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Determination of optical and microphysical Properties of Water Clouds

Determination of optical and microphysical Properties of Water Clouds. Retrieved Parameters. Cloud optical thickness Cloud effective droplet radius Cloud top height Liquid water path Thermodynamic phase. Retrieved Parameters – Mathematical formulation.

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Determination of optical and microphysical Properties of Water Clouds

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  1. Determination ofoptical and microphysical Properties of Water Clouds

  2. Retrieved Parameters • Cloud optical thickness • Cloud effective droplet radius • Cloud top height • Liquid water path • Thermodynamic phase

  3. Retrieved Parameters – Mathematical formulation • Effective cloud droplet radius Optical thickness

  4. Basic concept of optical retrievals • reflectance / emission of a cloud • microphysical cloud parameters

  5. Reflection Function • = ratio of reflected light intensity of a cloud to that of an ideal Lambertian white reflector • for Lambertian ideally white reflector • Clouds are not a Lambertian reflector •  geometric dependence of R •  transmission of incident radiation

  6. Reflection Function – Geometric Dependence • Exact radiative transfer code (Mishchenko et al. 1999) using Gamma size distribution: 1

  7. Reflection Function – Transmission • VIS: Reflection reduces due to transmission • = reflection function of a semi-infinite, non-abs. cloud • = global transmittance of a cloud • = asymmetry parameter • = escape functions

  8. Dependence of RVIS on m0,aef, t • Reflection function of clouds in VIS • depends strongly on optical thickness • depends weakly on aef (Kokhanovsky et al. 2003)

  9. Reflection Function – NIR • NIR: Reflection reduces due to transmission and weak absorption • = reflection function of a semi-infinite cloud • = diffusion exponent • = escape functions •  Satellite signal is composed of a) solar component and b) thermal component

  10. Dependence of RNIR on m0,aef, t • Reflection function of clouds in NIR (weakly absorbing) • depends strongly on aef • depends moderately on optical thickness (Kokhanovsky et al. 2003)

  11. Dependence of RNIRaef Large droplets  Volume is dominant parameter  Absorption > Reflection Small droplets  Cross-section is dominant parameter  Reflection > Absorption

  12. Dependence of R on m0,aef, t for VIS and NIR

  13. Dependence of Radiance Density on m0,aef, t • Retrieval of cloud parameters is possible with VIS / NIR bands of satellite sensors

  14. Meteosat-8 Eumetsat geostationary orbit (0°) launch: 28.08.2002 operational since 4/2004 available at least up to 2012 SEVIRI Sensor repetition: 15 minutes 12 bands: 2 VIS (3km) 2 NIR (3km) 7 WV/IR (3km) 1 HRV (1km) Examples of suitable systems – Meteosat-8 SEVIRI

  15. Terra & Aqua NASA (EOS) sun-synchronous orbit Terra launch 1999-12-18 EOS-AM (10:30 south) Aqua launch 2002-05-04 EOS-PM (13:30 north) MODIS Sensor 36 bands (0,62 – 14,39 µm) resolution 1km 2 VIS (250m) 5 VIS/NIR (500m) Examples of suitable systems – Terra-/Aqua-MODIS

  16. Retrieval Concepts • Look-up table approach • = satellite signal is iteratively lined with pre-calculated look-up tables connecting cloud microphysical parameters with measured radiance density in VIS/NIR bands. • GTR (T. Nakajima, T. Y. Nakajima, Kawamoto) • NASA MOD06 (Platnick, King, Ackerman, Menzel, Baum, Riédi, Frey) • Semianalytical approach • = satellite signal is used for the solution of a simplified, single semi-analytical equation which is derived from exact radiative transfer equations. • SACURA (Kokhanovsky)

  17. Example 1 - GTR • Look-up table approach • GTR retrieval • T. Nakajima, T. Y. Nakajima, Kawamoto

  18. GTR – Extraction of Radiance Density from Signal ground thermal component cloud thermal component ground reflection • VIS • NIR

  19. GTR - Preparation of LUTs • Grid system of LUTs • 1.,2.,4.,6.,9.,14.,20.,30.,50.,70.2.,4.,6.,9.,12.,15.,20.,25.,30.,35.,40.0.,5.,10.,20.,30.,35.,40.,45.,50.,55.,60.0.,5.,10.,20.,30.,35.,40.,45.,50.,55.,60.,65.,70.0.,10.,20.,30.,40.,50.,60.,70.,80.,90.,100.,110.,120.,130.,140.,150.,160.,170.,180. • Liquid water content for several classified cloud types • Cu, Sc 0.300 g/m3As, Ac 0.250 g/m3Ci, Cs, Cc 0.014 g/m3Ns 0.300 g/m3Cb 0.393 g/m3St 1.540 g/m3 Pruppacher & Klett 1978, Heymsfield 1993

