Decadal application of WRF/Chem for regional air quality and climate modeling over the U.S. under the representative concentration pathways scenarios. Part 1: Model evaluation and impact of downscaling

Abstract

1. A comprehensive decadal evaluation of WRF/Chem over the U.S. using surface and satellite datashows an overall good performance 2. WRF/Chem outperforms WRF in terms of radiative variables due to the inclusion of chemistry feedbacks to climate 3. WRF/Chem outperforms CESM in terms of domain-average performance statistics, and temporal/spatial variations due to a finer grid scale

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

“…Recently, the online-coupled Weather Research and Forecasting (WRF) model with Chemistry (WRF-Chem) was evaluated for decadal application over the continental US under RCP4.5 and RCP8.5 (Yahya et al, 2016(Yahya et al, , 2017a and applied to decadal projections of future climate and air quality under both scenarios (Yahya et al, 2017b). The Community Multi-scale Air Quality (CMAQ) model has historically been an offline model developed by the US Environmental Protection Agency (EPA) and widely used for air quality simulations over numerous countries and regions (Wang et al, 2009(Wang et al, , 2012Gao et al, 2013;Penrod et al, 2014;Sun et al, 2015;Zheng et al, 2015;Hu et al, 2016); it has recently been further developed to provide an online-coupled version with the Weather Research Forecast (WRF) model to simulate feedbacks between chemistry and meteorology (Wong et al, 2012).…”

“…The two-way coupled WRF-CMAQ model, which takes into account the air quality and climate interactions, is driven by the Community Earth System Model (CESM) implemented with advanced chemistry and aerosol treatments by North Carolina State University (NCSU) (hereafter CESM-NCSU) He et al, 2015a, b;Gantt et al, 2014;Glotfelty et al, 2017a, b;Glotfelty and Zhang, 2017) for highresolution regional simulation under a changing climate. Both meteorological dynamical downscaling and chemical composition downscaling from the CESM-NCSU were applied following the work of Yahya et al (2016Yahya et al ( , 2017a. The dynamical downscaling methods fully take advantage of global climate-chemistry models that can well predict largescale global changes and regional models that can better represent regional phenomena.…”

“…In this study, following the work of Yahya et al (2016Yahya et al ( , 2017a, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework (see Fig. 1).…”

A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization

“…The air pollutant data in this study included the concentrations of O 3 , PM 2.5 and SO 2 . We downloaded the air pollutant data from the U.S. EPA's Air Quality System (AQS) database (https://www.epa.gov/outdoor-air-quality-data), which has been widely used for model evaluation [42,43]. We selected the meteorological variables that would affect the air pollutant concentrations, including air temperature, relative humidity, wind speed and direction, wind gust, precipitation accumulation, visibility, dew point, wind cardinal direction, pressure, and weather conditions.…”

“…We collected 10 years worth of meteorological and air pollution data from the Chicago area. The air pollutant data was from the EPA [42,43], and the meteorological data was from MesoWest [44]. From their databases, we fetched consecutive hourly measurements of various meteorological variables and pollutants reported by two air quality monitoring stations and two air pollutant monitoring sites in the Chicago area.…”