Application of WRF/Chem over   North America  under the AQMEII Phase 2 – Part 2: Evaluation of 2010 application and responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010

Yahya, Khairunnisa; Wang, Kai; Zhang, Y.; Kleindienst, Tadeusz E.

doi:10.5194/gmd-8-2095-2015

Cited by 19 publications

(17 citation statements)

References 43 publications

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“…Therefore, the underpredictions of the total precipitation in d04 during these field campaigns may be mainly associated with the limitations of either the M09 double moment microphysics scheme in the former case or the MSKF cumulus parameterization in the latter case. The underpredictions in total precipitation over Australia in this work are different from the reported overpredictions by WRF/Chem [67,68] or WRF/Chem-ROMS [40] over the U.S. where the simulated convective precipitation dominates the total precipitation. Such overpredictions are attributed to the limitations of the cumulus parameterization parameterizations of Grell and Devenyi [101] (GD) or Grell and Freitas [102].…”

Section: Boundary Layer Meteorological Evaluationcontrasting

confidence: 99%

“…The WRF/Chem-ROMS simulation uses the same physics and chemistry options as WRF/Chem except that it explicitly simulates air-sea interactions and the sea surface temperature (SST). Similar to nearly all past WRF/Chem applications [32][33][34][35][36][37][38][39][64][65][66][67][68][69], the SST is prescribed using NCEP high-res SST analysis (RTG_SST) data [70,71] for the WRF/Chem simulation but simulated by ROMS for the WRF/Chem-ROMS simulation. As shown in He et al [40], by explicitly simulating air-sea interactions and SST, WRF/Chem-ROMS can improve the predictions of most cloud and radiative variables compared to WRF/Chem that does not simulate such interactions.…”

Section: Model Descriptionmentioning

confidence: 99%

“…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%

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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: Boundary Layer Meteorological Evaluationcontrasting

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

Section: Evaluation Datasets and Protocolsmentioning

confidence: 99%

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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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“…To re-grid the RCP emissions, the RCP rectilinear grid is first interpolated to a WRF/Chem curvilinear grid using a simple inverse distance weighting (NCAR Command Language Function -rgrid2rcm), and a subset of the RCP grid that covers the WRF/Chem CONUS domain is then extracted. To derive a temporal allocation for monthly averaged RCP emissions, hourly emission profiles are taken from those used in-house WRF/Chem simulations over CONUS during 2001 (Yahya et al, 2015c), and 2006 and 2010 as part of the AQMEII project (Yahya et al, 2015a, b). The emissions for those existing inhouse simulations were generated based on the 2002 NEI; the emissions were generated with the Sparse Matrix Operator Kernel Emissions (SMOKE) model version 2.3.…”

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

confidence: 99%

Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1

et al. 2016

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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 8.5 (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 with a normalized mean bias (NMB) of 9.7 % but underpredicted at rural locations with an NMB of −8.8 %. PM 2.5 concentrations are moderately overpredicted with an NMB of 23.3 % at rural sites but slightly underpredicted with an NMB of −10.8 % 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. Cloudaerosol 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 the eastern US result in underpredictions of radiation variables (such as net shortwave radiation -GSW -with a mean bias -MB -of −5.7 W m −2 ) and overpredictions of shortwave and longwave cloud forcing (MBs of ∼ 7 to 8 W m −2 ), 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.Published by Copernicus Publications on behalf of the European Geosciences Union.

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“…In this study, the North Carolina State University's modified Community Earth System long periods using emissions based on the U.S. National Emission Inventories (NEI)[e.g., Im et 74 al., 2014a, b;Yahya et al, 2014Yahya et al, , 2015a and a decade-long period (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) using 75 emissions based on RCP8.5. Decadal current and future climate simulations over CONUS under 76 multiple RCP scenarios have not been performed using advanced online-coupled meteorologychemistry models such as WRF/Chem, because such simulations are computationally veryexpensive.…”

mentioning

confidence: 99%

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

Yahya

Wang

Campbell

et al. 2017

Atmospheric Environment

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Application of WRF/Chem over North America under the AQMEII Phase 2 – Part 2: Evaluation of 2010 application and responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010

Cited by 19 publications

References 43 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

Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1

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

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