Simulation of surface ozone pollution in the Central Gulf Coast region during summer synoptic condition using WRF/Chem air quality model

Yerramilli, Anjaneyulu; Challa, Venkata Srinivas; Dodla, Venkata Bhaskar Rao; Myles, LaToya; Pendergrass, William R.; Vogel, Christoph; Tuluri, Francis; Baham, Julius M.; Hughes, Robert W.; Patrick, Chuck; Young, John H.; Swanier, Shelton J.

doi:10.5094/apr.2012.005

Cited by 21 publications

(14 citation statements)

References 44 publications

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“…Hari Prasad et al used widely in weather prediction and to generate meteorological parameters for air quality modelling. Some of the problems in the application of the ARW for atmospheric dispersion/air quality simulation studies include underestimation of air temperature, overestimation of wind speed during the night conditions and extremities in PBL mixing (Hanna and Yang, 2001;Cheng and Steenburgh, 2005;Borge et al, 2008;Steeneveld et al, 2008;Roux et al, 2009;Yerramilli et al, 2010Yerramilli et al, , 2012Shin and Hong, 2011). Such errors in low-level meteorological fields would also influence the short-range weather predictions.…”

Section: Introductionmentioning

confidence: 99%

Assessment of surface layer parameterizations in ARW using micro‐meteorological observations from a tropical station

Prasad

Srinivas

Naidu

et al. 2016

Meteorological Applications

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The performance of two surface layer parameterizations (MM5‐similarity/Eta‐similarity schemes) in an Advanced Research Weather Research and Forecasting (ARW) mesoscale model is assessed over the tropical site Kalpakkam in southeast India. The ARW is run with four‐nested domains and at a high resolution (1 km) for four 2 day periods covering eight days in each season, 9–17 January, 4–12 April, 4–12 August and 13–21 October 2014 representing winter, summer, the southwest monsoon and the northeast monsoon, respectively. Turbulence observations on winds collected using a fast‐response ultrasonic anemometer are used to estimate the surface layer parameters of friction velocity (u*), and Monin–Obukhov length (L), and surface fluxes of heat and moisture using the eddy correlation method. Results of simulations indicate seasonal variation in friction velocity, drag co‐efficient, turbulent fluxes of heat and moisture, air temperature, humidity and winds at the study site. Relatively higher u*, drag co‐efficients, turbulent fluxes of heat and moisture are found during summer and the southwest monsoon, followed by the northeast monsoon and winter due to the prevalence of more unstable conditions in these seasons. Although the diurnal cycle of various parameters is well produced by both the schemes, the Eta‐similarity scheme produced higher values of surface layer variables relative to the MM5‐similarity scheme. Although both schemes depict a systematic bias in various surface layer parameters, the predicted values with MM5‐similarity scheme are in better agreement with observational estimates as indicated by the qualitative and quantitative comparisons of results. The differences in the results of the two schemes are attributed to the variation in the empirical stability correction functions and the stability regimes used in the two schemes.

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Section: Introductionmentioning

confidence: 99%

Assessment of surface layer parameterizations in ARW using micro‐meteorological observations from a tropical station

Prasad

Srinivas

Naidu

et al. 2016

Meteorological Applications

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“…The PBL parameterizations influence the simulations of the wind, turbulence, and other thermodynamic variables in the lower atmosphere. It is in this area where many processes that govern transport and dispersion of pollutants take place [17]. The uncertainty in parameter estimates arises from the different assumptions on the transport of mass, moisture, and energy.…”

Section: Sensitivity Experiments and Computation Of Pollutant Concentmentioning

confidence: 99%

“…The two PBL schemes used in the runs were the YSU and MYJ schemes. According to Yerramilli et al [17], the YSU is a modification of the Medium Range Forecast scheme. It uses an explicit treatment of entrainment processes.…”

Section: Sensitivity Experiments and Computation Of Pollutant Concentmentioning

confidence: 99%

Application of the WRF/Chem v.3.6.1 on the reanalysis of criteria pollutants over Metro Manila

