Application of a statistical post-processing technique to a gridded, operational, air quality forecast

Neal, Lucy; Agnew, Paul; Moseley, Stephen; Ordóñez, Carlos; Savage, Nicholas; Tilbee, Marie

doi:10.1016/j.atmosenv.2014.09.004

Cited by 32 publications

(28 citation statements)

References 16 publications

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“…We revisit the June 2013 haze episode and apply a bias correction scheme to hourly PM 10 model time series for Singapore. Real‐time bias correction based on observations can be used to improve operational air quality and pollutant dispersion forecasts [e.g., Borrego et al , ; Neal et al , ]. Systematic and random model errors introduced, for example, by regionally varying uncertainties in emissions or NWP skill can be addressed through such a correction process.…”

Section: Resultsmentioning

confidence: 99%

Development and demonstration of a Lagrangian dispersion modeling system for real‐time prediction of smoke haze pollution from biomass burning in Southeast Asia

et al. 2015

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Transboundary smoke haze caused by biomass burning frequently causes extreme air pollution episodes in maritime and continental Southeast Asia. With millions of people being affected by this type of pollution every year, the task to introduce smoke haze related air quality forecasts is urgent. We investigate three severe haze episodes: June 2013 in Maritime SE Asia, induced by fires in central Sumatra, and March/April 2013 and 2014 on mainland SE Asia. Based on comparisons with surface measurements of PM10 we demonstrate that the combination of the Lagrangian dispersion model NAME with emissions derived from satellite‐based active‐fire detection provides reliable forecasts for the region. Contrasting two fire emission inventories shows that using algorithms to account for fire pixel obscuration by cloud or haze better captures the temporal variations and observed persistence of local pollution levels. Including up‐to‐date representations of fuel types in the area and using better conversion and emission factors is found to more accurately represent local concentration magnitudes, particularly for peat fires. With both emission inventories the overall spatial and temporal evolution of the haze events is captured qualitatively, with some error attributed to the resolution of the meteorological data driving the dispersion process. In order to arrive at a quantitative agreement with local PM10 levels, the simulation results need to be scaled. Considering the requirements of operational forecasts, we introduce a real‐time bias correction technique to the modeling system to address systematic and random modeling errors, which successfully improves the results in terms of reduced normalized mean biases and fractional gross errors.

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Section: Resultsmentioning

confidence: 99%

Development and demonstration of a Lagrangian dispersion modeling system for real‐time prediction of smoke haze pollution from biomass burning in Southeast Asia

et al. 2015

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“…Present-day concentrations for the UK were generated by the Met Office operational air quality forecast model AQUM [25], operated in a hindcast mode. The raw model hourly output data were combined with corresponding hourly surface air pollution measurements by the technique described in [26], to produce improved estimates of pollutant concentrations over the whole UK. The model operates at a spatial resolution of 12km and does not explicitly resolve the fine structure of emissions in urban areas.…”

Section: Methodsmentioning

confidence: 99%

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

et al. 2017

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BackgroundEstimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future.MethodsWe conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO2); ozone (O3); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM2.5), and less than 10 micrometers (PM10); and sulphur dioxide (SO2). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP).ResultsNO2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO2 emissions and concentrations.ConclusionsNO2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-017-0237-1) contains supplementary material, which is available to authorized users.

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“…In addition, using reanalysis data and reforecasting using the current model, or blend of models, is a possibility for further improvement (Hamill et al, 2015). In the short term, a variety of bias correction techniques have been used (Delle Monache et al, 2008;Kang et al, 2008;Neal et al, 2014). In 2015, a Kalman filter/analogue technique was tested for PM 2.5 model forecasts (Djalalova et al, 2015).…”

Section: Conclusion: the Future Forecast Rotementioning

confidence: 99%

The air quality forecast rote: Recent changes and future challenges

Ryan

2016

Journal of the Air & Waste Management Association

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Routine air quality forecasts are now issued for nearly all the major U.S. metropolitan areas. Methods of forecast preparation-the forecast rote-have changed significantly in the last decade. Numerical air quality models have matured and are now an indispensable part of the forecasting process. All forecasting methods, particularly statistically based models, must be continually calibrated to account for ongoing local- and regional-scale emission reductions.

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Application of a statistical post-processing technique to a gridded, operational, air quality forecast

Cited by 32 publications

References 16 publications

Development and demonstration of a Lagrangian dispersion modeling system for real‐time prediction of smoke haze pollution from biomass burning in Southeast Asia

Development and demonstration of a Lagrangian dispersion modeling system for real‐time prediction of smoke haze pollution from biomass burning in Southeast Asia

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England

The air quality forecast rote: Recent changes and future challenges

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