Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements
Abstract While multiple information sources exist concerning surface‐level air pollution, no individual source simultaneously provides large‐scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the stre...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2021-07-01
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| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2021EA001743 |
| _version_ | 1818613692223193088 |
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| author | C. Malings K. E. Knowland C. A. Keller S. E. Cohn |
| author_facet | C. Malings K. E. Knowland C. A. Keller S. E. Cohn |
| author_sort | C. Malings |
| collection | DOAJ |
| description | Abstract While multiple information sources exist concerning surface‐level air pollution, no individual source simultaneously provides large‐scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross‐validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground‐based measurements only. Results also indicate the potential for near‐term updating of forecasts based on recent ground measurements. |
| first_indexed | 2024-12-16T16:06:09Z |
| format | Article |
| id | doaj.art-08bb06aa29484776ac949e5cc255f8cf |
| institution | Directory Open Access Journal |
| issn | 2333-5084 |
| language | English |
| last_indexed | 2024-12-16T16:06:09Z |
| publishDate | 2021-07-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj.art-08bb06aa29484776ac949e5cc255f8cf2022-12-21T22:25:21ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842021-07-0187n/an/a10.1029/2021EA001743Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground MeasurementsC. Malings0K. E. Knowland1C. A. Keller2S. E. Cohn3Goddard Space Flight Center NASA Postdoctoral Program Fellow Greenbelt MD USAGoddard Space Flight Center Global Modeling and Assimilation Office Greenbelt MD USAGoddard Space Flight Center Global Modeling and Assimilation Office Greenbelt MD USAGoddard Space Flight Center Global Modeling and Assimilation Office Greenbelt MD USAAbstract While multiple information sources exist concerning surface‐level air pollution, no individual source simultaneously provides large‐scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross‐validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground‐based measurements only. Results also indicate the potential for near‐term updating of forecasts based on recent ground measurements.https://doi.org/10.1029/2021EA001743Air qualityurbanmodelingforecastingGEOS‐CFTROPOMI |
| spellingShingle | C. Malings K. E. Knowland C. A. Keller S. E. Cohn Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements Earth and Space Science Air quality urban modeling forecasting GEOS‐CF TROPOMI |
| title | Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements |
| title_full | Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements |
| title_fullStr | Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements |
| title_full_unstemmed | Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements |
| title_short | Sub‐City Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements |
| title_sort | sub city scale hourly air quality forecasting by combining models satellite observations and ground measurements |
| topic | Air quality urban modeling forecasting GEOS‐CF TROPOMI |
| url | https://doi.org/10.1029/2021EA001743 |
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