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Dynamic evaluation of WRF-CMAQ PM2.5 simulations over the Contiguous United States for the period 2000-2010
Citation:
Luo, H., M. Astitha, C. Hogrefe, R. Mathur, AND S. Rao. Dynamic evaluation of WRF-CMAQ PM2.5 simulations over the Contiguous United States for the period 2000-2010. 2018 CMAS Conference, Chapel Hill, NC, October 22 - 24, 2018.
Impact/Purpose:
By comparing observed and CMAQ-simulated long term changes in aerosols, the work in this study addresses the question of how well CMAQ performs when applied to simulate the effects of changing emissions and meteorological variability on ambient particulate matter. To this end the study utilizes different statistical techniques to determine the time-scale dependency of model performance. Results of this analysis can inform model development efforts by focusing such efforts on time scales most directly connected to the models ability in reproducing observed variability and trends.
Description:
Dynamic evaluation of the 2000-2010 fully coupled Weather Research and Forecasting (WRF) - Community Multiscale Air Quality (CMAQ) model fine particulate matter simulations over the contiguous United States (CONUS) is conducted to assess how well the changes and trends in observed PM2.5 and its speciation are simulated by the model. The changes induced by variations in meteorology and/or emissions are also evaluated during the same timeframe using Kolmogorov-Zurbenko (KZ) spectral decomposition of observed and modeled time series with the aim of identifying the underlying forcing mechanisms that control particulate matter exceedances. Apart from KZ filtering, mode decomposition methods such as Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are also employed to study the intrinsic time scales embedded in the observed and simulated time series and to critically assess the model’s capability in reproducing the spatio-temporal features seen in observations. Results of the dynamic evaluation are presented for total PM2.5 and its chemical components for multiple time scales across six different geographical regions in the CONUS.
