ESTIMATION OF AIR AND SURFACE ESTIMATION OF AIR AND SURFACE TEMPERATURE EVOLUTION OF THE EAST TEMPERATURE EVOLUTION OF THE EAST

ANTARCTIC SHEET BY MEANS OF PASSIVE ANTARCTIC SHEET BY MEANS OF PASSIVE MICROWAVE REMOTE SENSING MICROWAVE REMOTE SENSING

M. BrogioniM. Brogioni, G. Macelloni, S. Pettinato, , G. Macelloni, S. Pettinato, F.MontomoliF.Montomoli

IFAC - Institute of Applied PhysicsNational Research Council

Firenze, Italia

International Geoscience and Remote Sensing Symposium Vancouver, Canada, 24-29 July, 2011

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Introduction

• Antarctica is the coldest and emptiest place on Earth

• Antarctica influence directly the Earth climate due to its extension (14-30 million of km2) and average temperature ~ -50°C As a comparison: Arctic 8 million of km2, Greenland 2 million of km2, Europe 10 million of km2

• It is one of the most important indicators of the climate changes

• Knowledge about Antartica is limited due to the harsh environment

Monagham, WWI Mag. 22.13/20

South Pole

AntarcticaAntarctic Peninsula4% of Antarctica (like California)Glacial retreats are widespreadsand move to South

West Antarctica20% of Antarctica (like Greenland)Stores 6m of global sea levelMarine based (it rests over the sea)It is shrinking overall

East Antarctica76% of Antarctica (larger than USA)Stores 60m of global sea levelApproximatively in balanceMean altitude ~3000m

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Aim of the work

Passive microwave sensors are working since the 80s’ and can image Antarctica several times per day (up to 8 in the Dome C region (75° S)

Antartica is the most undersampled continent due to the cost of the manned exploration and the difficulties related to the impervious environment

The use of remote sensing techniques can help in monitoring the spatial and temporal characteristics of large regions.

Some interesting topics are the spatial and temporal evolution of temperatures, the snow mass balance, the detection of melting zones.

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Time

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0

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TBm37V

T50 (C)T 50

37 GHz

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Time

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TBm19V

T100 ( C )T 100

19 GHz

MW and Snow temperature data (Dome-C)

The temporal behavior of Tb was closely related to the snow temperature at

different depths

This analysis was conducted on more than 25000 images (at least five images per day)

The mean value of the 3x3 pixel area was extracted from each image in order to reduce noise.

6/17

y = 1.1186x - 63.874

R2 = 0.9796

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Snow Temperature 10 cm (K)

Tb

v 3

7 (

K)

y = 0.9298x - 14.053

R2 = 0.9035

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Snow Temperature 200 cm (K)

Tb

v 1

9 (

K)

37 GHz – 10 cm 19 GHz – 200 cm

Correlation analysis

Examples of correlation between snow temperature and brightness

Frequency T50 T100 T200 T300 T400 T600 T800 T1000

6.9 GHz 0.63 0.72 0.62 0.38 0.13 0.08 0.62 0.54

10 GHz 0.74 0.87 0.78 0.51 0.19 0.07 0.73 0.68

19 GHz 0.83 0.94 0.80 0.50 0.17 0.10 0.80 0.70

37 GHz 0.98 0.90 0.55 0.19 0.01 0.39 0.83 0.43

Determination coefficient (R2) between Tb and Snow Temperature at different depths

7/20

Experimental data

AMSR-E :

More than 45000 images Frequencies used: Ku, Ka V polarizationTime: January 2003- December 2008

AWS snow and air temperature measurements:

Air Snow Accumulation

1 89577 Dome A -80.368 77.374 16/01/2005 30/12/2008 X X X2 89578 Eagle -76.420 77.024 26/01/2005 30/12/2008 X X X3 Panda (N) -73.689 76.967 01/01/2008 31/12/2008 X X X4 89813 GC41 -71.603 111.263 28/10/1984 29/12/2005 X X5 89757 LGB20 -73.833 55.672 17/01/1991 31/08/2004 X X6 89568 LGB 35 -76.043 65.010 20/12/1993 30/06/2008 X X8 89828 Dome C -75.050 123.180 01/01/2005 31/12/2008 X X X9 89734 Dome Fuji -77.310 39.700 01/01/1997 31/12/2008 X10 JASE 2007 -75.890 25.830 01/01/2007 31/12/2008 X11 89544 Mizuho -70.700 44.290 01/10/2000 31/12/2008 X12 Panda (S) -82.320 75.990 01/11/2007 31/12/2008 X13 89744 Relay station -74.020 43.060 01/02/1995 31/10/2005 X14 Giulia -75.536 145.859 10/12/1997 31/12/2008 X15 Irene -71.653 148.656 22/11/2001 31/12/2008 X16 Concordia -75.100 123.300 27/01/2005 31/12/2008 X

