Aerosol Optical Depth Retrievals over Thick Smoke Aerosols using GOES-17
Severe wildfires generate thick smoke plumes, which degrade particulate matter air quality near the surface. Satellite measurements provide spectacular views of these smoke aerosols and Aerosol Optical Depth (AOD), a columnar measure of aerosol concentration widely used in assessing air quality near...
| Main Authors: | , |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://doi.org/10.5194/amt-2022-303 https://amt.copernicus.org/preprints/amt-2022-303/ |
| Summary: | Severe wildfires generate thick smoke plumes, which degrade particulate matter air quality near the surface. Satellite measurements provide spectacular views of these smoke aerosols and Aerosol Optical Depth (AOD), a columnar measure of aerosol concentration widely used in assessing air quality near the surface. However, these thick smoke plumes often go undetected in satellite imagery, creating missing gaps in these high-pollution areas. In this study, we develop a new algorithm to detect and retrieve AOD from GOES-17 and compare these estimates with the Aerosol Robotic Network (AERONET), MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC), and the current GOES Operational Aerosol Optical Depth (OAOD) product. Using the clear-sky reflectance composite approach to retrieve surface reflectance, AOD accuracy increases 2 %–7 % on different days for optically thin aerosols. We also found that adding information from the red channel in AOD retrieval brings more uncertainties for low AOD retrieval but increased accuracy for high AOD retrieval. After relaxing the maximum detectable AOD values, the number of valid AOD retrievals increases by 80 %, and the accuracy also increases by about 4 % compared to AERONET AOD. Our approach to retrieving AOD has a 386,091 ~ 937,210 square kilometer increase in valid AOD values each day. |
|---|