Post on 18-Nov-2014
description
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.