GERB Unfiltering: Validation of the Data Base of Spectral Radiance Curves (and Improvements)

1. GERB Unfiltering: Validation of the Data Base of Spectral Radiance Curves (and Improvements) Nicolas Clerbaux 22nd GIST Meeting – 9 November 2004

2. Operational unfiltering • Using channels (0.6,0.8 and 1.6µm for SW) • Parametrization using a data base of spectral “radiance” curves (in fact spectral flux curves for the SW simulations). SBDART version 2.1 was used. Direct unfiltering • Without SEVIRI information (-> interest) • Using the BB radiance and optionally the surface type • Similar to the CERES unfiltering • Parametrization using the same data base of spectral radiance curves as the operational unfiltering

3. Spectral Radiance Curves Data Base {L(l)}-- Version 1 • Built using SBDART 2.1 • Wavelength range: SW = [0.25:4]µm and LW=[4:100]µm • Lambertian surfaces • Spectral mixture of internal surface spectral reflectance curves (“sand”, “vege”, “water”, “snow”). • Atmospheric profiles from TIGR data base • Shortwave computations in flux mode (2 streams), longwave in radiance mode.

4. Jacqui’s Idea • Apply “GERB” direct unfiltering to CERES data and compare with the CERES unfiltering. This allows to check that our data base is consistent with the Norman Loeb’s data base (MODTRAN). • Some results presented during last data meeting but affected by a bug in the SW thermal contamination. New (and better) results presented here.

5. Method • LW (night-time): • SW (day-time):

6. Longwave Results • There was a systematic error. • Investigations have showed that this is due to a different definition of the unfiltering factor. For GERB the unfiltering factor was defined over [2.5:100µm] and this gives slightly different result than over [2.5:500] -> we have extended the wavelength range in the LW up to 500µm. GERB spectral response is supposed to be constant there. Now it is OK (see graph). Notes about CERES LW unfiltering: • UNFIL is defined over [0:500] µm while FIL over [0:200] µm. • Spectral information from the WN channel is used

7. 1% cold scenes 1% Bias < 0.1% hot scenes

8. Shortwave Results • Important dispersion and bias • The RMIB direct unfiltering: overestimation over the desert and underestimation in overcast condition. • Correction of the spectral “flux” computations using the ERBE anisotropy models:

9. New SBDART Simulations & New Direct Unfiltering Fits • Built using SBDART 2.4. • Lambertian surfaces except ocean (6S BRDF model). • Using spectral mixture of JPL surface reflectance curves. • Computations in radiance mode (20 streams). • First set 600 conditions: 50% cloudy/50% clear, 50%ocean/50%land. • Direct unfiltering fits for ocean, land and desert surface type. Note: database available on request.

10. CLEAR OCEAN (1) Bias rad: -0.13% Bias flux: -0.63%

11. CLEAR OCEAN SUN-GLINT CONDITIONS Bias rad: -0.10% Bias flux: -0.17%

12. CLEAR LAND (2) Bias rad: 0.14% Bias flux: 0.10%

13. CLEAR DESERT (4) Bias rad: 0.03% Bias flux: 0.17%

14. PARTLY CLOUDY OCEAN (6) Bias rad: 0.24% Bias flux: 0.08%

15. PARTLY CLOUDY OVER LAND OR DESERT (7) Bias rad: -0.16% Bias flux: -0.15%

16. MOSTLY CLOUDY OVER OCEAN (9) Bias rad: -0.03% Bias flux: -0.09%

17. MOSTLY CLOUDY OVER LAND OR DESERT (10) Bias rad: -0.17% Bias flux: -0.20%

18. OVERCAST (12) Bias rad: -0.11% Bias flux: -0.20%

19. Conclusions • Good agreement between the data bases • No important problem introduced by working in “spectral flux”/p for the SW, except for ocean surface (does SEVIRI info help here?) • Proposal for the data release: parameterize the unfiltering regressions using the spectral radiance curves (Version 2) instead of spectral flux curves (Version 1). But regressions not dependent on (q,j).