WP 3: Absorbing Aerosol Index (AAI) WP 10: Level-1 validation L.G. Tilstra 1, I. Aben 2, and P. Stammes 1 1 Royal Netherlands Meteorological Institute (KNMI) 2 Netherlands Institute for Space Research (SRON) SCIAvisie Meeting, KNMI, De Bilt,

(2) SCIAMACHY instrument degradation relevant to WP 3 and WP 10

SCIAvisie Meeting, KNMI, De Bilt, (3) 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, (4) 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, (5) Black curves: Fourier series without the polynomial base: 3. Simulations: c: FRESCO+ cloud fraction A c : FRESCO+ cloud albedo A s : 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:   

SCIAvisie Meeting, KNMI, De Bilt, (6) 4. Instrument degradation: correction factors d(340) and d(380) 340 nm 380 nm

SCIAvisie Meeting, KNMI, De Bilt, (7) 5. 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, (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

SCIAvisie Meeting, KNMI, De Bilt, (9) Extra slides (R)

SCIAvisie Meeting, KNMI, De Bilt, (10) R1: The “Global Dust Belt”

SCIAvisie Meeting, KNMI, De Bilt, (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: A 340 = A 380 ) 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) – The AAI represents the scene colour in the UV –

SCIAvisie Meeting, KNMI, De Bilt, (12) 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, (13) R4: measurements versus simulations

SCIAvisie Meeting, KNMI, De Bilt, (14) WP 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