LON Since ToIFAC index

WMO index

Station LAT

2008Data available

2004

2005

2006

2007

2003

GREEN - Australian Antarctic Survey BROWN - University of Wisconsin*

PURPLE - Italian National Project for Researches in Antarctica**Dome C data were collected also during the IFAC Domex experiment

AGO 1

AGO 4

Panda S

AGO 3

AGO 5

Dome C

GC 41

Giulia

Irene

Dome AEagle

Panda N

LGB 46LGB 35

LGB 20Dome Fuji

Relay Station

MizuhoJASE 2007

West Antarctica

Pen

insu

la

East Antarctica

No data were available in the period 2003-2008

Only air temperature was available

Air and snow temperature available

No data were available in the period 2003-2008

Only air temperature was available

Air and snow temperature available

AWS sites

Sites of the AWS consideredin this work

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Methodology

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The study was carried out by using linear regressions between ground measurements and satellite data (i.e. Tair and Tb 37GHz, Tsnow 50cm and Tb 19 GHz).

In order to keep the temporal variability of the datasets, Tair, Tsnow and Tb were not temporal averaged. Here we considered up to 8 measurements per day.

Brightness temperature were spatially averaged over a 3x3 pixel area in order to lower the noise. This has a tiny impact since the std dev of the 9 measurements is lower than 1K.

We didn’t use ANN techniques (already considered in previous works) because their performances seems to be comparable to the ones of the regressions for this kind of study.

Snow temperature retrieval

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R2 RMSE Equation R2 RMSE Equation

Ku, Ka 0.96319 1.7875 Tretr = 0.96231 Tmeas - 1.9594 0.9169 2.0812 Tretr = 0.90335 Tmeas - 5.1737X, Ku, Ka 0.96293 1.7924 Tretr = 0.96149 Tmeas - 1.9494 0.91864 2.0429 Tretr = 0.89834 Tmeas - 5.3126

Bands used

Retrieval with 2005 data

T50 T100

R2 RMSE Equation R2 RMSE EquationKu, Ka 0.96454 1.0167 Tretr = 1.059 Tmeas + 2.6165 0.92548 1.341 Tretr = 1.0168* Tmeas - 0.015978

X, Ku, Ka 0.96365 1.0308 Tretr = 1.0601 Tmeas + 2.6734 0.92633 1.3306 Tretr = 1.0152 Tmeas - 0.13871

Bands usedRetrieval with 2008 data

T50 T100

R2 RMSE R2 RMSE

Ku, Ka 0.98194 1.1735 0.97002 1.2076X, Ku, Ka 0.98213 1.1673 0.97184 1.1715

Bands used

Training with 2006 data

T50 T100

R2 RMSE Equation R2 RMSE EquationKu, Ka 0.98076 1.2154 Tretr = 0.98481 Tmeas - 0.98117 0.96849 1.2645 Tretr = 0.98999 Tmeas - 0.70031

X, Ku, Ka 0.96414 1.7666 Tretr = 0.98602 Tmeas - 0.9592 0.92158 2.0572 Tretr = 1.0068 Tmeas + 0.056809

T50 T100Bands usedRetrieval with 2006 data

R2 RMSE Equation R2 RMSE Equation

Ku, Ka 0.97021 0.9454 Tretr = 1.0776 Tmeas + 3.6287 0.93196 1.3355 Tretr = 1.0634 Tmeas + 2.5763X, Ku, Ka 0.97065 0.9424 Tretr = 1.0824 Tmeas + 3.8617 0.93691 1.3198 Tretr = 1.0943 Tmeas + 4.1456

Retrieval with 2008 data

T50 T100Bands used

R2 RMSE R2 RMSEKu, Ka 0.96401 1.7698 0.91807 2.09688

X, Ku, Ka 0.96414 1.7666 0.92158 2.0572

Training with 2005 dataBands used T50 T100

AN

N

Equation RMSE Bias Equation RMSE BiasKa Tretr = 0.9826 Tmeas + 1.1149 1.174 1.1346Ku Tretr = 0.9909 Tmeas + 0.5475 1.62 0.5525

