marc schröder, fub tutorial, de bilt, 10.´04 photon path length distributions and detailed...

Marc Schröder, FUB Tutorial, De Bilt, 10.´04

Photon path length distributions and detailed microphysical parameterisations

Marc Schröder

Institut für Weltraumwissenschaften, Freie Universität Berlin, Berlin, Germany


Overview

• Effect of different LWC profiles on absorption intensities• Adiabaticity and mixing schemes• LWC profiles• Results

• Parameterisation of mean photon path• Approach• Dependence on sun zenith• Results

• Radiative transfer based on cloud dropet number spectra• Data and approach• Problems• Results

• Conclusions and outlook


• The change of the cloud droplet distribution, n(r), with increasing droplet growth corresponds to a translation in the squared radius, r2 (Brenguier, 1991).

Assuming adiabaticity, 2 can be determined (Schueller et al., 2004).

• 1000 bins for r between 0.3 and 31.6 microns

2

Schueller et al. (2003)

Effect of different LWC(z) on absorption intensitiesAdiabaticity


Effect of different LWC(z) on absorption intensitiesLWC profiles

constant liquid water path


How can deviations of LWCLES from the adiabatic LWCad be interpreted toderive microphysical properties?

Turbulent mixing of entrained dry air and cloudy parcels may result in twoextreme cases:

I) Homogeneous mixing: The mixing is faster than the droplet evaporation. All droplet are exposed to the same water vapor deficit.II) Heterogeneous mixing: The droplet evaporation is faster than the mixing. The droplets exposed to the entrained air are totally evaporated.

I) II)

Effect of different LWC(z) on absorption intensitiesMixing scheme


Mixing Scheme

I) Homogeneous: Effective radius, reff, changes while N = const

h = LWCLES / cw

II) Heterogeneous: reff = const and N changes

Define f = LWCLES / LWCad, then

= f * ad with being the volume

extinction coefficient. Single scattering albedo, phase function, and ad are defined by h = LWCad / cw.

An explicit knowledge of the cloud base is required for this procedure.


Photon path length

Each photon is traced until it gets absorbed or until it hits a boundary without being reflected.

If the photon reaches the detector, the travelled path is stored: P(l)

P(l)

h

2 hcos()

2

1 * *


Effect on absorption


Parameterisation of mean photon pathApproach

• Use adiabatic model of Schueller et al., 2004

• Define a set of optical thicknesses and droplet number concentrations N:

: 1, 2, 5, 10, 20, 50, 100N: 50, 100, 200, 400 cm-3

• Utilise Monte Carlo simulations to determine mean photon path length:

< l > = l P(l) dl


Interim result

H: geometrical cloud thickness


Parameterisation of mean photon path length

In addition to optical thickness, < l > depends significantly oneffective radius.

N

< l >


Dependence on sun zenith


Dependence on surface albedo

A = 0.4 A = 0

< l > = 1.33 km < l > = 0.47 km


Parameterisation of mean photon path lengthDependence on LWC profile

adiab cten rad cool subad

heterogeneous 0.365 0.426 0.370 0.405

homogeneous 0.365 0.422 0.380 0.416

Mean photon path length with N = 200 cm-3 and = 11

maximum impact of ~14%


3D RT simulations based on cloud droplet numbers

cloud droplet number spectra, n(r), from M. Leporini, LaMP, CNRS

• detailed microphysical model DECAM (Flossmann, 1985)• 3D non-hydrostatic mesoscale model (Clark et al., 1996)

3D cloud model with warm microphysics

• 39 cloud droplet bins cover a radius range form 1.25 to 100.8 microns


Problem

Validation of Monte Carlo model shows a need for highly resolved phase functions (100,000 bins for the scattering angle):

The phase function depends on radius, so that each droplet spectrum may resultin a different phase function, in total 127,000.

That amounts to 51 Gbyte working space.


Solution

Bin properties of all phase functions:

30 bins each900 phase functions

20 10 2 8 20


2D averaging based on RT simulations

directly from n(r) homogeneous

< > 2.57 3.20 < R > 0.16 0.12

dire

ctly

fro

m n

(r)

hom

ogen

eous

mix

ing


Explanation

• Different microphysical models between the homogeneous mixing and the cloud droplet approach (recall the number of bins and ranges for the radius)

• Surface albedo A=0

• mean optical thickness low

red: homogeneous black: n(r)


Conclusions

• Extinction at cloud top strongly affects the signal at absorbing channels.

• Significant to strong dependence of the mean photon path on cloud optical depth AND effective radius.

Potential improvements for retrieval schemes, either through direct simulation or subsequent adjustments.

It may increase the accuracy of gas absorption estimation in GCMs.

• The microphysical properties, in particular the phase function, can have strong effects on the overall reflectance and absorption intensity.

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