AQPG Background

NOAA is supporting a number of topical testbeds, called Proving Grounds, to help test the algorithms, visualization and data delivery for the next generation GOES satellite products.

This site describes the Proving Ground for Air Quality Environmental Data Records.

AQPG Activities

AQPG 3rd Annual Workshop

Date and Location:

2013 AQPG Workshop
March 14, 2013
UMBC Campus
Rm 401
Physics Building
Baltimore, MD 21250
AQPG Meeting Agenda
Directions to UMBC

Publications

AQPG Personnel

Prof. Raymond Hoff, UMBC
Prof. Sundar Christopher, UAH
Prof. Barry Gross, CCNY
Dr. Amy Huff, Battelle
Dr. Shobha Kondragunta, NOAA
Dr. Brad Pierce, NOAA
Dr. Kathryn Mozer, NOAA
Dr. Ivanka Stajner, NOAA
Dr. Jim Szykman, EPA

Associates:
Dr. Hai Zhang, UMBC
Mr. Pubu Ciren, NOAA
Mr. Chuanyu Xu, NOAA
Ms. Julia Lu, CCNY

Glossary

Using GOES-R Advanced Baseline Imager (ABI) for Air Quality Applications

GOES-R is the first in the newest generation of NOAA Geostationary Satellites. Scheduled for launch in 2015, GOES-R will be placed in orbit at 135 °W. The GOES-R satellite will include a new instrument for the GOES satellite series, the Advanced Baseline Imager. Giving high resolution images of 500 m resolution for visible channels and < 2km resolution for all channels, GOES ABI will allow new algorithms to be developed to detect and measure aerosols and gases which have not previously been observable from US satelltes. GOES-R ABI channels (figure) at 0.47, 0.64, 0.865, 1.378, 1.61, and 2.25 µm allow the use of channel radiance ratios to better detect aerosols in the atmosphere over highly varying reflectivity surfaces. Like the MODIS and VIIRS series of aerosol instruments, aerosol optical depth will be retrievable at visible wavelengths over both land and water. Unlike MODIS and VIIRS on polar orbiting satellites, GOES-R sits 35,786 kilometers above the Earth in a geosynchronous orbit so that repeatable 5 min images will be retrieved over the Continental US and Canada (CONUS). Every 15 min, the entire disk of the northern and southern hemispheres will be visible. And when events occur, GOES-R can go into "rapid scan" mode and give 30 second images of a restricted region around the event. GOES-R gives us unprecedented ability to monitor air pollution from space over the western hemisphere.

How the AQPG Works

The AQPG provides synthetic imagery which emulates what GOES-R ABI will see after launch. Since this is a totally new instrument, we don't have actual imagery in orbit to use to prepare data sets which illustrate the capability of the instrument. But we cannot wait until after launch if we want the instrument and data used for air quality assessment on day 1 after launch. Instead, we have imagery from MODIS which have similar wavelength bands to what GOES-R will have. Using those radiances, which have been received on orbit, we can map those radiances into the GOES-R bands and reconstruct test images which retrieve aerosol products like MODIS does. But this is not all. We need to show the real value of GOES-R which is the motion of the scene showing aerosol transport and detection of new sources like fires. This requires model interpolation at the same time scale that GOES-R will observe.

Figure 2 shows the process used in the AQPG (click on the image to zoom in). The AQPG partners each share a role in this dataflow. UMBC, CCNY, UW, and UAH identify cases which have interesting air quality implications (fires, dust storms, pollution events, or a mixture of all those). From data taken at the partner sites, we have a significant base of ancillary information which can be used to construct faithful test cases for the ABI algorithm. These data are then modelled using large scale air quality computer models (such as CMAQ or WRF-CHEM) and this provides high temporal resolution for the event. These data are then transmitted to NESDIS who process the output of the computer models as input to the Community Radiative Transfer Model (CRTM) which generates the on-orbit radiances for the GOES-R bands as a function of solar and view angles for the instrument. Those radiances are processed as synthetic data for the ABI aerosol algorithms as they will for the real data in 2015.

These data need to be used for training purposes for the people who will actually use GOES-R on a day to day basis for air quality forecasting and assessment. We have developed an AQPG User Group who will work with us to understand these cases and deliver feedback to the ABI algorithm developers as to the utility of the product. This feedback will allow the ABI algorithm to be modified to deliver the best product to the user community. Finally, the algorithm becomes a basis for satellite derived air quality products (AOD, aerosol types, aerosol transport, and potentially estimated surface PM_2.5) for the Environmental Protection Agency as another end user.