Aerosol Characterization Using the SeaWiFS Sensor and Surface Data

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

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

Satellite-Integral

• Satellites, integrate over height H, size D, composition C, shape, and mixture

dimensions; these data need de-convolution of the integral measures.

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

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

R = (R0 + (e-– 1) P) e-

• The surface reflectance R0 objects viewed from space is modified by aerosol scattering and absorption.

• The apparent reflectance, R, is: R = (R0 + Ra) Ta

Aerosol as Reflector:

Ra = (e-– 1) P

Aerosol as Filter: Ta = e-

Apparent Reflectance

R may be smaller or larger then R0, depending on aerosol

reflectance and filtering.

Aerosols Increase of Decrease the Surface Reflectance, f(P/R0)

Aerosols will increase the apparent surface reflectance, R, if P/R0 < 1. For this reason, the reflectance of ocean and dark vegetation increases with τ.

When P/R0 > 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 τ (radiation equilibrium), both dark and bright surfaces asymptotically approach the ‘aerosol reflectance’, P

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)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.5 1 1.5 2 2.5 3 3.5 4

Aerosol Optical Thickness, AOT

Ap

par

ent R

efle

ctan

ce,R

--+= ePeRR ))1(( 0

Clouds at all wavelengths, P/Ro<o.5

Ocean at >0.6 umVegetation at 0.4 um, P/Ro>10

Soil at >0.6 umVegetation at >0.6 um, P/Ro=1

Vegetation at 0.5 um, P/Ro=2-5

Best fit P values:Haze: P = 0.38Dust: P = 0.28

Haze Effect on Spectral Reflectance over Land

The spectral reflectance of vegetation in the visible λ is low at 0.01<R0<0.1. 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.

Smoke & Dust Filter

0

0.5

1

1.5

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Wavelength, um

Re

lati

ve

Ba

ck

sc

att

er

Haze Filter

0

0.5

1

1.5

0.3 0.5 0.7 0.9Wavelength, um

Rel

ativ

e B

acks

catt

er

Co-Retrieval: Seasonal Surface Reflectance, Eastern US

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

Kansas Agricultural Smoke, April 12, 2003

Fire Pixels PM25 Mass, FRM65 ug/m3 max

Organics35 ug/m3 max

Ag Fires

SeaWiFS, Refl SeaWiFS, AOT Col AOT Blue

April 12, 2003

April 10, 2003

Kansas Smoke

Kansas Smoke Emission Estimation

April 11: 87 T/day

April 10: 1240 T/d

Assuming Mass Extinction Efficiency:

5 m2/g

April 11, 2003

Emission Estimate

Fire PixelsSurface

Observations

Monte CarloDiagnostic Local Model

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

Background Concentration

- Identified individual halos using a small pixel margin around each plume edge

- Background changes from area to area

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

Yesterday’s Smoke and Today’s Smoke

Yesterday’s smoke is not in a plume “worm” shape and it has moved a distance Today’s

smoke is distinct plumes

Quantifying yesterday’s 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

Satellite-Surface-Model Data IntegrationSmoke Emission Estimation

Emission Model

Land Vegetation

Fire Model

e..g. MM5 winds, plume model

Local Smoke Simulation Model

AOT Aer. Retrieval

Satellite Smoke

Visibility, AIRNOW

Surface Smoke

Assimilated Smoke Pattern

Continuous Smoke Emissions

Assimilated Smoke Emission for Available Data

Fire Pixel, Field Obs

Fire Location

Assimilated Fire Location

Satellite Aerosol Optical Thickness ClimatologySeaWiFS Satellite, Summer 2000 - 2003

20 Percentile

98 Percentile90 Percentile

60 Percentile

Smoke Sources 98 Percentile

Summer AOT 60 Percentile2000-2004 SeaWiFS AOT, 1 km Resolution

Mountain – Low AOT

Valley – High AOT

AtlantaBirmingham

Cloud Contamination?

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.