Sensitivity Analysis of Atmospheric Dispersion Simulations by FLEXPART to the WRF-Simulated Meteorological Predictions in a Coastal Environment

Srinivas, C. V.; Prasad, K. B. R. R. Hari; Naidu, C. V.; Baskaran, R.; Venkatraman, B.

doi:10.1007/s00024-015-1104-z

Cited by 27 publications

(21 citation statements)

References 46 publications

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“…However, the model has difficulties simulating the correct wind direction, which is especially expressed in the large RMSE. Similar errors have been observed before (Deng et al, 2017;Srinivas et al, 2016). The largest error is found in the second half of November, causing a large model-data discrepancy (also visible in Fig.…”

Section: Wrf-chem Urban Plume Transportsupporting

confidence: 83%

“…Given the importance of observed local meteorology for the model performance, we strongly recommend the inclusion of a (simple) meteorological station in any similar monitoring set-up. Also, Lagrangian particle dispersion models driven by WRF meteorological fields have proven useful in describing the transport of point source emissions and in inverse modelling (Brioude et al, 2013;Pan et al, 2014;Srinivas et al, 2016), but such a set-up would suffer from wind field errors. The optimal set-up for an urban monitoring network requires a semi-urban measurement site (here ∼ 30 km from the urban area with no other urban areas in between) and at least one additional urban measurement site (here at the edge of the urban area, at ∼ 7 km from the city centre).…”

Section: Discussionmentioning

confidence: 99%

“…Also at the Cabauw site, which is less influenced by built-up areas, the model-data agreement for the 10 m wind direction in November is limited. Previous research has also shown an uncertainty of 30-40 % in the tracer mixing ratio due to the uncertainty in meteorological conditions (Angevine et al, 2014;Srinivas et al, 2016). Additionally, Angevine et al (2014) have shown that using an ensemble mean of model simulations with different meteorology does not necessarily lead to a better representation of plume transport and dispersion in a Lagrangian model for area sources.…”

Section: Model Skillmentioning

confidence: 99%

See 2 more Smart Citations

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

Super

Gon²,

Molen

et al. 2017

Atmos. Chem. Phys.

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Abstract. Monitoring urban-industrial emissions is often challenging because observations are scarce and regional atmospheric transport models are too coarse to represent the high spatiotemporal variability in the resulting concentrations. In this paper we apply a new combination of an Eulerian model (Weather Research and Forecast, WRF, with chemistry) and a Gaussian plume model (Operational Priority Substances -OPS). The modelled mixing ratios are compared to observed CO 2 and CO mole fractions at four sites along a transect from an urban-industrial complex (Rotterdam, the Netherlands) towards rural conditions for October-December 2014. Urban plumes are well-mixed at our semi-urban location, making this location suited for an integrated emission estimate over the whole study area. The signals at our urban measurement site (with average enhancements of 11 ppm CO 2 and 40 ppb CO over the baseline) are highly variable due to the presence of distinct source areas dominated by road traffic/residential heating emissions or industrial activities. This causes different emission signatures that are translated into a large variability in observed CO : CO 2 ratios, which can be used to identify dominant source types. We find that WRF-Chem is able to represent synoptic variability in CO 2 and CO (e.g. the median CO 2 mixing ratio is 9.7 ppm, observed, against 8.8 ppm, modelled), but it fails to reproduce the hourly variability of daytime urban plumes at the urban site (R 2 up to 0.05). For the urban site, adding a plume model to the model framework is beneficial to adequately represent plume transport especially from stack emissions. The explained variance in hourly, daytime CO 2 enhancements from point source emissions increases from 30 % with WRF-Chem to 52 % with WRFChem in combination with the most detailed OPS simulation. The simulated variability in CO : CO 2 ratios decreases drastically from 1.5 to 0.6 ppb ppm −1 , which agrees better with the observed standard deviation of 0.4 ppb ppm −1 . This is partly due to improved wind fields (increase in R 2 of 0.10) but also due to improved point source representation (increase in R 2 of 0.05) and dilution (increase in R 2 of 0.07). Based on our analysis we conclude that a plume model with detailed and accurate dispersion parameters adds substantially to top-down monitoring of greenhouse gas emissions in urban environments with large point source contributions within a ∼ 10 km radius from the observation sites.

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Section: Wrf-chem Urban Plume Transportsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Model Skillmentioning

confidence: 99%

See 1 more Smart Citation

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

Super

Gon²,

Molen

et al. 2017

Atmos. Chem. Phys.

