1. Satellite Observations of Clouds and the Earth Radiation Budget over Snow: The Importance of Surface Roughness Stephen R. Hudson Other collaborators: Richard E. Brandt Thomas C. Grenfell, Delphine Six (LGGE), and Seiji Kato (NASA-Langley) Advisor: Stephen G. Warren

8. Party for Steve Hudson 7 pm today 6847 36 th Ave NE

9. Satellite Observations of Clouds and the Earth Radiation Budget over Snow: The Importance of Surface Roughness Stephen R. Hudson Other collaborators: Richard E. Brandt Thomas C. Grenfell, Delphine Six (LGGE), and Seiji Kato (NASA-Langley) Advisor: Stephen G. Warren

10. Outline Introduction –What is directional reflectance? Why is it important? –Background about the East Antarctic Plateau and the measurements and models I have used How does snow-surface roughness affect the directional reflectance? What impact does this roughness effect have on cloud observations over snow? By accounting for the roughness effect, can we evaluate CERES observations and algorithms?

11. What is directional reflectance? When the sun shines on a surface, the reflected radiance varies with direction. Photo by Joseph Shaw, NOAA

12. What is directional reflectance? When the sun shines on a surface, the reflected radiance varies with direction. This variation is less evident over snow than over many other surfaces, but it is still important.

13. Measuring directional reflectance Anisotropic reflectance factor (R) –Average value is 1 –An isotropic surface has R = 1 at all angles Bidirectional reflectance factor (BRF) –Average value is equal to the albedo –An isotropic surface has BRF =  at all angles

14. Okay, so what? Understanding the directional reflectance is important for interpreting satellite measurements. Satellites measure radiance coming from one angle; users must account for the anisotropy of the radiance field to determine flux or to estimate other properties.

15. Okay, so what? Understanding the directional reflectance is important for interpreting satellite measurements. Satellites measure radiance coming from one angle; users must account for the anisotropy of the radiance field to determine flux or to estimate other properties. Looking straight down Looking near horizon, away from the sun Looking near horizon, towards the sun

16. Background — Observations We made spectral directional-reflectance observations of the snow at Dome C – 75°S, 123°E, 3250 m – 350—2400 nm –  o 52—87° Representative of much of the East Antarctic Plateau

17. Background — Observations The observations were made with a 15° conical field of view from 32 m above the surface to capture the effects of the natural snow-surface roughness

18. Background — Model The model results I will show come from the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model Atmospheric Profile T, P, H 2 O, O 3 Clouds Aerosols LOWTRAN 7 Cloud Model Aerosol Model Surface Model DISORT Radiance and Flux at Sfc and TOA

19. How does surface roughness affect R? Looking towards the sun you see shaded faces Looking away from the sun you see faces tilted towards the sun

20. Roughness effect at Dome C Used SBDART to model the surface reflectance with a variety of phase functions (Mie, HG, Yang and Xie) Placed the snow under a clear, summertime-average, Dome-C atmosphere

21. Roughness effect at Dome C Rough aggregate grains produce the best match between the model and observations, but the model produces significant error consistent with macro- scale roughness effects for all of the phase functions

22. Roughness effect at Dome C The error increases with solar zenith angle The roughness has little effect on near-nadir intensity

23. How do clouds affect R over snow? From Welch and Wielicki 1989 Early nadir-viewing satellite observations suggested clouds may reduce the reflectance over snow. This was unexpected since the smaller particles in clouds should raise the albedo.

24. How do clouds affect R over snow? From Wilson and Di Girolamo 2004 Multiangle Imaging SpectroRadiometer Later observations showed clouds do raise the TOA albedo over snow, but also enhance the anisotropy over snow. This was unexpected since the smaller particles in clouds should be more isotropic scatterers than snow grains. Nadir View Forward View Cloud Clear

25. Effect of clouds on R over snow We believe much of this effect is caused by clouds hiding the surface roughness, not by differences in the single-scattering properties of snow and cloud particles

26. Effect of clouds on R over snow The key is that the height variations at the cloud top are very small compared to those on the snow surface, in units of optical depth.

