Comment on acp-2021-343
Notice bibliographique
Résumé
Abstract. We investigate the potential for aircraft-based top-down emission rate retrieval over- and under-estimation using a regional chemical transport model, the Global Environmental Multiscale-Modeling Air-Quality and CHemistry (GEM-MACH). In our investigations we consider the application of the mass-balance approach in the Top-down Emission Rate Retrieval Algorithm (TERRA). Aircraft-based mass-balance retrieval methodologies such as TERRA require relatively constant meteorological conditions and source emission rates to reliably estimate emission rates from aircraft observations. Avoiding cases where meteorology and emission rates change significantly is one means of reducing emissions retrieval uncertainty, and quantitative metrics that may be used for retrieval accuracy estimation are therefore desirable. Using these metrics has the potential to greatly improve emission rate retrieval accuracy. Here, we investigate the impact of meteorological variability on mass-balance emission rate retrieval accuracy by using model-simulated fields as a proxy for real-world chemical and meteorological fields, in which virtual aircraft sampling of the GEM-MACH output was used for top-down mass balance estimates. We also explore the impact of upwind emissions from nearby sources on the accuracy of the retrieved emission rates. This approach allows the state of the atmosphere used for top-down estimates to be characterized in time and 3D space; the input meteorology and emissions are “known”, and thus potential means for improving emission rate retrievals and determining the factors affecting retrieval accuracy may be investigated. We found that emissions retrieval accuracy is correlated with three key quantitative criteria, evaluated a priori from forecasts and/or from observations during the sampling period: (1) changes to the atmospheric stability (described as the change in gradient Richardson number), (2) variations in the direction of transport, as a result of plume vertical motion and in the presence of vertical wind shear, and (3) the combined effect of the upwind-to-downwind concentration ratio and the upwind-to-downwind concentration standard deviations. We show here that cases where these criteria indicate high temporal variability and/or high upwind emissions can result in “storage-and-release” events within the sampled region (control volume), which decrease emission rate retrieval accuracy. Storage-and-release events may contribute the bulk of mass-balance emission rate retrieval under- and over-estimates, ranging in the tests carried out here from −25 % to 24 % of the known (input) emissions, with a median of −2 %. Our analysis also includes two cases with unsuitable meteorological conditions and/or significant upwind emissions to demonstrate conditions which may result in severe storage, which in turn cause emission rate under-estimates by the mass-balance approach. We also introduce a sampling strategy whereby the emission rate retrieval under- and over-estimates associated with storage-and-release are greatly reduced (to −14 % to +5 %, respectively, relative to the magnitude of the known emissions). We recommend repeat flights over a given facility and/or time-consecutive upwind and downwind (remote) vertical profiling of relevant fields (e.g., tracer concentrations) in order to measure and account for the factors associated with storage-and-release events, estimate the temporal trends in the evolution of the system during the flight/sampling time, and partially correct for the effects of meteorological variability and upwind emissions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,026 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,272 | 0,222 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».