Aerosol Characterization Using the SeaWiFS Sensor and Surface Data

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Aerosol Characterization Using the SeaWiFS Sensor
and Surface Data

• E. M. Robinson and R. B. Husar
• Washington University, St. Louis, MO 63130

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Technical Challenge Aerosol Characterization

• PM characterization needs many different
  instruments and analysis tools
• Each sensor/network covers only a fraction of the
  6-Dim PM data space (X, Y, Z, T, Diameter,
  Composition)
• Most of the 6D pattern is extrapolated from
  sparse measured data

• Satellites, integrate over height H, size D,
  composition C, shape, and mixture dimensions
  these data need de-convolution of the integral
  measures.

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Co-Retrieval of Surface and Aerosol
PropertiesApparent Surface Reflectance, R

• The surface reflectance R0 objects viewed from
  space is modified by aerosol scattering and
  absorption.
• The apparent reflectance, R, is R (R0 Ra)
  Ta

Aer. Transmittance Both R0 and Ra are attenuated
by aerosol extinction Ta which act as a filter
Aerosol Reflectance Aerosol scattering acts as
reflectance, Ra adding airlight to the surface
reflectance
Surface Reflectance The surface reflectance R0
is an inherent characteristic of the surface
Apparent Reflectance R may be smaller or larger
then R0, depending on aerosol reflectance and
filtering.
Aerosol as Filter Ta e-t
Aerosol as Reflector Ra (e-t 1) P
R (R0 (e-t 1) P) e-t
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Aerosols Increase of Decrease the Surface
Reflectance, f(P/R0)
The critical parameter influencing the apparent
reflectance, R, is the ratio of aerosol phase
function (angular reflectance), P, to
bi-directional surface reflectance, R0, (P/ R0)
Aerosols will increase the apparent surface
reflectance, R, if P/R0 lt 1. For this reason,
the reflectance of ocean and dark vegetation
increases with t. When P/R0 gt 1, aerosols will
decrease the surface reflectance. Accordingly,
the brightness of clouds is reduced by overlying
aerosols. At P R0 the reflectance is unchanged
by haze aerosols (e.g. soil and vegetation at 0.8
um).. At large t (radiation equilibrium), both
dark and bright surfaces asymptotically approach
the aerosol reflectance, P
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Haze Effect on Spectral Reflectance over Land
The spectral reflectance of vegetation in the
visible ? is low at 0.01ltR0lt0.1. Haze
significantly enhances the reflectance in the
blue but the haze excess in the near IR is small.
This is consistent with radiative transfer theory
of haze impact.
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Co-Retrieval Seasonal Surface Reflectance,
Eastern US

• April 29, 2000, Day 120

July 18, 2000, Day 200
October 16, 2000, Day 290
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Kansas Agricultural Smoke, April 12, 2003
Organics 35 ug/m3 max
Fire Pixels
PM25 Mass, FRM 65 ug/m3 max
Ag Fires
SeaWiFS, Refl
SeaWiFS, AOT Col
AOT Blue
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April 12, 2003
Kansas Smoke
April 10, 2003
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Kansas Smoke Emission Estimation
April 11, 2003
Assuming Mass Extinction Efficiency 5 m2/g
April 10 1240 T/d
April 11 87 T/day
Monte Carlo Diagnostic Local Model
Emission Estimate
Fire Pixels
Surface Observations
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Find Total Concentration

• Used Blue band (.4um) because smoke has the
  strongest signal here
• By eyeball, at first identifying worm shape in
  the AOT
• Using cross sections or profiles to better see
  the edge of the plume
• Stretched image so that the plume is actually cut
  out

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Background Concentration

• Identified individual halos using a small pixel
  margin around each plume edge
• Background changes from area to area

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Quantification of smoke using AOT

• For each area circled there is an average column
  concentration (mass/area)
• With the total and background concentrations the
  plume concentration is
• Plume Concentration Total Background
• Multiplying by the concentration by its area
  gives the mass of each plume
• The sum of the plumes is the amount accumulated
  up to 12pm when the satellite passes over
• Total Mass 87 tons

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Yesterdays Smoke and Todays Smoke
Yesterdays smoke is not in a plume worm shape
and it has moved a distance
Todays smoke is distinct plumes
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Quantifying yesterdays smoke

• Using the same procedure we calculated the amount
  of smoke.
• We found that the area covered is 60,000km2 and
  the average concentration was .02g/m2
• Total mass 1200 tons

The amount produced up to 12pm is only 7 of the
amount that was produced the day before
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Satellite-Surface-Model Data IntegrationSmoke
Emission Estimation
Continuous Smoke Emissions
Assimilated Smoke Emission for Available Data
Local Smoke Simulation Model
e..g. MM5 winds, plume model
Assimilated Smoke Pattern
Assimilated Fire Location
Satellite Smoke
Surface Smoke
Fire Location
AOT Aer. Retrieval
Fire Pixel, Field Obs
Visibility, AIRNOW
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Satellite Aerosol Optical Thickness
ClimatologySeaWiFS Satellite, Summer 2000 - 2003
20 Percentile
60 Percentile
Smoke Sources 98 Percentile
98 Percentile
90 Percentile
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• Summer AOT 60 Percentile
• 2000-2004 SeaWiFS AOT, 1 km Resolution

Birmingham
Atlanta
Mountain Low AOT
Valley High AOT
Cloud Contamination?
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Abstract
The main scientific challenge in the study of
particulate matter (natural or man made) is to
understand the immense structural and dynamic
complexity of the 6-dimensional aerosol system
(X, Y, Z, T, Diameter, Composition). Each
sensor/network covers only a limited fraction of
the 8-D data space some measure only a small
subset of the PM pollution data and need
extrapolating. Others provide broad integral
measures of the aerosol system Satellites, for
example, integrate over atmospheric height,
particle size, composition, shape, and mixture
dimensions. The interpretation of these integral
data requires considerable de-convolution of the
integral measures. Given its many dimensional
properties, the aerosol system is largely
self-describing. The analyst's challenge is to
derive the pattern of dust, smoke, haze by
filtering, aggregating and fusing the
multidimensional data. The paper shows recent
results of aerosol characterization using seven
years of SeaWiFS-derived data over the US, along
with companion surface observations along with
surface PM chemical and physical data.