Evaluating trends and seasonality in modeled PM_2.5 concentrations using empirical mode decomposition.

ABSTRACT: Regional-scale air quality models are being used for studying the sources, composition, transport, transformation, and deposition of fine particulate matter (PM_2.5). The availability of decadal air quality simulations provides a unique opportunity to explore sophisticated model evaluation techniques rather than relying solely on traditional operational evaluations. In this study, we propose a new approach for process-based model evaluation of speciated PM_2.5 using improved complete ensemble empirical mode decomposition with adaptive noise (improved CEEMDAN) to assess how well version 5.0.2 of the coupled Weather Research and Forecasting model-Community Multiscale Air Quality model (WRF-CMAQ) simulates the time-dependent long-term trend and cyclical variations in daily

SUBMITTER: Luo H 

PROVIDER: S-EPMC7751620 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature