Satellite Data Us in PM Management:A Retrospective Assessment

Rudolf B. HusarCAPITA, Washington University

Presented at A&WMA’s 97th Annual Conference and ExhibitionJune 22-27, Indianapolis, IN

MexicanSmoke

Early Satellite Detection of Manmade Haze, 1976

Regional Haze

Low Visibility Hazy ‘Blobs’Lyons W.A., Husar R.B. Mon. Weather Rev. 1976

SMS GOES June 30 1975

Co-retrieval of Aerosol and Surface Reflectance:Analysis of Daily US SeaWiFS Data for 2000-2002

Sean Raffuse, Erin Robinson and Rudolf B. Husar CAPITA, Washington University

Presented at A&WMA’s 97th Annual Conference and ExhibitionJune 22-27, Indianapolis, IN

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

Satellite Aerosol Optical Thickness ClimatologySeaWiFS Satellite, Summer 2000 - 2003

20 Percentile

99 Percentile90 Percentile

60 Percentile

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

Dec, Jan Feb

Sep, Oct, NovJun, Jul, Aug

Mar, Apr, May

SeaWiFS AOT – Summer 60 Percentile1 km Resolution

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.

What kind of neighborhood is this anyway?

May 9, 1998 A Really Bad Aerosol Day for N. America

Asian Smoke

C. American Smoke

Canada Smoke

Near Real Time Public Satellite Data Delivery

Interactive Virtual Workgroup WebsitesJuly 2002 Quebec Smoke

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

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

Results – Seasonal surface reflectance, Western US

Results – Eight month animation

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

Information Techology Vision Scenario: Smoke ImpactREASoN Project: Application of NASA ESE Data and Tools to Particulate Air Quality Management (PPT/PDF)

• Scenario: Smoke form Mexico causes record PM over the Eastern US.

• Goal: Detect smoke emission and predict PM and ozone concentrationSupport air quality management and transportation safety

• Impacts: PM and ozone air quality episodes, AQ standard exceedanceTransportation safety risks due to reduced visibility

• Timeline: Routine satellite monitoring of fire and smokeThe smoke event triggers intensified sensing and analysisThe event is documented for science and management use

• Science/Air Quality Information Needs:Quantitative real-time fire & smoke emission monitoring PM, ozone forecast (3-5 days) based on smoke emissions data

• Information Technology Needs:Real-time access to routine and ad-hoc data and modelsAnalysis tools: browsing, fusion, data/model integrationDelivery of science-based event summary/forecast to air quality and

aviation safety managers and to the public

Record Smoke Impact on PM Concentrations

rhusar@me.wustl.edu, stefan@me.wustl.edu

Smoke Event