Optimizing Top-down Airborne Retrievals through High and Super-Resolution Numerical Modelling
Notice bibliographique
Résumé
A multi-scale-modelling study of conventional top-down source emission-rate estimation methodologies was conducted. Two modelling systems were employed: Environment and Climate Change Canada's regional air quality model GEM-MACH at 2.5km resolution (high-resolution), and Weather Research and Forecasting (WRF) with ARW dynamical core at 50m resolution (super-resolution).\n\nUsing GEM-MACH, high-resolution air-quality model simulations were conducted for the period of an airborne campaign in 2013 over the Canadian oil sands facilities. Modelling products from these simulations were analyzed to investigate the application of the mass-balance technique in aircraft-based retrievals. The focus was on exploring the theoretical aspects and the underlying assumptions of the mass-balance technique. An extensive range of realistic meteorological and source emission conditions were considered. It was demonstrated how temporal variability in meteorology/emission conditions can give rise to storage-and-release events, where mass-balancing using only aircraft measurements can result in significant under-/over-estimates.\n\nUsing WRF-ARW, super-resolution (<100 m) model simulations with Large-Eddy-Simulation (LES) subgrid-parameterization were developed/implemented. The objective was to resolve smaller dynamical processes at the spatio-temporal scales of the airborne measurements. This was achieved by multi-domain model nesting in the horizontal, grid-refining in the vertical, and down-scaling of reanalysis data from 31.25 km to 50 m. Further, WRF dynamical-solver source code was modified to simulate passive-tracer emissions within the finest resolution domain. Different meteorological case studies and several tracer emission sources were considered. Model-generated fields were evaluated against observational data and also in terms of tracer mass-conservation, results indicated high model performance.\n\nUsing the model output from the WRF super-resolution simulations, conventional aircraft-based retrievals were simulated/evaluated. It was shown that conventional methods can result in estimates with 30-50% uncertainty/error. Two major sources of uncertainty were identified: (a) the spatio-temporal variability in the sampled fields, and (b) the gap of information below the flight level. Optimal flight-time around one hour and sampling-distance between 10-15 km, were shown to minimize the uncertainty arising from (a)-(b). Finally, a new sampling/retrieval strategy is introduced where aircraft-based in-situ and remote measurements can be combined to improve the accuracy of top-down estimates by up to 30%. This method utilizes remote sensing to fill the information gap below flight level, characterize temporal trends in the environmental fields during flight-time (to estimate storage-rate), while reducing the required flight-time for more accurate source emission rate estimates.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».