Development of UI-WRF-Chem (v1.0) for the MAIA satellite mission: case demonstration
Notice bibliographique
Résumé
Abstract. The Multi-Angle Imager for Aerosols (MAIA) satellite mission, to be jointly implemented by NASA and the Italian Space Agency with an expected 2026 launch, aims to study how different types of particulate matter (PM) pollution affect human health. The investigation will primarily focus on a discrete set of globally distributed Primary Target Areas (PTAs) containing major metropolitan cities, and will integrate satellite observations, ground observations, and chemical transport model (CTM) outputs to generate maps of near-surface total and speciated PM within the PTAs. In addition, the MAIA investigation will provide satellite measurements of aerosols over a set of Secondary Target Areas (STAs), which are useful for studying air quality more broadly. For the CTM, we have developed a Unified Inputs (of initial and boundary conditions) for WRF-Chem (UI-WRF-Chem) modeling framework to support the MAIA satellite mission. These developments include: (1) application of NASA GEOS FP and MERRA-2 data to provide both meteorological and chemical initial and boundary conditions for performing WRF-Chem simulations of air quality at a fine spatial resolution for both forecast and reanalysis modes; (2) a stand-alone emission preprocessor that ingests both global and regional anthropogenic emission inventories as well as fire emissions; (3) application of MODIS land data to improve land surface properties such as land cover type; (4) application of GLDAS and NLDAS data to constrain surface soil properties such as soil moisture; (5) development of a new soil NOx emission scheme – the Berkeley Dalhousie Iowa Soil NO Parameterization (BDISNP). Here, we illustrate the model improvements because of these developments over four target areas: Beijing in China, CHN-Beijing (STA); Rome in Italy, ITA-Rome (PTA); Los Angeles in the U.S., USA-Angeles (PTA) and Atlanta in the U.S., USA-Atlanta (PTA). UI-WRF-Chem is configured as 2 nested domains using an outer domain (D1) and inner domain (D2) with a 12 km and 4 km spatial resolution, respectively. For each target area, we first run a suite of sensitivity simulations to test the model sensitivity to different options of physics schemes and then select the optimal combination of physics schemes based on evaluation of model simulated meteorology. For the inner domain (D2), we have chosen to turn off the traditional Grell 3D ensemble (G3D) cumulus scheme. We conduct a case study over USA-Atlanta for June 2022 to demonstrate the impacts of cumulus scheme on precipitation and subsequent surface PM2.5 concentration. Our results show that keeping the G3D cumulus scheme on results in higher precipitation and lower PM2.5 than the simulation with the G3D cumulus scheme off. Compared with surface observations of precipitation and PM2.5 concentration, the sensitivity simulation with the G3D scheme off shows better performance than keeping it on. We focus on two dust intrusion events over CHN-Beijing and ITA-Rome, which occurred in March 2018 and June 2023, respectively. We carry out a suite of sensitivity simulations using UI-WRF-Chem by excluding chemical boundary conditions or including MERRA-2 chemical boundary conditions. Our results show that using MERRA-2 data to provide chemical boundary conditions can help improve model simulation of surface PM concentration and AOD. Some of the target areas have also experienced significant changes in land cover and land use over the past decade. Our case study over CHN-Beijing in July 2018 investigates the impacts of improved land surface properties with timely MODIS land data on capturing the urban heat island phenomena. Model-simulated surface skin temperature shows better agreement with MODIS observed land surface temperature. The updated soil NOx emission scheme leads to higher NO2 vertical column density (VCD) in rural areas over CHN-Beijing target area, which matches better with TROPOMI observed NO2 VCD. This in turn affects the simulation of surface nitrate concentration. Lastly, we conduct a case study over USA-LosAngeles to tune the dust emissions. This gives an example to show the fine-tuning work we do over each target area to investigate the problem specific to that target area as we continue evaluating and improving model performance.
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 | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,004 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,005 |
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 ».