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Home > Documents > April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface...

April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface...

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Satellite Aerosol Optical Thickness Climatology SeaWiFS Satellite, Summer Percentile 99 Percentile90 Percentile 60 Percentile
10
April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US
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Page 1: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290

Results – Seasonal surface reflectance, Eastern US

Page 2: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

SeaWiFS Satellite Platform and Sensors

• Satellite maps the world daily in 24 polar swaths

• The 8 sensors are in the transmission windows in the visible & near IR

• Designed for ocean color but also suitable for land color detection, particularly of vegetation

Swath

2300 KM

24/day

Polar Orbit: ~ 1000 km, 100 min.

Equator Crossing: Local NoonChlorophyll Absorption

Designed for Vegetation Detection

Page 3: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

Satellite Aerosol Optical Thickness ClimatologySeaWiFS Satellite, Summer 2000 - 2003

20 Percentile

99 Percentile90 Percentile

60 Percentile

Page 4: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

Satellite AOT – Time Fraction (0-100%)SeaWiFS Satellite, Summer 2000 - 2003

Dec, Jan Feb

Sep, Oct, NovJun, Jul, Aug

Mar, Apr, May

Page 5: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

SeaWiFS AOT – Summer 60 Percentile1 km Resolution

Page 6: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

Technical Challenge: Characterization

• PM characterization requires many different instruments and analysis tools.

• Each sensor/network covers only a limited fraction of the 8-D PM data space.

• Most of the 8D PM pattern is extrapolated from sparse measured data.• Some devices (e.g. single particle electron microscopy) measure only a

small subset of the PM; the challenge is extrapolation to larger space-time domains.

• Others, like satellites, integrate over height, size, composition, shape, and mixture dimensions; these data need de-convolution of the integral measures.

Page 7: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

Summary

• Satellite data have aided the science of Particulate Matter since the 1970s

• Satellite data have supported PM air quality management since the 1990s.

• Past satellite data helped the qualitative description of PM spatial pattern

• Quantitative satellite data use and fusion with surface data is still in infancy

• Satellite data applications will require collaboration across disciplines

Page 8: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

April 29, 2000, Day 120 July 18, 2000, Day 200 October 16, 2000, Day 290

Results – Seasonal surface reflectance, Western US

Page 9: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

Results – Eight month animation

Page 10: April 29, 2000, Day 120 July 18, 2000, Day 200October 16, 2000, Day 290 Results – Seasonal surface reflectance, Eastern US.

Apparent Surface Reflectance, R• The surface reflectance R0 is obscured by aerosol scattering and absorption before it reaches the sensor

• Aerosol acts as a filter of surface reflectance and as a reflector solar radiation

Aerosol as Reflector: Ra = (e-– 1) P

R = (R0 + (e-– 1) P) e-

Aerosol as Filter: Ta = e-

Surface reflectance R0

• The apparent reflectance , R, detected by the sensor is: R = (R0 + Ra) Ta

• Under cloud-free conditions, the sensor receives the reflected radiation from surface and aerosols• Both surface and aerosol signal varies independently in time and space

• Challenge: Separate the total received radiation into surface and aerosol components


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