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Enregistrement W4417154471 · doi:10.5194/egusphere-2025-4323

Operational chemical weather forecasting with the ECCC online Regional Air Quality Deterministic Prediction System version 023 (RAQDPS023) – Part 1: System description

2025· article· en· W4417154471 sur OpenAlexaffabout
Michael D. Moran, Verica Savic-Jovcic, Craig Stroud, Sylvain Ménard, Wanmin Gong, Junhua Zhang, Qiong Zheng, Jack Chen, Ayodeji Akingunola, Alexandru Lupu, Konstantinos Menelaou, Rodrigo Muñoz‐Alpizar

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueAtmospheric chemistry and aerosols
Établissements canadiensEnvironment and Climate Change Canada
Organismes subventionnairesnon disponible
Mots-clésUpstream (networking)Air quality indexTropical cyclone forecast modelNumerical weather predictionGlobal Forecast SystemIndex (typography)Key (lock)Downstream (manufacturing)Pollutant

Résumé

récupéré en direct d'OpenAlex

Abstract. The online version of the Regional Air Quality Deterministic Prediction System (RAQDPS) is a chemical weather forecast system that has been employed operationally by Environment and Climate Change Canada (ECCC) since 2009. It is run twice per day to produce 72-hour forecasts of hourly 10 km abundance fields of three key predictands, NO2, O3, and PM2.5 total mass, as well as other gas-phase chemical species, PM2.5 chemical components, and dry and wet deposition for Canada, the contiguous U.S., and northern Mexico. The forecasts of NO2, O3, and PM2.5 are needed to calculate the Air Quality Health Index (AQHI), which is used to communicate current and forecasted pollutant levels to the Canadian public. Version 023 of the RAQDPS (RAQDPS023) went into service at ECCC in December 2021 and was replaced by the RAQDPS025 in June 2024. This paper provides the first full description of any version of the online RAQDPS. After giving a brief history of the ECCC operational air quality forecasting program, we provide a comprehensive description of the RAQDPS023 forecast system as well as shorter descriptions of several upstream and downstream forecast and analysis systems. The latter include two upstream operational meteorological forecast systems that were based on version 5.1.0 of the ECCC Global Environmental Multiscale (GEM) numerical weather prediction model, one which used a global configuration, the Global Deterministic Prediction System (GDPS 8.0.0), and the other which used a regional configuration, the Regional Deterministic Prediction System (RDPS 8.0.0). An emissions processing system, an Updateable Model Output Statistics-based system for bias-corrected station-specific pollutant concentration forecasts (UMOS-AQ), and a regional objective analysis system for surface pollutant concentration fields, the Regional Deterministic Air Quality Analysis system (RDAQA 2.0.0), are also described. The RAQDPS023 itself consisted of version 3.1.0.0 of the GEM-Modelling Air quality and CHemistry (GEM-MACH) chemistry module, which was embedded with one-way coupling within GEM 5.1.0, its meteorological host model. The meteorological configuration of the RAQDPS023 closely followed that of the RDPS 8.0.0. Details covered in this paper include a summary of the dynamical representations and physical parameterizations used in the three GEM-based forecast systems, which are closely harmonized, the chemical parameterizations used in the MACH chemistry module, numerical solvers, system inputs, including both anthropogenic and natural emissions of chemical species, system outputs, and run configuration, strategies, and timings. One simplification employed to reduce RAQDPS023 execution time for operational deployment was to represent the particulate matter (PM) size distribution with only two aerosol particle size bins, one corresponding to particle diameters in the 0–2.5 µm range (“fine particles” or PM2.5) and the other to the 2.5–10 µm range (“coarse fraction” or PMcf). A second simplification was to represent the chemical composition of PM2.5 with only nine chemical components, and a third simplification was to use a longer time step (900 s) for the time integration of atmospheric chemistry than the time step used for time integration of atmospheric dynamics and physics (300 s). Even so, activating the MACH module increased RAQDPS023 run time by a factor of 4.4 on average compared to meteorology only, partly due to the cost of the integration of chemistry but partly to the increased cost of integration of the GEM dynamical core due to the advection with imposed shape preservation and mass conservation of 57 additional chemical tracers. The role of the RAQDPS-FW023, a second chemical weather forecast system that was identical to the RAQDPS023 except for the addition of near-real-time biomass burning emissions, is also described. Biomass burning emissions for Canada and the U.S. estimated from satellite measurements were first calculated by the Canadian Forest Fire Emissions Prediction System (CFFEPS) version 4.1 before each RAQDPS-FW023 run was launched. Outputs from the two RAQDPS versions were then used to produce forecasts of wildfire smoke transport and diffusion. The paper closes by summarizing the key upgrades made to the RAQDPS025, the current version of the ECCC operational chemical weather forecast system, and then describing some possible future improvements and updates. A companion paper by Moran et al. (2025) presents the results of a comprehensive, five-year performance evaluation of prospective and retrospective annual air quality simulations made with the RAQDPS023.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: aucune
Score de désaccord entre enseignants0,998
Score d'incertitude au seuil0,195

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,006
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0020,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0280,019

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.

Tête enseignante Opus0,034
Tête enseignante GPT0,223
Écart entre enseignants0,189 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission2
Résumé présentoui

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