Multiyear applications of WRF/Chem over continental U.S.: Model evaluation, variation trend, and impacts of boundary conditions

Yahya, Khairunnisa; He, Jian; Zhang, Yang

doi:10.1002/2015jd023819

Cited by 12 publications

(13 citation statements)

References 97 publications

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“…No published benchmarks are available to judge the model's performance for precipitation. Based on previous applications of MM5 and WRF over various regions [36,37,40,66,67,[84][85][86][87][88][89], NMBs within 30% represent the precipitation performance by current models, which will be used as a benchmark for precipitation in this work. For chemical concentrations, Zhang et al [90] suggested good performance benchmarks of NMBs and NMEs within ±15% and 30% for O 3 and PM 2.5 .…”

Section: Evaluation Datasets and Protocolsmentioning

confidence: 99%

Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions

et al. 2019

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Air pollution and associated human exposure are important research areas in Greater Sydney, Australia. Several field campaigns were conducted to characterize the pollution sources and their impacts on ambient air quality including the Sydney Particle Study Stages 1 and 2 (SPS1 and SPS2), and the Measurements of Urban, Marine, and Biogenic Air (MUMBA). In this work, the Weather Research and Forecasting model with chemistry (WRF/Chem) and the coupled WRF/Chem with the Regional Ocean Model System (ROMS) (WRF/Chem-ROMS) are applied during these field campaigns to assess the models’ capability in reproducing atmospheric observations. The model simulations are performed over quadruple-nested domains at grid resolutions of 81-, 27-, 9-, and 3-km over Australia, an area in southeastern Australia, an area in New South Wales, and the Greater Sydney area, respectively. A comprehensive model evaluation is conducted using surface observations from these field campaigns, satellite retrievals, and other data. This paper evaluates the performance of WRF/Chem-ROMS and its sensitivity to spatial grid resolutions. The model generally performs well at 3-, 9-, and 27-km resolutions for sea-surface temperature and boundary layer meteorology in terms of performance statistics, seasonality, and daily variation. Moderate biases occur for temperature at 2-m and wind speed at 10-m in the mornings and evenings due to the inaccurate representation of the nocturnal boundary layer and surface heat fluxes. Larger underpredictions occur for total precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. The model performs well at 3-, 9-, and 27-km resolutions for surface O3 in terms of statistics, spatial distributions, and diurnal and daily variations. The model underpredicts PM2.5 and PM10 during SPS1 and MUMBA but overpredicts PM2.5 and underpredicts PM10 during SPS2. These biases are attributed to inaccurate meteorology, precursor emissions, insufficient SO2 conversion to sulfate, inadequate dispersion at finer grid resolutions, and underprediction in secondary organic aerosol. The model gives moderate biases for net shortwave radiation and cloud condensation nuclei but large biases for other radiative and cloud variables. The performance of aerosol optical depth and latent/sensible heat flux varies for different simulation periods. Among all variables evaluated, wind speed at 10-m, precipitation, surface concentrations of CO, NO, NO2, SO2, O3, PM2.5, and PM10, aerosol optical depth, cloud optical thickness, cloud condensation nuclei, and column NO2 show moderate-to-strong sensitivity to spatial grid resolutions. The use of finer grid resolutions (3- or 9-km) can generally improve the performance for those variables. While the performance for most of these variables is consistent with that over the U.S. and East Asia, several differences along with future work are identified to pinpoint reasons for such differences.

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Section: Evaluation Datasets and Protocolsmentioning

confidence: 99%

Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions

et al. 2019

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“…The CB05‐MADE/VBS mechanism with aqueous‐phase chemistry is fully coupled to existing model treatments for different aerosol‐radiation‐cloud feedback processes such as the aerosol direct effect on shortwave radiation, aerosol indirect effects on cloud droplet number concentration (CDNC), and cloud effects on shortwave radiation (Wang et al, ; Yahya, He, & Zhang, ). To account for the aerosol direct effect, aerosol radiative properties such as aerosol optical depth (AOD), single scattering albedo, and asymmetry factors are initially calculated based on the approach devised by Fast et al () according to the Mie theory (Mie, ).…”

Section: Description Of Models Emissions and Observationsmentioning

confidence: 99%

“…In this case, insufficient titration reactions are related to underprediction of NO x emissions from biomass burning regions. The level of NO x can influence O 3 mixing ratios through titration chemistry during the night and in the early morning hours (Yahya, He, & Zhang, ). Comparisons between the observed and predicted concentrations of NO 2 at four CETESB sites are shown in supporting information Figure S4.…”

Section: Impacts Of Biomass Burning Emissionsmentioning

confidence: 99%

“…However, most of the pairs (NMB and NME) for PM 2.5 and PM 10 were more clustered around the zero lines when compared to those from the entire study period (see Figure 6). Larger contributions of fire emissions to the maximum O 3 and PM 2.5 concentrations may be explained by the transport of such air pollutants from fire regions (during the day for O 3 and during the night for fine particles), as well as by additional in situ (Yahya, He, & Zhang, 2015). Comparisons between the observed and predicted concentrations of NO 2 at four CETESB sites are shown in supporting information Figure S4.…”

