Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006

Djalalova, Irina V.; Wilczak, James M.; McKeen, S. A.; Grell, Georg; Peckham, Steven E.; Pagowski, Mariusz; DelleMonache, L.; McQueen, Jeff; Tang, Youhua; Lee, P.; McHenry, John N.; Gong, Wei; Bouchet, V. S.; Mathur, Rohit

doi:10.1016/j.atmosenv.2009.11.007

Cited by 65 publications

(48 citation statements)

References 29 publications

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“…[], Djalalova et al . [] showed the combination of Kalman filtering and weighted averaging. PM 2.5 aerosol ensembles demonstrated significant improvement gains.…”

Section: Ensemble Mesoscale Chemistry Transport Modelingmentioning

confidence: 99%

Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations

Wang

Reid³

et al. 2017

JGR Atmospheres

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Atmospheric transport of smoke from equatorial Southeast Asian Maritime Continent (Indonesia, Singapore, and Malaysia) to the Philippines was recently verified by the first‐ever measurement of aerosol composition in the region of the Sulu Sea from a research vessel named Vasco. However, numerical modeling of such transport can have large uncertainties due to the lack of observations for parameterization schemes and for describing fire emission and meteorology in this region. These uncertainties are analyzed here, for the first time, with an ensemble of 24 Weather Research and Forecasting model with Chemistry (WRF‐Chem) simulations. The ensemble reproduces the time series of observed surface nonsea‐salt PM2.5 concentrations observed from the Vasco vessel during 17–30 September 2011 and overall agrees with satellite (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS)) and Aerosol Robotic Network (AERONET) data. The difference of meteorology between National Centers for Environmental Prediction (NCEP's) Final (FNL) and European Center for Medium range Weather Forecasting (ECMWF's) ERA renders the biggest spread in the ensemble (up to 20 μg m−3 or 200% in surface PM2.5), with FNL showing systematically superior results. The second biggest uncertainty is from fire emissions; the 2 day maximum Fire Locating and Modelling of Burning Emissions (FLAMBE) emission is superior than the instantaneous one. While Grell‐Devenyi (G3) and Betts‐Miller‐Janjić cumulus schemes only produce a difference of 3 μg m−3 of surface PM2.5 over the Sulu Sea, the ensemble mean agrees best with Climate Prediction Center (CPC) MORPHing (CMORPH)'s spatial distribution of precipitation. Simulation with FNL‐G3, 2 day maximum FLAMBE, and 800 m injection height outperforms other ensemble members. Finally, the global transport model (Navy Aerosol Analysis and Prediction System (NAAPS)) outperforms all WRF‐Chem simulations in describing smoke transport on 20 September 2011, suggesting the challenges to model tropical meteorology at mesoscale and finer scale.

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“…[], Djalalova et al . [] showed the combination of Kalman filtering and weighted averaging. PM 2.5 aerosol ensembles demonstrated significant improvement gains.…”

Section: Ensemble Mesoscale Chemistry Transport Modelingmentioning

confidence: 99%

Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations

Wang

Reid³

et al. 2017

JGR Atmospheres

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“…Hogrefe et al, 2006;Djalalova et al, 2010). Bias-adjustment strategies range from the relatively simple mean bias and multiplicative ratio adjustments used by McKeen et al (2005) to the more complex Kalman filter techniques (Manders et al, 2009;Kang et al, 2010;Sicardi et al, 2011).…”

Section: Model Bias Correctionmentioning

confidence: 99%

Aerosols in the CALIOPE air quality modelling system: evaluation and analysis of PM levels, optical depths and chemical composition over Europe

et al. 2012

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Abstract. The CALIOPE air quality modelling system is developed and applied to Europe with high spatial resolution (12 km × 12 km). The modelled daily-to-seasonal aerosol variability over Europe in 2004 is evaluated and analysed. Aerosols are estimated from two models, CMAQv4.5 (AERO4) and BSC-DREAM8b. CMAQv4.5 calculates biogenic, anthropogenic and sea salt aerosol and BSC-DREAM8b provides the natural mineral dust contribution from North African deserts. For the evaluation, we use daily PM10, PM2.5 and aerosol components data from 55 stations of the EMEP/CREATE network and total, coarse and fine aerosol optical depth (AOD) data from 35 stations of the AERONET sun photometer network. Annual correlations between modelled and observed values for PM10 and PM2.5 are 0.55 and 0.47, respectively. Correlations for total, coarse and fine AOD are 0.51, 0.63, and 0.53, respectively. The higher correlations of the PM10 and the coarse mode AOD are largely due to the accurate representation of the African dust influence in the forecasting system. Overall PM and AOD levels are underestimated. The evaluation of the aerosol components highlights underestimations in the fine fraction of carbonaceous matter (EC and OC) and secondary inorganic aerosols (SIA; i.e. nitrate, sulphate and ammonium). The scores of the bulk parameters are significantly improved after applying a simple model bias correction based on the observed aerosol composition. The simulated PM10 and AOD present maximum values over the industrialized and populated Po Valley and Benelux regions. SIA are dominant in the fine fraction representing up to 80% of the aerosol budget in latitudes north of 40° N. In southern Europe, high PM10 and AOD are linked to the desert dust transport from the Sahara which contributes up to 40% of the aerosol budget. Maximum seasonal ground-level concentrations (PM10 > 30 μg m−3) are found between spring and early autumn. We estimate that desert dust causes daily exceedances of the PM10 European air quality limit value (50 μg m−3) in large areas south of 45° N with more than 75 exceedances per year in the southernmost regions.

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“…Studies indicate that bias correction is a useful tool for improving model forecasts of both O 3 and PM 2.5 concentrations (Delle Monache et al, 2006; Djalalova et al, 2010; McKeen et al, 2005), even across large study areas (e.g., North America, eastern U.S.). Results of a comparison of two bias correction approaches (hybrid filter, Kalman filter) applied to CMAQ simulation results indicate that these techniques reduced systematic errors in model forecasts, although residual error from unsystematic and random errors remained (Kang et al, 2010; Kang et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Comparison of exposure estimation methods for air pollutants: Ambient monitoring data and regional air quality simulation

Bravo

Fuentes

Zhang

et al. 2012

Environmental Research

109

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Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5) and ozone (O3) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM2.5 and O3, respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O3 (annual normalized mean bias = 4.30%), while modeled PM2.5 had an annual normalized mean bias of −2.09%, although bias varied seasonally, from 32% in November to −27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors.

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Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006

Cited by 65 publications

References 29 publications

Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations

Mesoscale modeling of smoke transport from equatorial Southeast Asian Maritime Continent to the Philippines: First comparison of ensemble analysis with in situ observations

Aerosols in the CALIOPE air quality modelling system: evaluation and analysis of PM levels, optical depths and chemical composition over Europe

Comparison of exposure estimation methods for air pollutants: Ambient monitoring data and regional air quality simulation

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