1. GOES-R AEROSOL PRODUCTS AND AND APPLICATIONS APPLICATIONS Ana I. Prados, S. Kondragunta, P. Ciren R. Hoff, K. McCann

2. Why study aerosols ? ● Aerosols & Human health - PM 2.5 & PM 10 are EPA criteria pollutants with respiratory and cardiac implications ● Aerosols & the Environment: - visibility and aesthetics - earth’s climate and radiative balance - ecological balance of lakes, streams, soils and forests (acid rain) ● Aerosols can be used as tracers of transport pathways

3. Aerosol Optical Depth Retrievals -GOES-12 (East) operational http://www.ssd.noaa.gov/PS/FIRE/GASP/gasp.html -GOES-11 (West) pre-operational Aerosol smoke concentrations -From GASP AOD and fire locations Current GOES Imager Aerosol Products

4. GOES-R ABI Aerosol Products Istvan Laszlo at (NOAA/NESDIS) ● Aerosol Optical Depth ● Aerosol Particle size ● Dust/Aerosol loading ● Suspended matter ● Volcanic Ash: Detection and Height.

5. Background composite image LUT (6S Radiative Transfer Model) Retrieved surface reflectivity Retrieved GOES AOD (4x4 km), ½ hour GOES-12 Visible Image Cloud screen: CLAVR method (GOES-12 IR channels 2 and 4) LUT Current GOES AOD Retrieval Algorithm

6. GASP/AERONET Comparisons High correlation in the northeast/midatlantic region, low correlation in central/southwest US, moderate correlation elsewhere

7. GASP/AERONET/MODIS Comparisons

8. Current AOD Algorithm Issues Errors in surface reflectance retrieval, particularly at high solar zenith angle Larger rms differences than MODIS over eastern US due to 1 channel retrieval and lack of SW IR channels Incorrectly identify thick dust/aerosol plumes as cloud due to 1 channel retrieval

9. GOES-11/12 GOES-R ABI Single visible channel retrieval for surface reflectivity and AOD - Improved AOD retrieval over land due to multiple visible channels - Improved surface reflectance retrieval due to additional SW-IR channels No onboard VIS channel calibration Improved accuracy due to onboard calibration No information on particle sizePotential for studying aerosol size/type Single aerosol model, independent of time and space. Greater ability to choose multiple aerosol models Variability in gaseous absorption is not accounted for Total amounts/profiles derived from ABI/HES/climatology Spatial resolution- 4 x 4 km Temporal Resolution- 15 minutes Spatial resolution- 2 x 2 km Temporal Resolution- 5 minutes

10. GOES-R AOD Air Quality Applications Shobha Kondragunta at NOAA/NESDIS ● Pollution Monitoring ● Air Quality Modeling/Forecasting- Assimilation of GASP AODs into air quality models ● GASP/IDEA-Infusing Satellite data into Environmental Applications-Combines satellite and ground based observations ● AODs will be a component of 3D-AQS (3-Dimensional Air Quality System), also to be used for CDC health studies ● Support for NOAA & NASA field campaigns

11. Smoke Regional (industrial) haze Dust

12. GASP and long range transport of aerosols - August 2005

13. GASP/IDEA – A two dimensional Air Quality System MODIS AOD and surface PM 2.5 maps and time series 48-Hour aerosol trajectory forecasts

14. 3D-AQS (3-D Air Quality System) Raymond Hoff, UMBC Lidar adds third vertical dimension GOES AOD- High temporal resolution MODIS AOD AIRS CO

15. Summary Demonstrated Utility of Current GOES Aerosol Optical Depth Good agreement with AERONET & MODIS over the eastern US provides confidence in product for those regions Monitoring of aerosol plumes at high temporal resolution compared to polar orbiting (i.e MODIS) instruments Smoke concentrations (from AOD) help HYSPLYT forecasts Health studies currently underway between CDC and EPA GOES-R Aerosol Optical Depth Retrievals Improved AOD retrievals due to multispectral VIS channel, SW-IR channels, aerosol size/type information, and onboard calibration Improved spatial and temporal resolution over current imager GOES-R and US Air Quality GASP/ IDEA and 3D-AQS - Multiplatform systems for monitoring US Air Quality Improving Air quality (PM 2.5 ) forecasting

16. Acknowledgements ● GASP AOD work was funded by the GIMPAP (DD133E0SSE6814) and G-PSDI programs ● This work was funded in part by the Cooperative Remote Sensing Science and Technology Center (CREST) through a grant from NOAA (Contract Number NA17AE162) and from a NASA Cooperative Agreement (3D-AQS, NNS06AA02A) ● Tony Wimmers (U. Wisconsin) - For providing 2004 & 2005 MODIS data