S n o w s p e c i fj c s u r f a c e a r e a r - - PowerPoint PPT Presentation

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S n o w s p e c i fj c s u r f a c e a r e a r - - PowerPoint PPT Presentation

S n o w s p e c i fj c s u r f a c e a r e a r e t r i e v a l f r o m r e fm e c t a n c e m e a s u r e m e n t s : t h e q u e s t i o n o f s n o w g r a i n s h a


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SLIDE 1

H e l s i n k i – A l b e d

  • w
  • r

k s h

  • p

A

  • u

t 2 1 6

S n

  • w

s p e c i fj c s u r f a c e a r e a r e t r i e v a l f r

  • m

r e fm e c t a n c e m e a s u r e m e n t s : t h e q u e s t i

  • n
  • f

s n

  • w

g r a i n s h a p e

Q . L i b

  • i

s

1

, G . P i c a r d

1

, M . D u m

  • n

t

2

, L . A r n a u d

1

S e e P

  • s

t e r f

  • r

m

  • r

e d e t a i l s

1

U G A / C N R S , L a b

  • r

a t

  • i

r e d e G l a c i

  • l
  • g

i e e t G é

  • p

h y s i q u e d e l ' E n v i r

  • n

n e m e n t ( L G G E ) U M R 5 1 8 3 , G r e n

  • b

l e , F

  • 3

8 4 1 , F r a n c e

2

M é t é

  • F

r a n c e – C N R S , C N R M – G A M E U M R 3 5 8 9 , C e n t r e d ' E t u d e s d e l a N e i g e , G r e n

  • b

l e , F r a n c e

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SLIDE 2

A s i m p l e n a i v e q u e s t i

  • n

: S n

  • w

a l b e d

  • d

e p e n d s

  • n

g r a i n s i z e ( i n t h e n e a r i n f r a r e d ) b u t d

  • e

s i t a l s

  • d

e p e n d

  • n

g r a i n s h a p e ? I n

  • t

h e r t e r m s : w h a t i s t h e u n c e r t a i n t y d u e t

  • n
  • t

k n

  • w

i n g t h e g r a i n s h a p e w h e n e s t i m a t i n g g r a i n s i z e f r

  • m

a l b e d

  • m

e a s u r e m e n t ?

C

  • n

t e x t

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SLIDE 3

I t i s n a i v e b e c a u s e a m e t r i c s f

  • r

“ g r a i n s i z e ” h a s t

  • b

e c h

  • s

e n b e f

  • r

e a d d r e s s i n g t h e q u e s t i

  • n
  • f

t h e s e n s i t i v i t y t

  • t

h e s h a p e .

C

  • n

t e x t ?

N u m e r

  • u

s e l e c t r

  • m

a g n e t i c

  • r

r a y t r a c i n g c a l c u l a t i

  • n

s

  • n

i n v i d u a l c r y s t a l s h a v e s h

  • w

n t h a t t h e r a t i

  • S

u r f a c e / V

  • l

u m e

  • f

t h e c r y s t a l s i s t h e « b e s t » p r e d i c t

  • r

f

  • r

t h e a l b e d

  • ,

a t l e a s t f

  • r

c

  • n

v e x c r y s t a l s T r i l

  • g

y b y G r e n f e l l e t a l . ( 1 9 9 9 , 2 3 , 2 5 )

  • n

i c e c r y s t a l s : “ R e p r e s e n t a t i

  • n
  • f

a n

  • n

s p h e r i c a l i c e p a r t i c l e b y a c

  • l

l e c t i

  • n
  • f

i n d e p e n d e n t s p h e r e s f

  • r

s c a t t e r i n g a n d a b s

  • r

p t i

  • n
  • f

r a d i a t i

  • n

” Wa r n i n g : “ b e s t ” d

  • e

s n

  • t

m e a n t h a t S / V i s a “ p e r f e c t ” p r e d i c t

  • r

. E v e n w i t h t h e s a m e S / V , t w

  • c

r y s t a l s w i t h d i fg e r e n t s h a p e s h a v e d i fg e r e n t a l b e d

  • .
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SLIDE 4

S

  • h

a s b e e n i n t r

  • d

u c e d t h e m i s l e a d i n g

  • p

t i c a l r a d i u s : « t h e

  • p

t i c a l r a d i u s i s t h e r a d i u s

  • f

t h e c

  • l

l e c t i

  • n
  • f

i d e n t i c a l s p h e r e s t h a t h a v e t h e s a m e S / V r a t i

  • a

s t h a t

  • f

s n

  • w

» . Wh y s p h e r e s ? Wh y n

  • t

t

  • k

e e p S / V w h i c h i s d e fj n e d f

  • r

a n y b i

  • p

h a s i c m e d i u m , w h a t e v e r i t i s m a d e

  • f

g r a i n s

  • r

n

  • t

? F l

  • r

e n t D

  • m

i n é a n d M a r t i n S c h n e e b e l i h a v e r e

  • i

n t r

  • d

u c e d a n d m a d e p

  • p

u l a r t h e n

  • t

i

  • n
  • f

“ s p e c i fj c s u r f a c e a r e a ” ( S S A ) . S S A = S u r f a c e / m a s s S S A = S u r f a c e / ( V