Section: Chemical Concentrationsmentioning

confidence: 99%

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Modeling of Atmospheric Aerosol Properties in the São Paulo Metropolitan Area: Impact of Biomass Burning

et al. 2018

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have been numerous studies on the direct and indirect climatic effects of aerosols [Takemura et al., 2002;Zhang, 2008; Zhang 51 et al., 2010;Forkel et al., 2012; Scott et al., 2014; Cai et al., 2016;Yahya et al., 2017]. Most of studies of such effects in South 52America have focused on the Amazon rainforest, as smoke generated from biomass burning in the region can spread over 53 significant portions of the continent, having a considerable effect on direct and indirect radiative forcing [Bevan et al., 2009; 54 Artaxo et al., 2013; Moreira et al., 2017], as well as on human health [Alves et al., 2015; Alves et al., 2017; et al. [1994a; 1994b]. Nucleation processes are based on mathematical formulations described in Kulmala et al. 112[1998]; condensation processes are based on Binkowski and Shankar [1995] 167Performance statistics were calculated for the period from 19 August to 3 September (the first two days of each simulation 168 were considered spin-up time and were therefore discarded). In addition, spatial distributions of absolute and relative 169 differences between BBE and BASE and between 3BBE and BASE were employed to quantify and characterise the changes in 170 aerosol properties due to the inclusion of fire emissions. In this case, the differences are averaged over a five-day episode from 171 22 August to 26 August, as in this period prevailed meteorological conditions in terms of the transport of air pollutants from 172 fire regions. al., 2015]. To scale the top-down and bottom-up emissions into the 25 km and 5 km modelling domains, mass-conserving 212 emissions pre-processors anthro_emiss [Barth et al., 2015] and AAS4WRF [Vara-Vela et al., 2016;Vara-Vela et al., 2017] 213 were employed, respectively. Supplement Fig. S1 shows spatial distributions of CO emission rates in both domains. Table S1. Information on precipitation and AOD derived from satellite data were 306 considered in addition to in situ and lidar measurements throughout the evaluation of numerical simulations. Table 3 307 summarises the observational data sets used for model evaluation.308

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“…To derive a temporal allocation for monthly-averaged RCP emissions, hourly emissions profiles are taken from in-house WRF/Chem simulations over CONUS during 2001 (Yahya et al, 2015a), and 2006 and 2010 (Yahya et al, 2014(Yahya et al, , 2015b. For those existing in-house simulations, the emissions were generated with the Sparse Matrix Operator Kernel Emissions (SMOKE) model version 2.3 for 2002 NEI and SMOKE version 3.4 for 2008 NEI with year-specific sector emissions for 2006 and 2010, which prepare the spatially, temporally, and chemically speciated "model-ready" emissions.…”

Section: Processing Of Emissions and Initial Conditions (Ics)/boundarmentioning

confidence: 99%

Decadal evaluation of regional climate, air quality, and their interactions using WRF/Chem Version 3.6.1

Yahya

Wang

Campbell

et al. 2015

Preprint

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Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) v3.6.1 with the Carbon Bond 2005 (CB05) gas-phase mechanism is evaluated for its first decadal application during 2001–2010 using the Representative Concentration Pathway (RCP 8.5) emissions to assess its capability and appropriateness for long-term climatological simulations. The initial and boundary conditions are downscaled from the modified Community Earth System Model/Community Atmosphere Model (CESM/CAM5) v1.2.2. The meteorological initial and boundary conditions are bias-corrected using the National Center for Environmental Protection's Final (FNL) Operational Global Analysis data. Climatological evaluations are carried out for meteorological, chemical, and aerosol-cloud-radiation variables against data from surface networks and satellite retrievals. The model performs very well for the 2 m temperature (T2) for the 10 year period with only a small cold bias of −0.3 °C. Biases in other meteorological variables including relative humidity at 2 m, wind speed at 10 m, and precipitation tend to be site- and season-specific; however, with the exception of T2, consistent annual biases exist for most of the years from 2001 to 2010. Ozone mixing ratios are slightly overpredicted at both urban and rural locations but underpredicted at rural locations. PM2.5 concentrations are slightly overpredicted at rural sites, but slightly underpredicted at urban/suburban sites. In general, the model performs relatively well for chemical and meteorological variables, and not as well for aerosol-cloud-radiation variables. Cloud-aerosol variables including aerosol optical depth, cloud water path, cloud optical thickness, and cloud droplet number concentration are generally underpredicted on average across the continental US. Overpredictions of several cloud variables over eastern US result in underpredictions of radiation variables and overpredictions of shortwave and longwave cloud forcing which are important climate variables. While the current performance is deemed to be acceptable, improvements to the bias-correction method for CESM downscaling and the model parameterizations of cloud dynamics and thermodynamics, as well as aerosol-cloud interactions can potentially improve model performance for long-term climate simulations.

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Multiyear applications of WRF/Chem over continental U.S.: Model evaluation, variation trend, and impacts of boundary conditions

Cited by 12 publications

References 97 publications

Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions

Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions

Modeling of Atmospheric Aerosol Properties in the São Paulo Metropolitan Area: Impact of Biomass Burning

Decadal evaluation of regional climate, air quality, and their interactions using WRF/Chem Version 3.6.1

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