DRY SNOW

“Reading snow is like listening to music.
To describe what you've read is like explaining music in writing”
Peter Høeg, Smilla's Sense of Snow
reaDing SNOW
Notes on Microwave Remote
Sensing of Snow Cover
P. Pampaloni
IFAC-CNR, Firenze
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Why «reading» snow ?
 Snow cover has a fundamental role in the
global water cycle and is responsible and
indicator of climate changes at the same time
 Monitoring of nsow characteristics is very
important for the management of water
resources and the forecasting of dramatic
events (floods, landslides avalnches etc.)
Abetone 1948
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Neve
………..dirò come le nevi un tempo venivano indicate dalle mie parti ……….
 Brüskalan,
……….. la prima neve dell’ inverno quella vera,
 (quando)………i richiami dei passeri e degli scriccioli si facevano lievi …. la
brüskalan diventava sneea: neve abbondante e leggera.
 Quando l’ inverno stava per finire la sneea diventava
haapar
 Con l’ haapar veniva l’ haarnust. ….la neve vecchia che verso primavera,
nelle ore calde, il sole ammorbidisce e che poi il freddo della notte
indurisce.
 …Dopo l’ haarnust veniva la swalbasneea: la neve della rondine, la neve di
marzo che è sempre puntuale nei secoli….In una notte può caderne fino a un
metro.
 La kukusnea è la neve d’ aprile….(la neve del cuculo) …sui prati …dove sono
fioriti i crochi non si ferma molto.
 Una nube che scende da nord, una ventata, un rapido abbassamento della
temperatura ed ecco a maggio la bàchtalasnea Dura solo poche ore, ma
sufficiente a far paura agli uccelli.
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Mario Rigoni Stern: Sentieri sotto la neve
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Snowfall
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Snowfall
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Structure and
metamorphism of snow
Destructive metamorphism
Constructive metamorphism
Sintering
After a snowfall, the shapes of the
numerous ice particles in dry snow
are modified by metamorphism.
For large temperature gradients (> 1°C/10cm), water vapour is
produced by sublimation at warm grain surfaces, and is
deposited at colder surfaces. Rounded grains are transformed in
facet like crystals
Thermodinamically, the ice crystals seek equilibrium, for
which the ratio of surface area to volume is minimum.
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Structure and
metamorphism of snow
WET SNOW is a mixture
of air, ice particles and liquid water.
Two regimes of liquid saturation.
 PENDULAR: liquid occurs in the form of isolated inclusions into the
continuous porous air/ice medium.
 FUNICULAR: air occurs as distinct bubbles into liquid
The metamorphism caused by melting and freezing changes
the microstructure of snow; the grains become rounded
during the melting process, and some of the smaller grains
disappear completely. Snow that has undergone several
melt-freeze cycles tends to form multiple clusters.
P. Pampaloni – IFAC - CNR
Snow microstructure
1 mm
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Types of snow cover
 Snowpack, is the total of all the snow and ice on the ground. It
includes both new snow and previous snow and ice that have not
melted.
 New snow is a recent snow deposit in which the original form of
the ice crystals can be recognized.
 Firn is rounded, well-bonded snow that is older than one year and
has a density greater than 550 kilograms per cubic meter, or 55
percent.
 Névé is young, granular snow that has been partially melted,
refrozen and compacted;
 Old snow indicates deposited snow whose transformation is so far
advanced that the original form of the new snow crystals can no
longer be recognized.
 Seasonal snow refers to snow that accumulates during one season
or snow that lasts for only one season.
 Perennial snow is snow that persists on the ground year after year.
 Powder snow is dry new snow, which is composed of loose, fresh
ice crystals.
P. Pampaloni – IFAC - CNR
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“Reading Snow”:
Ground measurements
 Temperature
Courtesy of Andrea
Crepaz
CVA Arabba
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Routes of reading
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Optical Remote Sensing of Snow cover
Optical and nearinfrared sensors can
monitor the seasonal
variations of
snow
cover in alpine areas
in
cloud
free
conditions.
September
Subpixel Resolution Snow
Mapping
from
Landsat
Thematic Mapper
(Rosenthal & Dozier, Water Resour.
