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Evaluating Seasonality and Trends in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition
Citation:
Luo, H., M. Astitha, C. Hogrefe, R. Mathur, AND S. Rao. Evaluating Seasonality and Trends in Modeled PM2.5 Concentrations Using Empirical Mode Decomposition. 2019 Annual CMAS Conference, Chapel Hill, NC, October 21 - 23, 2019.
Impact/Purpose:
By comparing observed and CMAQ-simulated variability and trends 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 the Empirical Mode Decomposition (EMD) technique 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-scale air quality models are being used for studying the sources, composition, transport, transformation, and deposition of PM2.5. The availability of decadal air quality simulations provides a unique opportunity to explore sophisticated model evaluation techniques rather than relying solely traditional operational evaluations. In this study, we propose a new approach for process-based model evaluation of speciated PM2.5 using Empirical Mode Decomposition (EMD) to assess how well version 5.0.2 of the coupled WRF-CMAQ model simulates the time-dependent long-term trend and cyclical variations in daily average PM2.5 and its species, including SO4, NO3, NH4, Cl, OC and EC. The use of the proposed approach for model evaluation is demonstrated at three monitoring locations. At these locations, the model is generally more capable of simulating the rate of change in the long-term trend component than its absolute magnitude. Amplitudes of the sub-seasonal and 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 phase shift of up to half year, indicating the need for proper temporal allocation of emissions during this study period and an update to the treatment of organic aerosols compared to the model version used for this set of simulations. Evaluation of several sub-seasonal and inter-annual variations indicates that model is capable of replicating the sub-seasonal cycles in terms of magnitude and phase shift.