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SH for Improving PM2.5 forecasts
-Aerosol Optical Depth (AOD) data sets are used from satellite instruments MODIS Terra and Aqua and VIIRS which included a combination of the Dark Tark and Deep Blue algorithms and AOD from the NASA AERONET Network.
-Surface PM2.5 data sets are from the State and Local Monitoring Station and Interagency Monitoring of Protected Visual Environments Networks.
-PM2.5 model based data sets are from 3 separate chemical transport models; GEOS-Chem, WRF-Chem, and WRF-CMAQ. The EPA WRF-CMAQ data set is publicly available via the U.S. EPA Remote Sensing Information Gateway application. For CMAQ data access, users must first download and install the RSIG application at: https://www.epa.gov/hesc/remote-sensing-information-gateway.
This dataset is associated with the following publication:
Zhang, H., J. Wang, L. Castro Garcia, M. Zhou, C. Ge, T. Plessel, J. Szykman, R. Levy, B. Murphy, and T. Spero. Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction With Satellite Data for Rural Areas. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 127(1): e2021JD035563, (2022).
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
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|
| contactPoint |
{
"fn": "James Szykman",
"hasEmail": "mailto:szykman.jim@epa.gov"
}
|
| description | -Aerosol Optical Depth (AOD) data sets are used from satellite instruments MODIS Terra and Aqua and VIIRS which included a combination of the Dark Tark and Deep Blue algorithms and AOD from the NASA AERONET Network. -Surface PM2.5 data sets are from the State and Local Monitoring Station and Interagency Monitoring of Protected Visual Environments Networks. -PM2.5 model based data sets are from 3 separate chemical transport models; GEOS-Chem, WRF-Chem, and WRF-CMAQ. The EPA WRF-CMAQ data set is publicly available via the U.S. EPA Remote Sensing Information Gateway application. For CMAQ data access, users must first download and install the RSIG application at: https://www.epa.gov/hesc/remote-sensing-information-gateway. This dataset is associated with the following publication: Zhang, H., J. Wang, L. Castro Garcia, M. Zhou, C. Ge, T. Plessel, J. Szykman, R. Levy, B. Murphy, and T. Spero. Improving Surface PM2.5 Forecasts in the United States Using an Ensemble of Chemical Transport Model Outputs: 2. Bias Correction With Satellite Data for Rural Areas. JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 127(1): e2021JD035563, (2022). |
| distribution |
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|
| identifier | https://doi.org/10.23719/1523478 |
| keyword |
[
"PM2.5 air quality forecast",
"Satellite Air Quality",
"air quality",
"ensemble modeling"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2022-07-07 |
| programCode |
[
"020:000"
]
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| publisher |
{
"name": "U.S. EPA Office of Research and Development (ORD)",
"subOrganizationOf": {
"name": "U.S. Environmental Protection Agency",
"subOrganizationOf": {
"name": "U.S. Government"
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|
| references |
[
"https://doi.org/10.1029/2021jd035563"
]
|
| rights |
null
|
| title | SH for Improving PM2.5 forecasts |