T50 T100Band usedTraining with 2005 data -> Retrieval with 2006 data

Equation RMSE Bias Equation RMSE BiasKa Tretr = 1.0568 Tmeas + 2.3554 1.014 2.229Ku Tretr = 1.0679 Tmeas + 2.9725 1.433 2.7835

T100Band usedTraining with 2005 data -> Retrieval with 2008 data

T50

RE

GR

ES

SIO

NS

Developed for the year 2005

Equation RMSE Bias Equation RMSE BiasKa Tretr = 0.9624 Tmeas - 1.9582 1.79 -2.035Ku Tretr = 0.8631 Tmeas - 7.6921 2.08 -8.931

Band usedTraining with 2006 data -> Retrieval with 2005 data

T50 T100

Equation RMSE Bias Equation RMSE BiasKa Tretr = 1.056 Tmeas + 2.4288 1.0131 2.3Ku Tretr = 1.0111 Tmeas + 0.1812 1.3569 0.179

Band usedTraining with 2006 data -> Retrieval with 2008 data

T50 T100

Developed for the year 2006

Snow temperature retrieval (Dome C)

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Snow temperature retrieval (Dome A, Eagle)

Dome A and Eagle ground data were obtained from Australian AWS

In these sites, AWS measured Tsnow at 0.1, 0.3, 3 and 10 m below the surface

only Tsnow at 1m is estimated in this work.

Yeardelay (days) R2 RMSE

(K)p1 p2

2005 -4 0.89312 1.2276 0.7424 233.2

2006 -2 0.89307 1.2846 0.7907 235.7

2007 -3 0.86075 1.2385 0.8059 236.1

ALL -3 0.88568 1.2871 0.777 234.9

Eagle (76.43°S, 77.02°E)

Yeardelay (days)

R2 RMSE p1 p2

2005 2 0.96326 0.67422 0.5738 223.42006 <1 0.96367 0.65563 0.5548 222.52007 2 0.94298 0.76879 0.5785 223.82008 <1 0.94747 0.80977 0.5253 220.8

2009 -2 0.96976 0.598 0.5413 222.2

ALL <1 0.95151 0.76065 0.5515 222.3

Dome A (80.44°S, 77.21°E)

Regressions between Tb 19GHz and Tsnow 1m

R2 0.89R2 0.95

13/20

Dome A (80.44°S, 77.21°E)Eagle (76.43°S, 77.02°E)

We verified that (at least in these sites) it is possible to estimate snow temperature 1m below the surface with an RMSE of about 1.5K,

The error seems to be stable throughout the years

Test of the method was carried out at different latitude and longitude

Results of snow temperature retrieval

14/20

It is worth noticing that:

-The accuracy of the retrieval (i.e. the RMSE) and the determination coefficients (R2) obtained, makes this study useful for estimating the snow sub-superficial temperature when precision of 1K are sufficient,

Snow temperature retrieval

-For climatological issues, the obtained precision could not be adequate (i.e. if the accuracy required is one order of magnitude higher),

-It seems somewhat difficult to lower the RMSE of the relationships since the accuracy of the measuring instruments (i.e. the AMSR-E and SSM/I radiometers) is of the same order (around 1.5K).

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Air temperature retrieval

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Snow temperature variations are primarily driven by air temperature fluctuation which heat (and cool) the snow by convection. This is different from land surfaces whose temperature depends on the solar radiation.

Tair and Tsnow (on which depends the microwave Tb) are quite good correlated,

making possible an attempt to estimate air temperature from

Tb measurements.

Correlation between air and Tb at 37GHz (the highest frequency commonly used in the remote sensing of snow) is not high as with the Tsnow due to the heat latency of snow.

Few remarks

R2 = 0.6488

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Tsnow 10cm (°C)T

air

2m

(°C

) Data collected at Eagle in 2005

In order to obtain better performances it is useful to consider the temporal changes of snow emissivity

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Snow equivalent emissivity

In order to perform the Air temperature retrieval by means of MW data, we used an equivalent emissivity of snow obtained as

because the snowpack is subject to metamorphic changes due to the weather conditions (mainly air temperature and wind action).

Eagle Dome A

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Eagle Dome A

LGB35

Examples of air temperature retrieval results

Usually the average regression provide the best results!

