Deriving Aerosol Optical Thickness (AOT) from satellite data is important for air quality monitoring because such information helps us to identify and track atmospheric particles, including dust and smoke, in the air we breathe.
In the past however, the data processing of this retrieval often required several hours to complete.
Recently, a research team that included ESSIC’s Dr. Jingfeng Huang was recognized with NOAA’s Annual STAR Award for Innovation, by accomplishing the goals of the VIIRS aerosol product validation and significantly reducing the data analysis time required.
The new version of the group’s processing algorithm reduces final product time from approximately six hours to around two. This is achieved according to Huang, by running the VIIRS (Visible Infrared Imaging Radiometer Suite) aerosol algorithm based on the Direct Broadcast datasets that are available at near-real time, as opposed to waiting for the aerosol products produced from the algorithm chain processing at operational centers.
While aerosol retrieval requires clear skies, Huang said the VIIRS aerosol algorithm can be further improved by reducing contamination from clouds and snow.
“If you want to have quick air quality assessment, particularly for public health warnings, it not only requires near-real time data processing but also requires high quality aerosol products,” said Huang. “We need to enhance the algorithm by improving the accuracy and precision [of the data], reducing uncertainty.”
Huang additionally stated that data from sensors like VIIRS can also be combined with data from other sensors such as OMPS (Ozone Mapping and Profiler Suite), or other satellites, such as geostationary satellites, to provide an even more detailed assessment of dust storms and smoke particles in the air.