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Record W2005160638 · doi:10.1029/2008jd011697

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

2009· article· en· W2005160638 on OpenAlexaff
S. A. McKeen, Georg Grell, Steven E. Peckham, James M. Wilczak, Irina V. Djalalova, E.‐Y. Hsie, G. J. Frost, Jeff Peischl, Joshua P. Schwarz, R. Spackman, John W. Holloway, J. A. de Gouw, C. Warneke, V. S. Bouchet, Stéphane Gaudreault, Jacinthe Racine, John N. McHenry, Jeff McQueen, P. Lee, Youhua Tang, Gregory R. Carmichael, Rohit Mathur

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsEnvironmental scienceAir quality indexMeteorologyParticulatesAtmospheric sciencesClimatologyAerosolSampling (signal processing)StatisticsGeographyMathematics

Abstract

fetched live from OpenAlex

Forecasts of ozone (O3) and particulate matter (diameter less than 2.5 μm, PM2.5) 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 O3 and PM2.5 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 O3 the models and ensemble generally show statistical skill relative to persistence for the entire region, but fail to predict high‐O3 events in the Houston region. For PM2.5, 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 NOy emissions from continuous monitors and expected NOx 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 NOy. The ratios of ethylene and aromatics to NOy are reasonably consistent with observations over Dallas, but are significantly underpredicted for Houston. Excess ratios of PM2.5 to NOy reasonably match observations for most models but the organic carbon fraction of PM2.5 is significantly underpredicted, pointing to compensating error between secondary organic aerosol (SOA) formation and primary emissions within the models' photochemistry and emissions. Rapid SOA formation associated with both Houston and Dallas is inferred to occur within 1 to 3 h downwind of the urban centers, and none of the models reproduce this feature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.068
GPT teacher head0.375
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations96
Published2009
Admission routes1
Has abstractyes

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