Co-retrieval of Aerosol and Surface Reflectance

SeaWiFS Satellite

Sean Raffuse, Erin Robinson

Chlorophyll Absorption

Designed for Vegetation Detection

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.

Co-Retrieval: Seasonal Surface Reflectance, Eastern US

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

Results – Eight month animation

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

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 - 2004

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?

Temporal Signal Decomposition and

Event Detection

1. Daily median & average over a region

2. Temporal smoothed by a 30 day

EUS Daily Average 50%-ile, 30 day 50%-ile smoothing

Deviation from %-ile

Event : Deviation > x*percentile

Median Seasonal Conc.

Mean Seasonal Conc.

Average

Median

3. Event is the deviation of daily value from the smooth median (event – red; noise blue)

Causes of Temporal Variation by Region

The temporal signal variation is decomposable into seasonal, meteorological noise and events. Statistically:

V2Total = V2

Season + V2MetNoise + V2

Event

Northeast exhibits the largest coeff. variation (56%); seasonal, noise and events each at 30%Southeast is the least variable region (35%), with virtually no contribution from eventsSouthwest, Northwest, S. Cal. and Great Lakes/Plains show 40-50% coeff. variation mostly, due to seasonal and meteorological noise.Interestingly, the noise is about 30% in all regions, while the events vary much more, 5-30%

Origin of Fine Dust Events over the US

Gobi dust in springSahara in summer

Fine dust events over the US are mainly from intercontinental transport

Fine Dust Events, 1992-2003ug/m3