Garcia

Vallar

Galvez

et al. 2019

Sustain Environ Res

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Metro Manila, Philippines and other urban areas have reached internationally known unacceptable levels of pollution where about 80% can be attributed to vehicular emissions. The Weather Research and Forecasting model coupled with Chemistry v.3.6.1 was used in the reanalysis of pollutant concentrations for the year 2013. Initial results from the planetary boundary layer study suggested that the Yonsei University scheme provides a good estimate of the atmosphere's condition; hence, this setting was used for the succeeding simulations. The land coverage over Sangley point was not properly resolved by the model. This caused a cold bias for the station. Further evaluation of the model's sea level pressure output for all sites returned high correlations showing that modeled values are in phase with the observed time series; however, wind speed values did not correlate well with the observed values and were all overestimated. The low correlations found were a result of the incapability of the model to detect the urban canopy layer over Metro Manila. Pollutant concentrations were overestimated. The pollutant time series suggests that the model overestimates concentration values for PM 10 , PM 2.5 , and SO 2 , while underestimating NO 2 and O 3 values. However, it does capture a significant 24-hourly cycle as seen in the time series' spectra in the frequency domain. Furthermore, through a student's t-test, the model also captures a significant difference in daytime and nighttime concentrations.

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“…Some studies indicate that the Yonsei University (YSU) PBL scheme shows the closest agreement between predicted hourly O 3 and observed hourly O 3 (Yerramilli et al, 2012;Cuchiara et al, 2014;Cheng et al, 2012). Although Yerramilli et al (2012) claim that O 3 is more sensitive to model uncertainty in the Land Surface Model (LSM), Pleim (2011) notes that there are deficiencies in that study, including the fact that the second version of the Asymmetric Convective Model (ACM2) at that time was not implemented for use with tracers within WRF-Chem. Since then, other studies, such as Cuchiara et al (2014), have used ACM2 within WRF-Chem since diffusion was implemented in 2013. and Hu, Klein, Xue, Zhang et al (2013) indicate that the mixing strength in the PBL scheme is critical to correctly simulate near-surface O 3 and its dry deposition during night time, which will affect the amount of O 3 in the convective boundary layer during the next day.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Uncertainties of Ground‐Level Ozone Within WRF‐Chem Simulations in the Mid‐Atlantic Region of the United States as a Response to Variability

Thomas

Huff

et al. 2019

J Adv Model Earth Syst

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Understanding forecast uncertainties and error growth dynamics is a prerequisite for improving dynamical prediction of meteorology and air quality. While predictability of meteorology has been investigated over the past few decades, the uncertainties in air quality simulations are less well known. This study explores the uncertainties in predicting ground‐level ozone (O3) in the Mid‐Atlantic region of the United States during June 2016 through a series of simulations using WRF‐Chem, focusing on the sensitivity to the meteorological initial and boundary conditions (IC/BCs), emissions inventory (EI), and planetary boundary layer (PBL) scheme. The average uncertainty of ground‐level maximum 8‐hr average O3 mixing ratio (MD8‐O3) was most sensitive to uncertainties in the IC/BCs, while uncertainty in the EI was of secondary importance, and was least sensitive was to the use of different PBL schemes. Updating the NO emissions in the EI had the greatest influence on the accuracy, with an estimated decrease of 0.59 ppbv/year in the root‐mean‐square error and an average decrease of 0.63 ppbv/year in the values of modeled MD8‐O3. Our study suggests using perturbations in IC/BCs may lead to a more dispersive ensemble of O3 prediction than using different PBL schemes and/or different EI. However, considering the combined uncertainties from all three sources examined are still smaller than the averaged root‐mean‐square errors of predicted O3 against observations, there are apparent other sources of uncertainties not studied that need to be considered in future ensemble predictions of O3.

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Simulation of surface ozone pollution in the Central Gulf Coast region during summer synoptic condition using WRF/Chem air quality model

Cited by 21 publications

References 44 publications

Assessment of surface layer parameterizations in ARW using micro‐meteorological observations from a tropical station

Assessment of surface layer parameterizations in ARW using micro‐meteorological observations from a tropical station

Application of the WRF/Chem v.3.6.1 on the reanalysis of criteria pollutants over Metro Manila

Quantifying Uncertainties of Ground‐Level Ozone Within WRF‐Chem Simulations in the Mid‐Atlantic Region of the United States as a Response to Variability

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