  20. GTR - Preparation of additional datasets • Cloud-free albedo maps (monthly mean – minimum map) • VIS and NIR (solar radiation only) band • 6S code (Tanré 1990) • Cloud-free background BTT map (actual scene) • Multiple regression function • Latitude • Longitude • Height above sea level (DGM) • Temperature • Vertical profiles (actual scene) • MM5, Sounding data, etc. • Temperature • Humidity • Pressure

  21. GTR – Additional datasets Satellite dataVIS / NIR bandsCloud-free albedo maps (6S)Cloud-free ground BBT map Radiative-Transfer-CalculationRadiance Density / BBT vs.microphysical Parameters Actual Atmosphere ProfilesMM5Sounding data IterationSatellite data - LUTs

  22. GTR – Flow of Analysis (Kawamoto et al. 2001)

  23. GTR – Calculation of w, D and Z • Liquid water path • Geometrical thickness • Cloud-top height from vertical profile data

  24. GTR – Input Satellite Data Radiance density 0.6µm Radiance density 3.9µm[W/m2/µm/sr] [W/m2/µm/sr]

  25. GTR - Results 11µmT[K] t Re[µm] Terra-MODIS, 2002-08-05, 11:05 GMT

  26. Example 2 - SACURA • Semianalytical approach • SACURA retrieval • A. A. Kokhanovsky

  27. SACURA – Retrieval of aef & t for 2 band algorithm 01 • VIS • NIR • can be calculated by simple approximation equations

  28. SACURA – Retrieval of aef & t for 2 band algorithm 02 • from VIS: • from scaled optical thickness: • from other simplifications: •  •  Substitution in R2 retrieves aef with a single transcendent equation •  t is retrieved subsequently with equation above

  29. SACURA - Results 11µmT[K] t Re[µm] Terra-MODIS, 2002-08-05, 11:05 GMT

  30. Error Estimation • Theoretical Errors

  31. Error Estimation - SACURA • Error of R due to simplification of semi-analytical equations (Kokhanovsky et al. 2003)

  32. Error Estimation - GTR • Error of retrieved parameters when applied to simulated satellite signals using t [5;10;15] at aef 10µm and aef [6;10;16µm] at t = 10. (Kawamoto et al. 2001)

  33. Intercomparison • Intercomparison • SACURA vs. GTR. vs MOD06

  34. Intercomparison SACURA vs. GTR vs. MOD06 aef [µm] GTR SACURA MOD06 Terra-MODIS, 2001-07-18, 15:30 GMT t GTR SACURA MOD06

  35. Intercomparison SACURA vs. GTR vs. MOD06 aef [µm] GTR SACURA MOD06 t GTR SACURA MOD06

  36. Intercomparison SACURA vs. GTR vs. MOD06 - aef Terra-MODIS, 2001-07-18, 15:30 GMT

  37. Intercomparison SACURA vs. GTR vs. MOD06 - t Terra-MODIS, 2001-07-18, 15:30 GMT

  38. Intercomparison SACURA vs. GTR vs. MOD06 – Freq. Terra-MODIS, 2001-07-18, 15:30 GMT

  39. Conclusion • Retrieval of aef and t from satellite data is possible • Retrieval is one realization of the reality • LUT and asymptotic theory approaches have errors due to • Inhomogeneous clouds • Errors in additional datasets, partly cloud covered pixels etc. • Errors of asymptotic approach are negligible for optically thick clouds • Asymptotic equations can be simplified with negligible errors for t > 5

  40. Outlook • We will join efforts to implement a new version combining both approaches • t > 10  semi-analytical equations • t < 5  LUT approach • 5 < t < 10  one of both but we will see…. •  Optimized algorithm with regard of • minimization of computer time and • minimization of errors

  41. Acknowledgments • Alexander A. Kokhanovsky

  42. Thank you • The End

  43. Intercomparison SACURA vs. GTR vs. MOD06 aef [µm] GTR SACURA MOD06 Terra-MODIS, 2002-08-10, 09:45 GMT t GTR SACURA MOD06

  44. Intercomparison SACURA vs. GTR vs. MOD06 - aef Terra-MODIS, 2002-08-10, 09:45 GMT

  45. Intercomparison SACURA vs. GTR vs. MOD06 - t Terra-MODIS, 2002-08-10, 09:45 GMT

  46. Intercomparison SACURA vs. GTR vs. MOD06 – Freq. Terra-MODIS, 2002-08-10, 09:45 GMT

  47. Intercomparison SACURA vs. GTR vs. MOD06 – Delta Terra-MODIS, 2002-08-10, 09:45 GMT

  48. SACURA – Lambert surface reflection • VIS • Large optical thickness  direct solar light term can be neglected • NIR • can be calculated by simple approximation equations

  49. Error Estimation - SACURA • Error of retrieved parameters due to measurement errors and t (Kokhanovsky et al. 2003)

  50. Geometry

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