields in July 2005 CMAQ simulations over the eastern U.S
3A (Washington State Convention Center)
Brian J. Etherton, University of North Carolina, Chapel Hill, NC; and P. D. Dolwick, S. Arunachalam, and K. Baker

Accurate prediction of past air quality concentrations within a chemical-transport model requires an accurate characterization of the meteorological conditions that occurred during the evaluation base case. Numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, are typically used to develop the requisite gridded meteorological data that are input to the air quality model. Due to the complexity of the atmosphere, meteorological models like WRF often parameterize key atmospheric processes that occur at scales smaller than the model grid resolution. The purpose of this analysis was to model an ensemble of potential WRF configurations looking at the impacts of the differing planetary boundary layer (PBL) parameterizations and differing land surface models.

A set of 16 WRF simulations was completed over an eastern U.S. domain for the month of July 2005, a period in which air quality was relatively poor. Each of the WRF ensemble members were evaluated against existing ambient meteorological data to assess which configurations best matched the historical meteorological conditions. Additionally, each of the meteorological simulations were input into the Community Multiscale Air Quality (CMAQ) model to assess how differing meteorological model performance affected the accuracy of the air quality model, specifically on predictions of O3 and PM2.5. The three specific goals of this analysis were: 1) to better understand how meteorological model performance affects downstream air quality model performance (i.e., how correlated are WRF bias/errors with eventual CMAQ bias/errors), 2) to better understand which meteorological parameters are most important to replicate in order to maximize AQ model performance, and 3) to better understand the potential variability in meteorological model parameterizations that can affect the resultant met inputs and AQ model outputs.

Supplementary URL: www.sensordatabus.org/wrf