  • l

u m e * r h

  • _

i c e )

C

  • n

t e x t

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SLIDE 5

I n t h e R T t h e

  • r

y , t h e g r a i n s i z e a n d s h a p e i n fm u e n c e t h e s c a t t e r i n g a n d a b s

  • r

p t i

  • n

c

  • e

ffj c i e n t s a n d t h e p h a s e f u n c t i

  • n

. S n

  • w

i s a w e a k l y a b s

  • r

b i n g m e d i u m i n t h e v i s i b l e / n e a r

  • i

n f r a r e d → A s y m p t

  • t

i c R a d i a t i v e T r a n s f e r T h e

  • r

y ( A R T T ) b y K

  • k

h

  • n

a v s k y e t a l . : G r a i n s h a p e f a c t

  • r

i s a m u l p l i y i n g f a c t

  • r
  • f

g r a i n s i z e

T h e

  • r

e t i c a l r e s u l t s

α(λ)=exp(−12 7 (1+2cosθ)√ 2 3 γ(λ) 1 ρiceSSA B 1−g)

Angle Ice absorption → wavelength dependence Grain size Grain shape

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SLIDE 6

D e t e r m i n i n g s n

  • w

s p e c i fj c s u r f a c e a r e a f r

  • m

n e a r

  • i

n f r a r e d r e fm e c t a n c e m e a s u r e m e n t s : N u m e r i c a l s t u d y

  • f

t h e i n fm u e n c e

  • f

g r a i n s h a p e ( P i c a r d e t a l . 2 9 ) R a y

  • t

r a c i n g c a l c u l a t i

  • n
  • n

s i m p l e g e

  • m

e t r i c s h a p e F

  • r

a w i d e r a n g e

  • f

g e

  • m

e t r i c s h a p e , t h e g r a i n s h a p e f a c t

  • r

v a r i e s b y a f a c t

  • r

2 ! F

  • r

a g i v e n S S A , a l b e d

  • c

a n v a r y b y u p t

  • 1

. 4 d u e t

  • s

h a p e

T h e

  • r

e t i c a l r e s u l t s

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SLIDE 7
  • g

i s t h e a s y m e t r y f a c t

  • r

(

  • r

t h e 1

s t

m

  • m

e n t

  • f

t h e p h a s e f u n c t i

  • n

i n L e g e n d r e s e r i e ) : m e a u r e t h e d e g r e e

  • f

f

  • r

w a r d v e r s u s b a c k w a r d s c a t t e r i n g

  • B

i s t h e a b s

  • r

p t i

  • n

e n h a n c e m e n t p a r a m e t e r ( K

  • k

a n

  • v

s k y a n d Z e g e 2 4 ) M e a s u r e t h e l e n g t h i n g

  • f

t h e p a t h i n t h e g r a i n I m p

  • r

t a n t : t h e y d e p e n d

  • n

s h a p e a n d r e f r a c t i v e i n d e x a n d a r e c

  • m

p u t e d f r

  • m

e l e c t r

  • m

a g n e t i c p r i n c i p l e ( n

  • t

p u r e l y g e

  • m

e t r i c a l a s S S A i s )

α(λ)=exp(−12 7 (1+2cosθ)√ 2 3 γ(λ) 1 ρiceSSA B 1−g)

T h e

  • r

e t i c a l r e s u l t s

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SLIDE 8

I n L i b

  • i

s e t a l . 2 1 3 : C a l c u l a t i

  • n

s f r

  • m

K

  • k

h a n

  • v

s k y a n d M a c k e , 1 9 9 7 ; P i c a r d e t a l . , 2 9 B a n d g f

  • r

g e

  • m

e t r i c a l s h a p e s . → + r a n d

  • m

m i x t u r e b y M a l i n k a e t a l . 2 1 4

Spheres

α(λ)=exp(−12 7 (1+2cosθ)√ 2 3 γ(λ) 1 ρiceSSA B 1−g)

x 2

S p h e r e s h a v e a m e d i u m B / ( 1

  • g

)

T h e

  • r

e t i c a l r e s u l t s

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SLIDE 9

I n L i b

  • i

s e t a l . 2 1 3 : C a l c u l a t i

  • n

s f r

  • m

K

  • k

h a n

  • v

s k y a n d M a c k e , 1 9 9 7 ; P i c a r d e t a l . , 2 9 B a n d g f

  • r

g e

  • m

e t r i c a l s h a p e s . →

Spheres

α(λ)=exp(−12 7 (1+2cosθ)√ 2 3 γ(λ) 1 ρiceSSA B 1−g)

x 2 x 4

ke(λ)=ρ√ 3 2 γ(λ) SSA ρice B(1−g)

S p h e r e s h a v e a m e d i u m B / ( 1

  • g

) E fg e c t i v e e x t i n c t i

  • n

c

  • e

ffj c i e n t : S p h e r e s h a v e a n e x t r e m e B ( 1

  • g

)