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Results of the air temperature retrieval

Site Average relationship R2

Years consideredMean

RMSE (°C)2003 2004 2005 2006 2007 2008

Eagle Tb37GHz = 0.5256 Tair + 222.82 0.746 5.65

Dome A Tb37GHz = 0.5486 Tair + 211.75 0.775 8.06

LGB20 Tb37GHz = 0.6180 Tair + 233.64 0.843 5.4

LGB35 Tb37GHz = 0.8280 Tair + 226.40 0.919 3.9

Dome Fuji Tb37GHz = 0.6207 Tair + 216.25 0.744 7.48

Mizuho Tb37GHz = 0.7334 Tair + 203.91 0.62 6.28

Relay station Tb37GHz = 0.5642 Tair + 223.42 0.757 6.43

Giulia Tb37GHz = 0.7059 Tair + 221.93 0.854 5.25

Irene Tb37GHz = 0.4918 Tair + 233.14 0.692 8.71

Despite the quite high R2, the mean RMSE obtained is not very good (betw.4 and 8K)

Possible causes can be:- the heat latency of snow which damped the Tair variations, making the Tb slightly "insensitive" to the Tair variations,

- the quality of the AWS data were not always good due to the enviromental conditions which in some cases affect the normal service of the AWSs

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Future works

The results found outline that it is possible to retrieve the snow and air temperature from microwave data, albeit with a RMSE error of some degrees.

Next steps of this work will be the exploitation of the spatial and temporal trends of the retrieved Snow and Air temperatures over a long time period (since the 80’s) in order to assess the climate variations on the East Plateau. This will be obtained by using passive microwave data, consolidated relationships between Tsnow, Tair and Tb, and assimilation methods (like kriging).

Future possible improvements could be obtained by the joint use of microwave and infrared images, albeit these latter are affected by the weather conditions and, for a certain extent, by the diurnal cycle.

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Outline

Why to study Antarctica for the climate changes

Experimental data description

Retrieval of snow temperatures

Future actions

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0 10 20 30 40 50 60 70 80 90 100

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Dep

th (

m)

Layer Contribution (%)

CXKuKaP

ene

tra

tion

dep

th (

1/e

)

Model Analysis:Contribution of Layers (0-100 m)

Multilayer model based on the Strong Fluctuation TheoryInput: experimental data from Epica and Domex campaigns

25/17

Retrieval of snow temperature : 1 – 10 meters

 T10

0T200 T300 T400 T500 T600 T800 T1000

ΔT [°C] 25 17.03 10.30 6.74 4.31 3.39 1.60 0.99

R2 0.95 0.89 0.90 0.89 0.94 0.97 0.90 0.88

SE [°C] 1.9 1.21 0.74 0.54 0.26 0.17 0.12 0.08

SE/ΔT [%] 7.6 7.1 7.2 8 6 5 7.5 8.1

ΔT = Maximum – Minimum Temperature, R2 = Correlation coefficient , SE = Standard Error of Estimate, Err = Mean Percentage Error

Very good correlation !

26/17

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Tmeasured (°C)

Tre

trie

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d (

°C)

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Tmeasured (°C)

Tre

trie

ve

d (

°C)

Retrieval of snow temperature : 1 m

100 cm

y = 0.9909x + 0.5475R2 = 0.9531

100 cm

Trained 2005Retrieved 2008

y = 1.0679x + 2.9725R2 = 0.9688

Trained 2005Retrieved 2006

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Previous study: retrieval of Tsnow 0-2 meters

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Tsnow Measured [°C]

Tsn

ow R

etrie

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[°C

]

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Tsnow Measured [°C]

Tsn

ow R

etrie

ved

[°C

]

100 cm

Data measured for the year 2006 compared with the retrieved one. Relationship between Tb and Tsnow for the year 2005 were used for the retrieval

R2=0.98, SE=1.5 °C R2=0.95, SE= 1.9 °C

28/17

The electromagnetic model

The Brightness Temperature Tb was computed according to the

wave approach which accounts for reflection and transmission

between the layers by means of the propagating matrix

(Kong,1990).

The V and H components of Tb were obtained by adding the

contributions of the snow layers by means of the fluctuation

dissipation theorem (Jin,1984).

