1. Developement of exact radiative transfer methods Andreas Macke, Lüder von Bremen, Mario Schewski Institut für Meereskunde, Uni Kiel

2. Introduction ❑ Monte Carlo method exactly simulates radiative transfer prosesses in arbitrary complex scattering and absorbing media (clouds) ❑ Numerical application feasible for ❑ local and domain average ❑ fluxes and radiance fields at specified layers ❑ Full 3D radiance fields  SHDOM (not exact)

3. Complete MC package (GRIMALDI) ❑ forward scheme to solve radiative transfer in 3d scattering and absorbing atmosphere with directed (solar) illumination ❑ preprocessor for absorption properties of atmospheric gases ❑ monochromatic and spectral band fluxes and radiances for finite sized angular bins ❑ photon path length pdf for finite sized angular bins ❑ data base for scattering phase functions and single scattering albedos for spherical and non-spherical cloud particles

4. Forward Local Estimate MC (MC-UNIK) ❑ forward scheme to solve radiative transfer in 3d scattering and absorbing atmosphere with directed (solar) illumination ❑ monochromatic fluxes and radiances for discrete directions (Local Estimate scheme) ❑ data base for scattering phase functions and single scattering albedos for spherical and non-spherical cloud particles

5. Backward Local Estimate MC (MC-UNIK-BW) ❑ backward scheme to solve radiative transfer in 3d scattering and absorbing atmosphere with directed (solar) illumination ❑ monochromatic radiances for discrete directions (Local Estimate scheme) ❑ photon pathlength pdf for predefined viewing geometries and viewing locations ❑ data base for scattering phase functions and single scattering albedos for spherical and non-spherical cloud particles

6. Backward MC (3RAD-UNIK) ❑ backward scheme to solve radiative transfer in 3d scattering, absorbing and emitting atmosphere ❑ monochromatic radiances for discrete directions ❑ preprocessor for gas absorption from the thermal to the microwave ❑ data base for scattering phase functions and single scattering albedos for spherical cloud particles

7. Internet http://www.ifm.uni-kiel.de/fb/fb1/me/research/Projekte/topic2-e.html  Programs & Tools

9. Remote Sensing of inhomogeneous clouds - vertical structure, photon path length - Steffen Meyer, Mario Schewski, Andreas Macke Institut für Meereskunde, Uni Kiel

10. Problem - cloud variability breaks unique cloud-radiance relation -

11. MC-Local Estimate - discrete directions, spatial weighting function - ❑ Initialisation: ❑ scattering phase function ❑ single scattering albedo  0 ❑ vol. extinction coefficient  x ❑ detector position (  ) ❑ output: ❑ reflected radiances L ❑ weighting function of L (x,y,z)

12. artificial vertical cloud profiles ❑ local variations of cloud extinction coefficient with preseved optical thickness

13. settings ❑  = 10, 20, 40 ❑ = 0.625; 0.850; 1.000; 1.600; 3.750  m ❑ r eff = 16  m; variable ❑ surface albedo  = 0.0 ❑ geometries: ❑ sun:   = 60 0 ;  0 = 0 0 ❑ satellite:  =0 0 ;  =0 0 (nadir)

14. constant profile (  =10; r eff =16  m)

15. linear profile (  =10; r eff =16  m)

16. constant profile (  =10; r eff =var)

17. constant profile (  =40; r eff =16  m)

18. linear profile (  =40; r eff =16  m)

19. dependency of penetration depth on absorption single scattering albedo  0 wavelength [  m] single scattering albdo

20. spectral variation of scattering phase function and single scattering albedo cloud profile spectral weighting function

21. spectral variation of extinction coefficient percent of incoming radiation absorbed 5.0 1.0 0.5 [  m] 0.9-1.0  m 1.4-1.5  m 1.95-2.2  m 2.8-3.0  m

22. settings ❑ = [0.9, 1.0, 0.025]  m (b1) [1.4, 1.5, 0.025]  m (b2) [1.95, 2.2, 0.025]  m (b3) [2.8, 3.0, 0.025]  m (b4) ❑  = 10.8 ❑ r eff = proportional to extinction coefficient ❑ surface albedo  = 0.0 ❑ geometries: ❑ sun:   = 60 0 ;   = 0 0 ❑ satellite:  =0 0 ;  =0 0 (nadir)

23. Weighting function for gaussian profile of ext. coeff. and particle size (  =10.8; Q ext = 2.0)

24. extinction efficiency - size and wavelength dependency -

25. Weighting function for gaussian profile of ext. coeff. and particle size (  =10.8; Q ext = f(, r eff ))

26. Simulation of photon pathlength distributions - backward Monte Carlo for solar radiative transfer - ❑ Initialisation: ❑ scattering phase function ❑ single scattering albedo  0 ❑ vol. extinction coefficient  x ❑ detector position (  ) ❑ detector location (x,y) ❑ output: ❑ transmitted radiances L ❑ photon pathlength distributions

27. example cloud

28. photon pathlength pdf

29. Summary and outlook ❑ MC methods developed, validated and made public available http://www.ifm.uni-kiel.de/fb/fb1/me/research/Projekte/topic2-e.html ❑ profiling of vertical cloud structure at solar wavelengths not feasible ❑ backward MC at solar wavelengths developed and validated ❑ photon pathlength pdf in progress ❑ simulation of radiance power spectra as a function of vertical and horizontal cloud variability ❑ more detailed consideration of cloud microphysical properties

30. The Future ❑ Proposal for a new research activity in support of 4DCLOUDS (year 4 & 5) ❑ cloud resolving models (dynamical, statistical) with explicit cloud microphysics as data base for cloud-radiation correlation

32. Correlation between domain averaged fluxes and cloud fields (parameterisation?)

33. Radiative transfer calculations ❑ Radiative fluxes calculated with MC-forward modell GRIMALDI ❑ Scattering and absorption at atmospheric gases and cloud particles ❑ 756 GESIMA clouds ❑ 52 x 52 x 26 grid boxes ❑ 2 km horizontal resolution ❑ 100 m – 1 km vertical resolution  10 km domain height ❑ 9 solar zenith angles (0°, 10°... 80°) ❑ 13 spectral bands

34. Spectral intervals

35. Scattering phase functions

36. Parameterization ❑ Multiple regression ❑ Nonlinear dependencies between cloud-parameters and radiative fluxes

37. Best fitting cloud parameters Cloud parameters used to obtain the individual radiative properties at solar zenith angle  0 = 50°:

38. Dependency on solar zenith angle

44. The End!

45. vertikale Profile GESIMA Wolke 'Issig' Wolke idealisierte Wolke

46. homogene Wolke (  =67.21)

47. Gauss-Wolke (  =1.0;  =100.0 ) r eff =1.0

48. Gauss-Wolke (  =1.0; 100.0 ) r eff =10.0

49. GESIMA (  =19.25)  = 0.0  = 0.06

50. 'Issig' Wolke

51. Gauss-Wolken (  =1.0; 100.0)(detector=bottom)

52. GESIMA (  =19.25) (detector=bottom)

53. Zusammenfassung I ❑ reflektierte Strahldichte am Satelliten stammt aus oberen Wolkenschichten ❑ Berücksichtigung versch. Wellenlängen führt zu keinem Gewinn an Information ❑ auch mit bodengebundener Fernerkundung keine Information über Profil

54. effektiver Radius Extinktionskoeffizient optische Dicke

55. Gauss-Issig (  =272)  = 0.06  = 0.0  = 0.8

56. Gauss-Wolken (detector=bottom)

57. reste  = Grass ?

59. 'Issig' Wolke (detector=bottom)

60. Doppelschichtwolke