USWRP Workshop on Air Quality Forecasting

Dabberdt, Walter F.; Carroll, Mary Anne; Appleby, William; Baumgardner, Darrel; Carmichael, Gregory R.; Davidson, Paula M.; Doran, J. C.; Dye, Timothy S.; Grimmond, Sue; Middleton, Paulette; Neff, W. D.; Zhang, Yang

doi:10.1175/bams-87-2-215

Cited by 6 publications

(4 citation statements)

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“…Prediction of ozone air quality on local and regional scales is key for providing prior warning of impending ozone exceedances (Dabberdt et al, 2004(Dabberdt et al, , 2006. Knowledge of the processes that control the variability in ozone precursors is vital for understanding and predicting ozone air quality.…”

Section: Introductionmentioning

confidence: 99%

“…For prognostic models, uncertainties result from meteorology, the limitations of the photochemical mechanisms, wet and dry deposition, uncertainties in the emissions of ozone precursors, and, for data assimilation, observation uncertainty (Dabberdt et al, 2004(Dabberdt et al, , 2006. Most current statistical and data assimilation air quality forecasting techniques rely primarily on surface observing networks, but satellite observations are increasingly coming to the fore (Lahoz et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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The impact of observing characteristics on the ability to predict ozone under varying polluted photochemical regimes

et al. 2015

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Abstract. We conduct analyses to assess how characteristics of observations of ozone and its precursors affect air quality forecasting and research. To carry out this investigation, we use a photochemical box model and its adjoint integrated with a Lagrangian 4D-variational data assimilation system. Using this framework in conjunction with pseudoobservations, we perform an ozone precursor source inversion and estimate surface emissions. We then assess the resulting improvement in ozone air quality prediction. We use an analytical model to conduct uncertainty analyses. Using this analytical tool, we address some key questions regarding how the characteristics of observations affect ozone precursor emission inversion and in turn ozone prediction. These questions include what the effect is of choosing which species to observe, of varying amounts of observation noise, of changing the observing frequency and the observation time during the diurnal cycle, and of how these different scenarios interact with different photochemical regimes. In our investigation we use three observed species scenarios: CO and NO 2 ; ozone, CO, and NO 2 ; and HCHO, CO and NO 2 . The photochemical model was set up to simulate a range of summertime polluted environments spanning NO x -(NO and NO 2 )-limited to volatile organic compound (VOC)-limited conditions. We find that as the photochemical regime changes, here is a variation in the relative importance of trace gas observations to be able to constrain emission estimates and to improve the subsequent ozone forecasts. For example, adding ozone observations to an NO 2 and CO observing system is found to decrease ozone prediction error under NO x -and VOC-limited regimes, and complementing the NO 2 and CO system with HCHO observations would improve ozone prediction in the transitional regime and under VOC-limited conditions. We found that scenarios observing ozone and HCHO with a relative observing noise of lower than 33 % were able to achieve ozone prediction errors of lower than 5 ppbv (parts per billion by volume). Further, only observing intervals of 3 h or shorter were able to consistently achieve ozone prediction errors of 5 ppbv or lower across all photochemical regimes. Making observations closer to the prediction period and either in the morning or afternoon rush hour periods made greater improvements for ozone prediction: 0.2-0.3 ppbv for the morning rush hour and from 0.3 to 0.8 ppbv for the afternoon compared to only 0-0.1 ppbv for other times of the day. Finally, we made two complementary analyses that show that our conclusions are insensitive to the assumed diurnal emission cycle and to the choice of which VOC species emission to estimate using our framework. These questions will address how different types of observing platform, e.g. geostationary satellites or ground monitoring networks, could support future air quality research and forecasting.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The impact of observing characteristics on the ability to predict ozone under varying polluted photochemical regimes

et al. 2015

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“…Knowledge of the processes that control the variability of ozone precursors is vital for understanding and predicting ozone air quality. Prediction of ozone air quality on local and regional scales is key for providing prior warning of impending ozone exceedances (Dabberdt et al, 2004(Dabberdt et al, , 2006. Currently, a wide variety of techniques are used to predict ozone concentrations ranging from statistically based models (Gardner and Dorling, 2000), neural networks (Yi and Prybutok, 1996), to prognostic models of atmospheric processes that include data assimilation (Grell et al, 2005;Otte et al, 2005;Zhang et al, 2008;Kang et al, 2010).…”

Section: Figures 1 Introductionmentioning

confidence: 99%

“…Currently, a wide variety of techniques are used to predict ozone concentrations ranging from statistically based models (Gardner and Dorling, 2000), neural networks (Yi and Prybutok, 1996), to prognostic models of atmospheric processes that include data assimilation (Grell et al, 2005;Otte et al, 2005;Zhang et al, 2008;Kang et al, 2010). For prognostic models, uncertainties result from meteorology, the limitations of the photochemical mechanisms, wet and dry deposition, uncertainties in the emissions of ozone precursors, and, for data assimilation, observation uncertainty (Dabberdt et al, 2004(Dabberdt et al, , 2006. Current predictive statistical and data assimilation forecasting techniques rely primarily on surface observing networks.…”

Section: Figures 1 Introductionmentioning

confidence: 99%

Observing requirements for geostationary satellites to enable ozone air quality prediction

Hamer

Bowman

Henze

2011

Preprint

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We conduct a variety of analyses to support mission planning for geostationary satellite measurements of atmospheric composition. We carry out a simplified observing system simulation experiment (OSSE) using a photochemical box model and its adjoint integrated with a Lagrangian 4-D-variational data assimilation system. Using this framework in conjunction with pseudo observational constraints we estimate surface emissions and assess the improvement in ozone air quality forecasting and prediction. We use an analytical model as our principle method of conducting uncertainty analyses, which is the primary focus of this work. We investigate the impacts of changing the observed species (e.g., ozone, carbon monoxide (CO), nitrogen dioxide (NO2), and formaldehyde (HCHO)), observation frequency and quality upon the ability to predict the magnitude of summertime peak ozone events, characterize the uncertainties of those predictions, and the performance of the assimilation system. We use three observed species scenarios: CO and NO2; ozone, CO, and NO2; and HCHO, CO and NO2. These scenarios are designed to test the effects of adding observations of either ozone or HCHO to an existing CO and NO2 observing system. The studies were conducted using the photochemical model setup to simulate a range of summertime polluted environments spanning NOx limited to volatile organic compound (VOC) limited conditions. As the photochemical regime changes the relative importance of trace gas observations to constrain emission estimates and subsequent ozone forecasts varies. For example, adding ozone observations to an NO2 and CO observing system is found to decrease ozone prediction error under NOx and VOC limited regimes, and complimenting the NO2 and CO system with HCHO observations would improve ozone prediction in the transitional regime and under VOC limited conditions

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An Air Quality Forecasting Tool over Italy (ForeChem)

Curci

2011

Air Pollution Modeling and Its Application XXI

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USWRP Workshop on Air Quality Forecasting

Cited by 6 publications

References 1 publication

The impact of observing characteristics on the ability to predict ozone under varying polluted photochemical regimes

The impact of observing characteristics on the ability to predict ozone under varying polluted photochemical regimes

Observing requirements for geostationary satellites to enable ozone air quality prediction

An Air Quality Forecasting Tool over Italy (ForeChem)

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