Res., 1996)
February
April
Only microwave sensors are
able
to
acquire
data
independently of day light and
weather conditions and to
estimate
snow
water
equivalent.
P. Pampaloni – IFAC - CNR
Microwave Remote Sensing of snow
Multifrequency
Radiometers
Synthetic Aperture
Radar (SAR)
Multifrequency 7 – 90 GHz
Global observation
Low ground resolution (40 – 3 Km)
Single Frequency L, C, X bands
High ground resolution (up to 1 m)
Global to regional observations
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Electromagnetic characteristics of snow
SOIL/VEG
ε’ = 3 – 20
ε’’ = 0.5 - 2
WET SNOW
DRY SNOW
mixture of ice particles and air voids
ε d = f (snow density)
εd’ = 1.2 - 3.5
ε d’’ << 0.1
PD =
√ε′
λ
2πε"
P. Pampaloni – IFAC - CNR
mixture of ice particles, air voids
and water droplets
εw = f (snow wetness)
εw’ = 1.4 - 5.0
εw’’ = (0.1 – 0.6)
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As the frequency increases
εw’ decreases and εw’’ increases
Influence of snow on microwave measurements
Snow
type
Main
characteristics
Influence of
increasing SD
Effect of
frequency
Dry
Snow scatters
incident
radiation and
emission from
ground below
Increases
scattering
(Decreases
Scattering
increases with
frequency
Wet
Strong
absorption
Brightness
Temperature and
increases
Backscattering)
No effect
More sensitive
at higher
frequencies
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Readapted from Amlien 2008
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Experimental approaches for detecting snow
cover
Temporal trends
Dry snow
Wet snow
19 GHz
Radiometer
Snow Depth (SD) = f {Tb (Ku, Ka,V, H})
37 GHz
Brogioni et al. IEEE TGRS 2009
5.3 GHz
Radar
Dry : SD= f { σ° (Ku,X,C)}

 Wet : Change detection
σ°
(C, X band ): 2-3 dB below reference
level of bare soil
“It had begun to snow again. He watched sleepily the flakes, silver and dark,
falling obliquely against the lamplight. …..Yes, the newspapers were right: snow
was general all over Ireland. It was falling on every part of the dark central
15 plain,
on the treeless hills, falling softly upon the Bog of Allen and, farther westward,
softly falling into the dark mutinous Shannon waves.” J. Joyce Dubliners
Simple models simulating microwave
emission and scattering from dry and
wet snowpack
Direct contribution from snowpack
Interaction soil snow
Contribution from soil
attenuated by snow
EMISSION
Single scattering Radiative Transfer
(Ulaby et al. 1986)
BACKSCATTERING
Water cloud
ke = kem ρ extinction (?) P. Pampaloni – IFAC - CNRσpack (𝝑) = 𝑻𝟐(σv
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+ σsoil 𝐞𝐱𝐩(−𝟐𝒌𝒆𝒅𝒔𝒆𝒄𝝑′ )
“First readings”: Snow cover from
Multifrequency Radiometer
http://www.wmo.int/pages/prog/hwrp/
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P. Pampaloni – IFAC - CNR
Snow cover from C-band SAR
26 January 2004
5 April 2004
10 May 2004
Light blue: dry-snow
Blue: wet snow
Green: forests
Brown: bare soil
Red: layover and
shadow areas
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P. Pampaloni – IFAC - CNR
Open questions
 Penetration Depth
 Sensitivity of microwave emission and scattering to
snow cover parameters of hydrological interests
SNOW






Depth
Snow Water Equivalent (SWE)
Density
Grain size and shape
Layering
Wetness
Temperature
SOIL




Permittivity (moisture)
Surface Height Standard Deviation
Correlation function
Correlation length
The retrieval of Snow
Depth/SWE
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“Sensate esperienze e certe dimostrazioni”
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“Sensate esperienze e certe dimostrazioni”
Experiments
EM MODELS
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Microwave Observations
Satellite
Ground/Airborne
RADIOMETER
1978 SMMR (1987) C-Ka
1987 SMM/I Ku-Ka
2002 AMSR-E (2011) C-Ka
2003 SMMIS Ku-Ka
2003 WINDSAT C-Ka
2012 AMSR2 C-Ka
SAR
1991 ERS-1 (2000) C-band
1992 JERS-1 (1998)L-band
