Determination of optical and microphysical properties of water clouds
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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

Determination ofoptical and microphysical Properties of Water Clouds


Retrieved parameters
Retrieved Parameters

  • Cloud optical thickness

  • Cloud effective droplet radius

  • Cloud top height

  • Liquid water path

  • Thermodynamic phase


Retrieved parameters mathematical formulation
Retrieved Parameters – Mathematical formulation

  • Effective cloud droplet radius Optical thickness


Basic concept of optical retrievals
Basic concept of optical retrievals

  • reflectance / emission of a cloud

  • microphysical cloud parameters


Reflection function
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


Reflection function geometric dependence
Reflection Function – Geometric Dependence

  • Exact radiative transfer code (Mishchenko et al. 1999) using Gamma size distribution:

1


Reflection function transmission
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


Dependence of r vis on m 0 a ef t
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)


Reflection function nir
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


Dependence of r nir on m 0 a ef t
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)


Dependence of r nir a ef
Dependence of RNIRaef

Large droplets  Volume is dominant parameter  Absorption > Reflection

Small droplets  Cross-section is dominant parameter  Reflection > Absorption


Dependence of r on m 0 a ef t for vis and nir
Dependence of R on m0,aef, t for VIS and NIR


Dependence of radiance density on m 0 a ef t
Dependence of Radiance Density on m0,aef, t

  • Retrieval of cloud parameters is possible with VIS / NIR bands of satellite sensors


Examples of suitable systems meteosat 8 seviri

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


Examples of suitable systems terra aqua modis

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


Retrieval concepts
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)


Example 1 gtr
Example 1 - GTR

  • Look-up table approach

  • GTR retrieval

  • T. Nakajima, T. Y. Nakajima, Kawamoto


Gtr extraction of radiance density from signal
GTR – Extraction of Radiance Density from Signal

ground thermal component

cloud thermal component

ground reflection

  • VIS

  • NIR


Gtr preparation of luts
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


Gtr preparation of additional datasets
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


Gtr additional datasets
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


Gtr flow of analysis
GTR – Flow of Analysis

(Kawamoto et al. 2001)


Gtr calculation of w d and z
GTR – Calculation of w, D and Z

  • Liquid water path

  • Geometrical thickness

  • Cloud-top height from vertical profile data


Gtr input satellite data
GTR – Input Satellite Data

Radiance density 0.6µm Radiance density 3.9µm[W/m2/µm/sr] [W/m2/µm/sr]


Gtr results
GTR - Results

11µmT[K]

t

Re[µm]

Terra-MODIS, 2002-08-05, 11:05 GMT


Example 2 sacura
Example 2 - SACURA

  • Semianalytical approach

  • SACURA retrieval

  • A. A. Kokhanovsky


Sacura retrieval of a ef t for 2 band algorithm 01
SACURA – Retrieval of aef & t for 2 band algorithm 01

  • VIS

  • NIR

  • can be calculated by simple approximation equations


Sacura retrieval of a ef t for 2 band algorithm 02
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


Sacura results
SACURA - Results

11µmT[K]

t

Re[µm]

Terra-MODIS, 2002-08-05, 11:05 GMT


Error estimation
Error Estimation

  • Theoretical Errors


Error estimation sacura
Error Estimation - SACURA

  • Error of R due to simplification of semi-analytical equations

(Kokhanovsky et al. 2003)


Error estimation gtr
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)


Intercomparison
Intercomparison

  • Intercomparison

  • SACURA vs. GTR. vs MOD06


Intercomparison sacura vs gtr vs mod06
Intercomparison SACURA vs. GTR vs. MOD06

aef [µm]

GTR SACURA MOD06

Terra-MODIS, 2001-07-18, 15:30 GMT

t

GTR SACURA MOD06


Intercomparison sacura vs gtr vs mod061
Intercomparison SACURA vs. GTR vs. MOD06

aef [µm]

GTR SACURA MOD06

t

GTR SACURA MOD06


Intercomparison sacura vs gtr vs mod06 a ef
Intercomparison SACURA vs. GTR vs. MOD06 - aef

Terra-MODIS, 2001-07-18, 15:30 GMT


Intercomparison sacura vs gtr vs mod06 t
Intercomparison SACURA vs. GTR vs. MOD06 - t

Terra-MODIS, 2001-07-18, 15:30 GMT


Intercomparison sacura vs gtr vs mod06 freq
Intercomparison SACURA vs. GTR vs. MOD06 – Freq.

Terra-MODIS, 2001-07-18, 15:30 GMT


Conclusion
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


Outlook
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


Acknowledgments
Acknowledgments

  • Alexander A. Kokhanovsky


Thank you
Thank you

  • The End


Intercomparison sacura vs gtr vs mod062
Intercomparison SACURA vs. GTR vs. MOD06

aef [µm]

GTR SACURA MOD06

Terra-MODIS, 2002-08-10, 09:45 GMT

t

GTR SACURA MOD06


Intercomparison sacura vs gtr vs mod06 a ef1
Intercomparison SACURA vs. GTR vs. MOD06 - aef

Terra-MODIS, 2002-08-10, 09:45 GMT


Intercomparison sacura vs gtr vs mod06 t1
Intercomparison SACURA vs. GTR vs. MOD06 - t

Terra-MODIS, 2002-08-10, 09:45 GMT


Intercomparison sacura vs gtr vs mod06 freq1
Intercomparison SACURA vs. GTR vs. MOD06 – Freq.

Terra-MODIS, 2002-08-10, 09:45 GMT


Intercomparison sacura vs gtr vs mod06 delta
Intercomparison SACURA vs. GTR vs. MOD06 – Delta

Terra-MODIS, 2002-08-10, 09:45 GMT


Sacura lambert surface reflection
SACURA – Lambert surface reflection

  • VIS

  • Large optical thickness  direct solar light term can be neglected

  • NIR

  • can be calculated by simple approximation equations


Error estimation sacura1
Error Estimation - SACURA

  • Error of retrieved parameters due to measurement errors and t

(Kokhanovsky et al. 2003)




Gtr preparation of luts1
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

ISCCP, Rossow et al. 1996


Retrieved parameters1
Retrieved Parameters

  • Cloud optical thickness [1... 70] resp. [5...150]

  • Cloud effective droplet radius [1...40 µm] resp. [1...140µm]

  • Cloud top height [0.1...10 km]

  • Liquid water path […50...200…g/m2]

  • Thermodynamic phase (ice, water, mixed clouds)


Intercomparison sacura vs gtr vs mod06 delta1
Intercomparison SACURA vs. GTR vs. MOD06 – Delta

Terra-MODIS, 2001-07-18, 15:30 GMT


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