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Analysis of Seasonality and Trends in WRF-CMAQ Modeled PM2.5 using Empirical Mode Decomposition
Citation:
Luo, H., M. Astitha, C. Hogrefe, R. Mathur, AND S. Rao. Analysis of Seasonality and Trends in WRF-CMAQ Modeled PM2.5 using Empirical Mode Decomposition. 2019 MAC-MAQ Conference, Davis, California, September 11 - 13, 2019.
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:
Regional air quality models have been widely used in studying sources, composition, transport and transformation of PM2.5 as well as their adverse environmental and health impacts. The emergence of decadal air quality simulations allows more sophisticated model evaluation other than the traditional operational evaluation. We propose a new framework of process-based model evaluation of speciated PM2.5 using Empirical Mode Decomposition (EMD) to assess how well regional-scale air quality models simulate the time-dependent long-term trend and cyclic variations in daily average PM2.5 and its species, including SO4, NO3, NH4, Cl, OC and EC. Amplitudes of the annual cycles of total PM2.5, SO4 and OC are well reproduced. However, the time-dependent phase difference in the annual cycles for total PM2.5, OC and EC reveal a shift of up to half year, indicating a potential challenge in the allocation of emissions during the study period and the urgent need for the recently completed model updates in the treatment of organic aerosols compared to the version employed for this set of simulations. Evaluation of several intra-annual and interannual variations indicates that model has larger potential in replicating the intra-annual cycles. In addition, we investigate the role of species other than those in the available dataset in driving agreements or discrepancies between model simulations and observations.