27. Effect of clouds at Dome C Nights with shallow fog allowed us to observe the reflectance of a cloud over the snow surface

28. Observation of fog at Dome C The difference caused by fog at Dome C is similar to the error in the plane-parallel modeling results

29. Roughness effect at Dome C The error increases with solar zenith angle The roughness has little effect on near-nadir intensity

30. Modeling fog at Dome C Using SBDART to model the upwelling intensity above a thin cloud over a surface with the observed BRDF gives results very similar to the foggy observation

31. Observed effect requires rough surface When the same cloud is placed over a modeled (flat) snow surface it does not produce the correct effect

32. Comparison with MISR Modeled TOA 866-nm radiances above our parameterized surface agree reasonably well with MISR observations of clear and cloudy scenes

33. Comparison with MISR Modeled TOA 866-nm radiances above our parameterized surface agree reasonably well with MISR observations of clear and cloudy scenes

34. Summary—So Far

36. A little about CERES Clouds and the Earth’s Radiant Energy System; follow-on to ERBE Instruments measure broadband-solar, longwave-window, and total radiances at TOA; algorithms estimate other quantities. Meant to improve on ERBE accuracy, providing better than 1% SW calibration Two instruments fly on each of two satellites that see Dome C about twice each day, giving many observations of the area.

37. Can we assess CERES SW calibration? Use the parameterized surface in spectral runs with SBDART to compare modeled and CERES solar TOA radiances CERES data from 4 clear days in January 2004 and 2005; about 20,000 observations Use CERES radiance data that include all reflected solar energy at all wavelengths, and no emitted energy

38. Can we assess CERES SW calibration?

39. Which is right? We would like to know if the model is overestimating the radiance or if CERES is underestimating it. Comparisons of the modeled radiances with MISR observations suggest the model is accurately calculating the radiance, or is slightly underestimating it. Some work by people on the CERES team also suggests the difference could be due to a bias in CERES data (Charlock; Kato).

40. Can we assess CERES ADMs? To convert the radiance measurements to flux estimates, the CERES team uses Angular Distribution Models. These ADMs provide the average R pattern at the TOA for each scene type and solar zenith angle. The R patterns can be compared with model results to evaluate the algorithms separately from the calibration issue.

41. Can we assess CERES ADMs?

42. Conclusions Studies involving the directional reflectance over snow must consider surface roughness. –Observations must be made with a footprint that is large enough to accurately capture the effect of the roughness. –Models of radiative transfer over snow-covered regions should not treat the snow as a plane- parallel surface.

43. Conclusions The enhanced anisotropy caused by clouds in the reflected radiance field above polar snow surfaces can be explained by accounting for the surface roughness in the clear-sky model. The clouds hide the rough surface with a surface that is very smooth in units of optical depth.

44. Conclusions The parameterization developed from our surface reflectance observations can be used to assess satellite observations and products. Doing this for CERES suggests a negative bias in the instruments’ shortwave channels, but indicates that the method used to convert radiance observations to fluxes works well.

45. Future Work Work with Seiji Kato to further validate the CERES algorithms for converting radiance to flux. Examine the importance of atmospheric aerosols or other constituents on R at the TOA. Look at CERES ADMs for clouds over permanent snow.

46. Acknowledgements Steve Warren

47. Acknowledgements Steve Warren Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick

48. Acknowledgements Steve Warren Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick Warren Associates – Von, Rich, Tom, Mike, Ryan, Mel, Penny

49. Acknowledgements Steve Warren Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick Warren Associates – Von, Rich, Tom, Mike, Ryan, Mel, Penny Mike Wallace

50. Acknowledgements Steve Warren Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick Warren Associates – Von, Rich, Tom, Mike, Ryan, Mel, Penny Mike Wallace Seattle friends

51. Acknowledgements Steve Warren Committee – Tom Grenfell, Tom Ackerman, Qiang Fu, and Norman McCormick Warren Associates – Von, Rich, Tom, Mike, Ryan, Mel, Penny Mike Wallace Seattle friends Family

52. Can we assess CERES SW calibration?

53. Which is right? Comparisons of the modeled radiances with MISR observation suggest the model gives accurate or low estimates of radiance.

54. Ice clouds have similar qualitative effect

55. Roughness effect on broadband albedo