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“…For example, fixed site monitors are sparse in much of the western US, and satellite products do not provide surface-level concentrations on their own. Atmospheric model simulations may be biased by their emission inventories (Davis et al, 2015;Zhang et al, 2014), spatial resolution (Misenis and Zhang, 2010;Punger and West, 2013;Thompson et al, 2014;Thompson and Selin, 2012), or input meteorological fields (Cuchiara et al, 2014;Srinivas et al, 2015;Žabkar et al, 2013). Thus, there is a growing effort to include multiple datasets (e.g., Henderson et al, 2011;Yao et al, 2013) and create blended products that can exploit the strengths of each dataset (Brauer et al, 2015;Lassman et al, 2017;Gan et al, 2017;Reid et al, 2015;Yao and Henderson, 2014).…”

Section: Introductionmentioning

confidence: 99%

Status update: is smoke on your mind? Using social media to assess smoke exposure

et al. 2017

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Abstract. Exposure to wildland fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for assessing these smoke events (groundbased measurements, satellite observations, and chemical transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using daily social media posts from Facebook regarding smoke, haze, and air quality to assess population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook dataset to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), daily (24 h) average surface particulate matter measurements, and modelsimulated (WRF-Chem) surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well correlated (R 2 generally above 0.5) with the other methods in smoke-impacted regions. The Facebook dataset is better correlated with surface measurements of PM 2.5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation. We also present an example case for Washington state in 2015, for which we combine this Facebook dataset with MODIS observations and WRF-Chem-simulated PM 2.5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct groundbased particulate matter measurements.

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“…Atmospheric dispersion over the complex terrain of the Mississippi Gulf Coast region was studied using an offline integration of a mesoscale atmospheric model for generating atmospheric variables and HYSPLIT model for dispersion Challa et al, 2008;Anjaneyulu et al, 2009;Challa et al, 2009). Srinivas et al (2015) used WRF for weather prediction and FLEXPART and HYSPLIT dispersion models for the assessment of atmospheric dispersion of pollutants over Kalpakkam located on the Indian southeast coast. Modeling studies of atmospheric dispersion over India are few and the fact that atmospheric dispersion is dependent on local circulations, studies for any selected location is to be thoroughly investigated under different meteorological conditions to understand the pollutant dispersion characteristics over the region.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Dispersion of PM2.5 Precursor Gases from Two Major Thermal Power Plants in Andhra Pradesh, India

Dodla

Satyanarayana

Desamsetti

2017

Aerosol Air Qual. Res.

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Fine particulate matter (PM 2.5 ) predominantly comprises sulphates and nitrates, which results from sulphur dioxide (SO 2 ) and nitrogen oxide (NO x ) gases that are emanated from excessive industrial activities and transport systems. PM 2.5 is known to affect respiratory health in humans. Coal-fired thermal power plants are a major source of SO 2 and NO x gases. Evaluation of the dispersion characteristics of these precursor gases from the power plants would help understand the vulnerability. Meteorological conditions that prevail over the region would influence the dispersion characteristics. In this study, dispersion of SO 2 and NO from two major coal-fired thermal power plants in Andhra Pradesh, India have been studied using an integrated modeling approach of the Advanced Research Weather Research & Forecasting (ARW) model and Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Meteorological conditions are obtained at 3-km resolution using the ARW model and dispersions of SO 2 and NO is computed using the HYSPLIT model for the four seasons of winter, summer, monsoon and post-monsoon. Forward trajectories produced by the HYSPLIT model show diurnal variations and dispersion patterns show seasonal variations indicating the influence of meteorological conditions. Dispersion characteristics show high dispersion in winter due to calm and stable atmospheric conditions to insignificant in summer season due to stronger winds and higher atmospheric instability. The study establishes the usefulness of integrated meteorological and dispersion models for the evaluation of pollutant dispersion.

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Sensitivity Analysis of Atmospheric Dispersion Simulations by FLEXPART to the WRF-Simulated Meteorological Predictions in a Coastal Environment

Cited by 27 publications

References 46 publications

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

Status update: is smoke on your mind? Using social media to assess smoke exposure

Atmospheric Dispersion of PM2.5 Precursor Gases from Two Major Thermal Power Plants in Andhra Pradesh, India

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Sensitivity Analysis of Atmospheric Dispersion Simulations by FLEXPART to the WRF-Simulated Meteorological Predictions in a Coastal Environment

Cited by 27 publications

References 46 publications

A multi-model approach to monitor emissions of CO&lt;sub&gt;2&lt;/sub&gt; and CO from an urban–industrial complex

A multi-model approach to monitor emissions of CO&lt;sub&gt;2&lt;/sub&gt; and CO from an urban–industrial complex

Status update: is smoke on your mind? Using social media to assess smoke exposure

Atmospheric Dispersion of PM2.5 Precursor Gases from Two Major Thermal Power Plants in Andhra Pradesh, India

Contact Info

Product

Resources

About

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex

A multi-model approach to monitor emissions of CO<sub>2</sub> and CO from an urban–industrial complex