1. UPDATE ON THE SUN GLINT Joe Tenerelli Ocean Data Lab SMOS Level 2 OS Progress Meeting 26 SMOS Barcelona Expert Centre Barcelona, Spain 21-23 April 2015

2. PRIORITIES (HIGHEST TO LOWEST) 1.Land contamination 2.Sun glint 3.Galactic glint 4.Short term (latitudinal) drift

3. SUN GLINT IN EAF FOV IN DESCENDING PASSES IN NOVEMBER-JANUARY

4. Huge first stokes bias on western side of EAF FOV in descending passes, peaking around December: THE SUN GLINT IMPACT IN THE EAF FOV IN DESCENDING PASSES

5. Huge first stokes bias on western side of EAF FOV in descending passes, peaking around December: THE SUN GLINT IMPACT IN THE EAF FOV IN DESCENDING PASSES

6. Bias can exceed 20 K in first Stokes divided by two, but is mainly in the EAF-FoV! THE SUN GLINT IMPACT IN THE EAF FOV IN DESCENDING PASSES

7. GO model (with total MSS=0.01, Sun Tb=100000 K) shows this signal too, but need to tune the model to fit the data: THE SUN GLINT IMPACT IN THE EAF FOV IN DESCENDING PASSES

8. WHERE DOES SUN GLINT IMPACT THE DATA? The sun specular point (where the sun glint is maximum) is closest to boresight in descending passes late in the year, from November through January:

9. WHERE DOES SUN GLINT IMPACT THE DATA? The sun specular point (where the sun glint is maximum) is closest to boresight in descending passes late in the year, from November through January:

10. EXTRACTING SUN GLINT FROM THE DATA To determine the impact of sun glint on the data, we need to find a way to extract the contribution of the sun glint to the total scene brightness temperature. Below is an example the the reconstructed scene brightness (Tx+Ty)/2 obtained from the MIRAS visibilities:

11. EXTRACTING SUN GLINT FROM THE DATA To extract the sun glint over pure open ocean scenes, we evaluate the forward ocean model over the entire FHS. Below is an example of this model over just the fundamental hexagon:

12. EXTRACTING SUN GLINT FROM THE DATA The visibilities corresponding to this model are calculated using the G-matrix, and then the model scene is recomputed by applying the J+ matrix and then computing the inverse Fourier transform, producing for this example:

13. EXTRACTING SUN GLINT FROM THE DATA Subtracting the reconstructed model from the total data yields the sun glint + scene bias + random noise:

14. EXTRACTING SUN GLINT FROM THE DATA To remove the scene bias we compute an OTT using an ascending pass that does not contain sun glint:

15. EXTRACTING SUN GLINT FROM THE DATA Subtracting this OTT from the image below

16. EXTRACTING SUN GLINT FROM THE DATA Yields the sun glint after correction for the OTT. This is the estimated sun glint.

17. EXTRACTING SUN GLINT FROM THE DATA We then replicate the solution by tiling the solutions over the fundamental hexagon to yield, after removal of discs around the direct sun aliases:

18. EXTRACTING SUN GLINT FROM THE DATA Finally, we remove the part of the solution that is contaminated by aliases to yield the final sun glint image:

19. MODELING THE SUN GLINT Some examples of model solutions for the sun glint: Kirchhoff model with Kudryavtsev wave spectrum evaluated at wind speed of 20 m/s

20. MODELING THE SUN GLINT Some examples of model solutions for the sun glint: SSA-1 model with Kudryavtsev wave spectrum evaluated at wind speed of 20 m/s

21. MODELING THE SUN GLINT Some examples of model solutions for the sun glint: Geometric optics model with an isotropic slope PDF with total MSS = 0.03:

22. MODELING THE SUN GLINT Some examples of model solutions for the sun glint: Difference between the geometric optics and KA solutions:

23. GLINT EVOLUTION ALONG AN ORBIT Sun glint can vary rapidly in time in response to variations is roughness and sun brightness temperature: -40 K 160 K

24. GLINT EVOLUTION ALONG AN ORBIT Sun glint can vary rapidly in time in response to variations is roughness and sun brightness temperature: -40 K 160 K

25. GLINT EVOLUTION ALONG AN ORBIT Sun glint can vary rapidly in time in response to variations is roughness and sun brightness temperature: -40 K 160 K

26. GLINT EVOLUTION ALONG AN ORBIT Sun glint can vary rapidly in time in response to variations is roughness and sun brightness temperature: -40 K 160 K

27. GLINT EVOLUTION ALONG AN ORBIT Sun glint can vary rapidly in time in response to variations is roughness and sun brightness temperature: -40 K 160 K

28. GLINT EVOLUTION ALONG AN ORBIT Sun glint can vary rapidly in time in response to variations is roughness and sun brightness temperature: -40 K 160 K

29. NEED SOLAR FLUX DATA TO MODEL SUN GLINT Sun brightness temperature varies greatly over the mission!

30. NEED SOLAR FLUX DATA TO MODEL SUN GLINT Sun brightness temperature varies greatly over the mission!

31. NEED SOLAR FLUX DATA TO MODEL SUN GLINT Sun brightness temperature varies greatly over the mission!

32. NEED SOLAR FLUX DATA TO MODEL SUN GLINT Main possible sources: Observatories at San Vito, Sagamore Hills (USA East Coast), Palehua (Hawaii), Learmonth (Australia), Penticton 10 cm (Western Canada) But for desc passes in East Pacific only data from Learmonth useful (daytime). Can compare flux data from different sources in overlapping time periods.

33. GLINT EVOLUTION ALONG AN ORBIT In this example the glint variations are apparently not associated with variations in sun brightness temperature:

34. COMPARING MODEL AND DATA To evaluate the various model solutions we divide the domain into strips bounded by curves of constant specular slope. The overlapping bands have width of 0.05 in slope and are spaced by 0.01 is slope.

35. COMPARING MODEL AND DATA Within each band we compute the average glint from both the data and model solutions as well as the average 10-m NE wind speed from ECMWF:

36. COMPARING MODEL AND DATA Within each band we compute the average glint from both the data and model solutions as well as the average 10-m NE wind speed from ECMWF:

37. WAY FORWARD Calculate a set of SSA-1 and KA solutions over a range of wind speeds on hexagonal grid and apply Blackman apodization. Compare solutions to the data as a function of some sort of geometry variable such as specular surface slope. Fit models to the data in both quasi-specular and EAF-FoV zones. May need to patch different models (GO in quasi-specular lobe, SSA-1 or KA in EAF- FoV?)

38. COMPARING MODEL AND DATA

40. Evolution of specular sun incidence angle for descending passes:

41. COMPARING MODEL AND DATA