WP 3 : Absorbing Aerosol Index (AAI)

WP 10 : Level-1 validation

L.G. Tilstra1, I. Aben2, and P. Stammes1

1Royal Netherlands Meteorological Institute (KNMI)2Netherlands Institute for Space Research (SRON)

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (2)

SCIAMACHY instrument degradationSCIAMACHY instrument degradation

relevant to WP 3 and WP 10relevant to WP 3 and WP 10

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (3)

1. AAI (=residue) as an indicator of instrument degradation:1. AAI (=residue) as an indicator of instrument degradation:

The global mean residue should be

more or less constant

m-factors not applied

m-factors applied: some improvement,

introduction of “features”

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (4)

2. In-flight reflectance calibration method:2. In-flight reflectance calibration method:

Global mean reflectance: isolate/remove the

natural seasonal variation from the time series

grey: daily global mean reflectance

coloured: 12-day average

black curves: Fourier series on polynomial base

dotted curves: polynomial base

(dotted curves)

(black curves)

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (5)

Black curves: Fourier series without the

polynomial base:

3. Simulations:3. Simulations:

c : FRESCO+ cloud fraction

Ac : FRESCO+ cloud albedo

As : OMI-based surface albedo (LER value)

Question: Are the seasonal variations really

related to natural variations?

Simulations based on RT calculations for

cloud-free scenes including ozone absorption

and Lambertian surface reflection.

Including clouds in the simulations:

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4. Instrument degradation: correction factors 4. Instrument degradation: correction factors d(340)d(340) and and d(380)d(380)

340 nm 380 nm

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (7)

5. Verification: analyse the global mean residue/AAI5. Verification: analyse the global mean residue/AAI

Correction for instrument degradation works well. Data affected by decontamination

events can be corrected. Seasonal variation similar to that found for GOME-1/2.

Grey bars: data were affected

by decontamination events

Secondary correction in “grey

areas” possible

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (8)

Keep improving the scientific AAI product (SC-AAI)

Include spectral dependence of surface albedo into algorithm

Continue the improvement and validation of the L2-AAI

Maintain SC-AAI data archive at the TEMIS website

Further study the scan-angle dependent degradation for support of WP 10

Plans for WP 3:

Plans for WP 10:

Verify improvement in calibration by the new “SRON” m-factors using the

various tools we developed

Monitor instrument degradation and analyse the quality of the applied

degradation correction (“m-factors”) in the UV using the Absorbing Aerosol

Index (AAI) and using in-flight monitoring of level-1 data

Monitor and validate polarisation product using special geometries

Analyse the reflectance over specific stable Earth targets

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Extra slides (R)

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (10)

R1: The “Global Dust Belt”

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (11)

R2: Introduction of the Absorbing Aerosol Index (AAI) and the residue

A. Definition of the residue:

where the surface albedo A for the simulations is such that:

(A is assumed to be wavelength independent: A340 =

A380)

no clouds, no aerosols : r = 0

clouds, no absorbing aerosols : r < 0

absorbing aerosols : r > 0

B. Definition of the AAI:

AAI = residue > 0 (and the AAI is not defined where residue < 0)

Rayleigh

380

34010

meas

380

34010 loglog100R

R

R

Rr

)(Rayleigh380

meas380 ARR

– The AAI represents the scene colour in the UV –

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R3: Typical global aerosol distribution:

The “Global Dust Belt”:

Desert Dust Aerosols (DDA)

(dust storms, all year)

AAI from other UV satellite instruments: TOMS, GOME-1, GOME-2. Combined with

SCIAMACHY there are more than three decades (1978–2010) of AAI data available for

studies of trends in desert dust and biomass burning aerosol.

Biomass Burning Aerosols (BBA)

(dry season, anthropogenic)

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (13)

R4: measurements versus simulationsR4: measurements versus simulations

SCIAvisie Meeting, KNMI, De Bilt, 07-12-2010 (14)

WP 10: level-1 validationWP 10: level-1 validation

– comparison with radiative transfer model “DAK” (in the UV)

– comparison with other satellite instruments (GOME-1, MERIS, POLDER-2, …)

– qualitative analysis of the spectra (spectral properties)

Validation techniques for the reflectance:

calibration offset spectral features