Regional‐scale chemical transport modeling in support of the analysis of observations obtained during the TRACE‐P experiment

Carmichael, G. R.; Tang, Youhua; Kurata, Gakuji; Uno, Itsushi; Streets, D. G.; Woo, Jung Hun; Huang, Hao; Yienger, J. J.; Lefer, B. L.; Shetter, R. E.; Blake, D. R.; Atlas, E.; Fried, Alan; Apel, E. C.; Eisele, F. L.; Cantrell, C. A.; Avery, M. A.; Barrick, J. D.; Sachse, G. W.; Brune, William H.; Sandholm, S. T.; Kondo, Yutaka; Singh, H. B.; Talbot, R. W.; Bandy, A. R.; Thorton, D.; Clarke, A. D.; Heikes, Brian G.

doi:10.1029/2002jd003117

Cited by 210 publications

(198 citation statements)

References 28 publications

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“…This model has been generalized to simulate regional air quality [Tang et al, 2003;Carmichael et al, 2003;Tang et al, 2004], and the latest version is used here. During the TexAQS II experiment, STEM-2K3 provided a multiscale forecast, including a primary domain with 60-km horizontal resolution covering the contiguous United States, and a nested 12-km domain covering Texas.…”

Section: A4 University Of Iowa Stem-2k3 Modelmentioning

confidence: 99%

An evaluation of real‐time air quality forecasts and their urban emissions over eastern Texas during the summer of 2006 Second Texas Air Quality Study field study

et al. 2009

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[1] Forecasts of ozone (O 3 ) and particulate matter (diameter less than 2.5 mm, PM 2.5 ) from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected during August and September of 2006 (49 days) through the Aerometric Information Retrieval Now (AIRNow) network throughout eastern Texas and adjoining states. Ensemble O 3 and PM 2.5 forecasts created by combining the seven separate forecasts with equal weighting, and simple bias-corrected forecasts, are also evaluated in terms of standard statistical measures, threshold statistics, and variance analysis. For O 3 the models and ensemble generally show statistical skill relative to persistence for the entire region, but fail to predict high-O 3 events in the Houston region. For PM 2.5 , none of the models, or ensemble, shows statistical skill, and all but one model have significant low bias. Comprehensive comparisons with the full suite of chemical and aerosol measurements collected aboard the NOAA WP-3 aircraft during the summer 2006 Second Texas Air Quality Study and the Gulf of Mexico Atmospheric Composition and Climate Study (TexAQS II/GoMACCS) field study are performed to help diagnose sources of model bias at the surface. Aircraft flights specifically designed for sampling of Houston and Dallas urban plumes are used to determine model and observed upwind or background biases, and downwind excess concentrations that are used to infer relative emission rates. Relative emissions from the U.S. Environmental Protection Agency 1999 National Emission Inventory (NEI-99) version 3 emissions inventory (used in two of the model forecasts) are evaluated on the basis of comparisons between observed and model concentration difference ratios. Model comparisons demonstrate that concentration difference ratios yield a reasonably accurate measure (within 25%) of relative input emissions. Boundary layer height and wind data are combined with the observed up-wind and downwind concentration differences to estimate absolute emissions. When the NEI-99 inventory is modified to include observed NO y emissions from continuous monitors and expected NO x decreases from mobile sources between 1999 and 2006, good agreement is found with those derived from the observations for both Houston and Dallas. However, the emission inventories consistently overpredict the ratio of CO to NO y . The ratios of ethylene and aromatics to NO y are reasonably consistent with observations over Dallas, but are significantly underpredicted for Houston.

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Section: A4 University Of Iowa Stem-2k3 Modelmentioning

confidence: 99%

An evaluation of real‐time air quality forecasts and their urban emissions over eastern Texas during the summer of 2006 Second Texas Air Quality Study field study

et al. 2009

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“…The numerical tests use the state-of-the-art regional atmospheric chemical transport model STEM [1]. The simulation covers a region of 7200 Km × 4800 Km in East Asia and uses a 30×20×18 computational grid with a horizontal resolution of 240 Km × 240 Km.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The simulated conditions correspond to March 2001. More details about the forward model simulation conditions and comparison with observations are available in [1]. The forward and adjoint models are parallelized using PAQMSG [12].…”

Section: Numerical Resultsmentioning

confidence: 99%

Total Energy Singular Vectors for Atmospheric Chemical Transport Models

Liao

Sandu

2005

Lecture Notes in Computer Science

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Abstract. The aim of this paper is to address computational aspects of the total energy singular vector analysis of atmospheric chemical transport models. We discuss the symmetry of the tangent-linear/adjoint operator for stiff systems. Results for a 3D simulation with real data reveal that the total energy singular vectors depend on the target domain, simulation window, chemical reactions, and meteorological data.

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“…To illustrate how this information can be used adjoint sensitivity analysis results for a cost function that measures ground level ozone concentration in the Dallas-Fort-Worth (DFW) receptor area using the STEM model [17] are presented in Fig. 5.…”

Section: Adjoint Sensitivity Analysis Resultsmentioning

confidence: 99%

“…The capabilities of current CTMs used in air quality forecasting are illustrated using results from the STEM model application during the NASA TRACE-P (Tropospheric Atmospheric Chemistry Pacific Experiment [60]) study [17].…”

Section: Air Quality Forecasting Performancementioning

confidence: 99%

Predicting Air Quality: Current Status and Future Directions

Carmichael

Sandu

Chai

et al.

Air Pollution Modeling and Its Application XIX

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Air quality prediction plays an important role in the management of our environment. Computational power and efficiencies have advanced to the point where chemical transport models can predict pollution in an urban air shed with spatial resolution less than a kilometer, and cover the globe with a horizontal resolution of less than 50 km. Predicting air quality remains a challenge due to the complexity of the governing processes and the strong coupling across scales. While air quality prediction is closely aligned with weather prediction, there are important differences, including the role of pollution emissions and their associated large uncertainties. As more atmospheric chemical observations become available chemical data assimilation is expected to play an essential role in air quality forecasting. In this paper the current status of air quality forecasting is discussed and illustrated by comparison of predictions with observations. The future directions are also discussed, with an emphasis on data assimilation. Applications of the four dimensional variational method (4D-Var) and the ensemble Kalman filter (EnKF) approach are presented and discussed.

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Regional‐scale chemical transport modeling in support of the analysis of observations obtained during the TRACE‐P experiment

Cited by 210 publications

References 28 publications

An evaluation of real‐time air quality forecasts and their urban emissions over eastern Texas during the summer of 2006 Second Texas Air Quality Study field study

An evaluation of real‐time air quality forecasts and their urban emissions over eastern Texas during the summer of 2006 Second Texas Air Quality Study field study

Total Energy Singular Vectors for Atmospheric Chemical Transport Models

Predicting Air Quality: Current Status and Future Directions

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