1995 ERS-2 (2003/ 2011)
1995 RADARSAT I (C-band)
2002 ENVISAT (2012) (C-band)
2006 ALOS (2011) L-band
2007 June: COSMO Skymed
2007 June: TerraSAR X
2007 December: RADARSAT II
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Microwave Emission and Scattering Models
SEMI-EMPIRICAL
EMISSION
HUT (Pulliainene et al. 1999
RTT scattering mainly forward
THEORETICAL
SFT (Tatarskii & Gertsenshtein,
Continuous medium
inhomogeneities
1963)
with
random
Multilayer HUT
DMRT/QCA (Tsang et al. 2007)
Scattering particles closely
Coherent Multiple scattering
MEMSL (Wiesman&Maetzler 1999)
Matrix Doubling (Du et al. 2010)
6 fluxes coherent multilayer
multiple scattering
packed
Backscattering – multiple scattering
Bicontinuous Medium (Xu et al. 2012)
SCATTERING
Backscattering
Water cloud
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The DMRT Model
“Nessuna certezza delle scienze è dove non si pò
applicare una delle scienze matematiche, ovver
che non sono unite con esse matematiche"
Leonardo da Vinci, Codice G, 1492/1516
Snow: DMRT–QCA (Dense Medium Radiative
Dry snow :Spherical ice particles
embedded in air
Wet snow
Transfer Model, Quasi Crystalline Approximation)
(Tsang et al. 2007)
Multilayer (Brogioni 2011)
Comparison of model with experimental data
Soil
Co-pol: AIEM (Chen et al. 2003)
X-pol: Oh et al. model
Pampaloni et al. Proc. IGARSS 2012
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Sensitivity of em parameters to grain radius and
snow density (dry snow)
X-band
Density
150-400 Kgm-3
Grain radius
0.1-1.3 mm
Density
150-400 Kgm-3
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Brogioni et al. Proc. IGARSS ,2012
P. Pampaloni – IFAC - CNR
Model analysis (QCA-DMRT)
Penetration depth (Dry Snow)
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P. Pampaloni – IFAC - CNR
Sensitivity of Brightness Temperature to dry snow
parameters
Snow depth
19 GHz
0.1 mm
0.2 mm
37 GHz
0.3 mm
0.4 mm
0.5 mm
Brightness Temperature (K)
Snow density
300
250
19 GHz
200
- - - GR = 0.3 mm
150
V pol
—GR = 0.3 mm
37 GHz
100
0
100
200
300
400
500
Density (Kg/m3)
Brogioni et al. Microrad 2008
P. Pampaloni – IFAC - CNR
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Backscattering sensitivity to Snow Dept, Density
and grain size: model simulations (dry snow)
Density =250 kg m-3, GR=0.7mm
17.0 GHz
9.5 GHz
5.3 GHz
1.2 GHz
VV Backscattering (dB)
Density =250 kg m-3, GR=0.7mm
150 Kgm-3
Density
Total
snow
400 Kg-3
X band
Grain Radius
Brogioni et al; Proc. IGARSS 2012
soil
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Θ =35, soil HStD=0.5cm, L=, 6cm
Layering
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Ding et al. IEEE Trans. Geosci. Remote Sens. 2008
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Today’s reading: Maps of Snow Depth/SWE
February 2010
Pettinato et al. IEEE GRSL 2012
Santi et al. HESSD 2012
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Future Redaings:
the contribution of Ku-band
(mm)
P. Pampaloni – IFAC - CNR
Grain radius: 0.5 mm 31
Incidence angle: 35°
The enlarging of our views in mathematics, and the possibility
of new discoveries, are infinite; and the same is the case with
the discovery of new properties of nature, of new powers and
laws, by continued experience and its rational combination
I. Kant - Prolegomena
 Microwave remote sensing is an efficient tool for a synthetic
“reading” of snow for both cognitive and applicative ends.
 Experiments, together with em models and retrieval algorithms,
makes it possible a critical analysis of “texts” as well as the
refinement of the investigation methods for more in depth
“readings”
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P. Pampaloni – IFAC - CNR
Thanks for
the
attention
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Abetone 2008
P. Pampaloni – IFAC - CNR
Questions ?
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