GOES-R AEROSOL PRODUCTSGOES-R AEROSOL PRODUCTS ANDAND APPLICATIONSAPPLICATIONS Ana I. Prados, S. Kondragunta, P. Ciren

R. Hoff, K. McCann

Why study aerosols ?● Aerosols & Human health - PM2.5 & PM10 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

• 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

GOES-R ABI Aerosol ProductsIstvan Laszlo at (NOAA/NESDIS)

● Aerosol Optical Depth● Aerosol Particle size● Dust/Aerosol loading● Suspended matter● Volcanic Ash: Detection and Height.

Background composite image

LUT (6S RadiativeTransfer 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

GASP/AERONET Comparisons

High correlation in the northeast/midatlantic region, low correlation in central/southwest US , moderate correlation elsewhere

GASP/AERONET/MODIS Comparisons

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 retrievaland lack of SW IR channels

• Incorrectly identify thick dust/aerosol plumes as cloud due to 1 channel retrieval

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 size Potential 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

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

Smoke Regional (industrial) haze

Dust

GASP and long range transport of aerosols - August 2005

GASP/IDEA – A two dimensional Air Quality System

• MODIS AOD and surface PM2.5 maps and time series

• 48-Hour aerosol trajectory forecasts

3D-AQS (3-D Air Quality System) Raymond Hoff, UMBC

Lidar adds third vertical dimension

GOES AOD- High temporal resolution

MODIS AOD

AIRS CO

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 (PM2.5) forecasting

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