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Satellite Data Us in PM Management: A Retrospective Assessment

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Title: Satellite Data Us in PM Management: A Retrospective Assessment


1
Satellite Data Us in PM ManagementA
Retrospective Assessment
MexicanSmoke
  • Rudolf B. Husar
  • CAPITA, Washington University
  • Presented at AWMAs 97th Annual Conference and
    Exhibition
  • June 22-27, Indianapolis, IN

2
Early Satellite Detection of Manmade Haze, 1976
Low Visibility Hazy Blobs Lyons W.A., Husar
R.B. Mon. Weather Rev. 1976
Regional Haze
SMS GOES June 30 1975
3
Co-retrieval of Aerosol and Surface
ReflectanceAnalysis of Daily US SeaWiFS Data
for 2000-2002
  • Sean Raffuse, Erin Robinson and Rudolf B. Husar
  • CAPITA, Washington University
  • Presented at AWMAs 97th Annual Conference and
    Exhibition
  • June 22-27, Indianapolis, IN

4
Results Seasonal surface reflectance, Eastern US
5
SeaWiFS Satellite Platform and Sensors
Designed for Vegetation Detection
Chlorophyll Absorption
  • 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

6
Satellite Aerosol Optical Thickness
ClimatologySeaWiFS Satellite, Summer 2000 - 2003
20 Percentile
60 Percentile
99 Percentile
90 Percentile
7
Satellite AOT Time Fraction (0-100)SeaWiFS
Satellite, Summer 2000 - 2003
Dec, Jan Feb
Mar, Apr, May
Sep, Oct, Nov
Jun, Jul, Aug
8
SeaWiFS AOT Summer 60 Percentile1 km Resolution
9
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.

10
(No Transcript)
11
May 9, 1998 A Really Bad Aerosol Day for N.
America
Asian Smoke
Canada Smoke
  • What kind of neighborhood is this anyway?

C. American Smoke
12
Near Real Time Public Satellite Data Delivery
13
Interactive Virtual Workgroup WebsitesJuly 2002
Quebec Smoke
14
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

15
Results Seasonal surface reflectance, Western US
April 29, 2000, Day 120
July 18, 2000, Day 200
October 16, 2000, Day 290
16
Results Eight month animation
17
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

R (R0 (e-t 1) P) e-t
Aerosol as Filter Ta e-t
Aerosol as Reflector Ra (e-t 1) P
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

18
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
    concentration
  • Support air quality management and transportation
    safety
  • Impacts
  • PM and ozone air quality episodes, AQ standard
    exceedance
  • Transportation safety risks due to reduced
    visibility
  • Timeline
  • Routine satellite monitoring of fire and smoke
  • The smoke event triggers intensified sensing and
    analysis
  • The 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

Record Smoke Impact on PM Concentrations
Smoke Event
rhusar_at_me.wustl.edu, stefan_at_me.wustl.edu
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