T h e

  • r

e t i c a l r e s u l t s

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SLIDE 10

Theoretical Calculation for spheres

E x p e r i m e n t a l r e s u l t s

Wh a t a b

  • u

t n a t u r a l s n

  • w

? D i ffj c u l t b e c a u s e S S A n e e d s t

  • b

e m e a s u r e d b y a n i n d e p e n d e n t m e t h

  • d
  • f

t h e

  • p

t i c s

  • G

a l l e t e t a l . 2 9 : S S A m e a s u r e d b y m e t h a n a d s

  • r

p t i

  • n

E x p e r i m e n t a l r e s u l t s a r e c l

  • s

e t

  • t

h e c a l c u l a t i

  • n

f

  • r

s p h e r e s .

  • N

u m e r

  • u

s

  • f

( u n p u b l i s h e d ) r e s u l t s a c q u i r e d i n 2 1 4 ( D a v

  • s

i n t e r c

  • m

p a r i s

  • n

) s e e m s t

  • c
  • n

fj r m t h i s fj n d i n g .

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SLIDE 11

L i b

  • i

s e t a l . 2 1 4 : D e s i g n a n

  • p

t i c a l t

  • e

s t i m a t e t h e s h a p e p a r a m e t e r s . O n l y B c a n b e e s t i m a t e d w h e n S S A i s n

  • t

a v a i l a b l e → U s i n g c

  • n

c

  • m

i t t e n t m e a s u r e m e n t s

  • f

p r

  • fj

l e s

  • f

i r r a d i a n c e ( S

  • l

e x s ) a n d r e fm e c t a n c e ( A S S S A P ) a t D

  • m

e C B →

Solexs (Libois 2014 et al., Picard et al. 2016 TCD) ASSSAP (Arnaud et al. 2011)

α(λ)=exp(−36 7 √ 2 3 γ(λ) 1 ρice SSA B 1−g) ke(λ)=ρ√ 3 2 γ(λ) SSA ρice B(1−g) B= ρice 4ρ γ(λ) ln(α(λ))ke(λ)

E x p e r i m e n t a l r e s u l t s

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SLIDE 12

Density probability of B for all samples

5 6 s n

  • w

l a y e r s a t D

  • m

e C + 3 6 s a m p l e s i n t h e A l p s B

s n

  • w

= 1 , 6 ± , 2 > B

s p h e r e

= 1 , 2 5 N

  • c

l e a r d e p e n d e n c e t

  • t

h e « v i s u a l » s h a p e

E x p e r i m e n t a l r e s u l t s

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SLIDE 13

M i x i n g B f r

  • m

L i b

  • i

s a n d S S A

  • a

l b e d

  • f

r

  • m

G a l l e t e t a l . 2 9

Spheres Natural snow

E x p e r i m e n t a l r e s u l t s

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SLIDE 14

C

  • n

c l u s i

  • n

& P e r s p e c t i v e s

  • T

h e

  • r

y s a y s : S S A r e t r i e v a l f r

  • m

m e a s u r e d a l b e d

  • l

a r g e l y d e p e n d s

  • n

g r a i n s h a p e

  • E

x p e r i m e n t a l r e s u l t s

  • n

n a t u r a l s n

  • w

d i s a g r e e

  • n

« l a r g e l y » .

  • T

h e r a n g e

  • f

B a n d g w

  • u

l d b e n a r r

  • w

e r . N

  • c

l e a r r e l a t i

  • n

s h i p b e t w e e n B , g a n d « v i s u a l » g e

  • m

e t r i c a l s h a p e . S e e Q u e n t i n L i b

  • i

s ' p

  • s

t e r f

  • r

m

  • r

e d e t a i l s .

  • I

n c r e a s i n g e x p e r i m e n t a l e v i d e n c e s t h a t s n

  • w
  • p

t i c a l p r

  • p

e r t i e s d i fg e r f r

  • m

t h a t

  • f

s p h e r e s .

  • b

u t u s i n g s p h e r e s h a s l i t t l e i m p a c t

  • n

t h e a l b e d

  • /

l a r g e i m p a c t

  • n

t h e p e n e t r a t i

  • n

d e p t h .

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SLIDE 15

C

  • n

c l u s i

  • n

& P e r s p e c t i v e s

To play with shape : TARTES radiative model (in Python), Libois et al. 2013 Also TARTES webapp: http://snowtartes.pythonanywhere.com

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SLIDE 16

C

  • n

c l u s i

  • n

& P e r s p e c t i v e s

  • The number of studies on the shape of natural snow is still insufficient.

On promising direction : Calculation on tomography

Preliminary results

2- Advanced geometrical metrics by H. Löwe, SLF Krol and Löwe, Relating optical and microwave grain metrics of snow: The relevance of grain shape, The Cryosphere Discussion 1- Raytracing with SnowRAT (Picard et al. 2009) improve to support tomography Tomo support Coll F. Flin, M. Dumont, CEN

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SLIDE 17