The obtained value of Tb was the results of the average of 50

realizations each one corresponding to a profile of (z)

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Model input parameters:

Density (z) was modeled as:

(z) = m + f(z) ;

m = measured mean value; f = fluctuating part

<f(z1) f(z2)> = p2 exp (- z1 – z2/lz) (Gaussian)

The correlation length was obtained from a semi-empirical

relationship derived from ice core data permittivity was

computed from the strong fluctuation theory as a function of

correlation length and density

Snow Temperature and Grain Sizes were obtained from

measurements

30/17

The snow measurements

31/17

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0

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100

Dep

th (

m)

Layer Contribution (%)

LCXKuKa

Model Analysis : Contribution of Layers (0-100 m)

Pe

netr

atio

n d

epth

(1

/e)

32/17

1

)(

)(*

*

*

*

nWn

Ws

xWxdw

dGi

i

jijij

i

The Getis local statistic of the i-th pixel

j

iji dwW )(*sum of the weight in the window

x and s are the mean and the standard deviation of the entire image

xj value of the j-th image pixel

)(dwij weight of the pixel : 1 if the pixel belong to the window, 0 elsewhere

i

j

Spatial and temporal analysis (I)

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Spatial and temporal analysis

Analysis performed on 2 orbits (22 and 23 images) in 2008

0

0.5

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3

Std

Dev

(K

)

6.8 GHz

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Std

Dev

(K

)

37 GHz

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(K

)

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Dev

(K

)

37 GHz

Spatial and temporal analysis (II)

Maps

Isolines

Temporal Std Dev

Spatial Getis statistic

2 orbits (22 and 23 images) in 2008

35/17

6.8 GHz

Spatial and temporal analysis (II)

Maps

Isolines

Temporal Std Dev

Spatial Getis statistic

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1.5

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3

Std

Dev

(K

)

Dome C

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Tmeasured (°C)

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trie

ve

d (

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50 cm

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Tmeasured (°C)

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trie

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d (

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100 cm

2005 2006

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Sn

ow

Tem

per

atu

re (

°C)

T50 retrieved T50 T100 retrieved T100

2005 2006

Based on the previous study, we performed a regression analysis in order to retrieve the snow temperature

Algorithm developed by using data collected in 2005

snow temperature retrieved for the year 2006

y = 0.9686x + 233.59

R2 = 0.9631160

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Snow Temperature @ -50cm (°C)B

rig

htn

es

s T

em

pe

ratu

re @

37

GH

z (K

)

2005

y = 0.474x + 216.69

R2 = 0.9096160

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Snow Temperature @ -100cm (°C)

Bri

gh

tne

ss

Te

mp

era

ture

@ 1

9 G

Hz

(K)

2005

Snow temperature retrieval

RMSE=1.16K RMSE=1.64K

37/17

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Tmeasured (°C)

Tre

trie

ved

(°C

)

50 cm

2005 2008

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Tmeasured (°C)

Tre

trie

ve

d (

°C)

100 cm

2005 2008

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Sn

ow

Tem

per

atu

re (

°C)

T50 retrieved T50 T100 retrieved T100

2005 2008

The algorithm was tested also with the Tsnow data of year 2008

Similar analysis were performed by developing algorithms for the years 2006, then validating them with data collected in different years.

Then, the retrieval was performed also by using ANN in a feed-forward multi-layer perceptron scheme (MLP) with some hidden layers of neurons between the input and output.

Snow temperature retrieval

RMSE=1.01K RMSE=1. 43K

38/17

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1997 1998 1999 2000 2001 2002 2003 2004 2005

Sn

ow

an

d A

ir T

emp

erat

ure

(°C

)

Tair T50 regression T100 ANN

Although it is not possible to verify the retrieved snow temperature values, these considerations indicate that the Tsnow estimation do not present appreciable problems

There is always a delay between the Tair and Tsnow temperature.

The range of T100 values is lower than the T50 one, which is in turn lower than the air temperature swing.

It is also worth noticing that the maximum in the Tair (which happened in 2002) corresponds to the maximum of the estimated Tsnow.

Retrieval of Tsnow for the past yearsSebbene nn ci siano dati per verifica

39/17

Analysis of temperature trends

The trend in the air temperature shows an increase of 1.3°C in the period 1997-2008

A first analysis seems to confirm that the temperature of the first layers increases

y = 6E-05x + 188.05

y = 0.0008x + 149.66160

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1997 1998 1999 2000 2000 2001 2002 2003 2004 2005 2006 2007 2008

Time (year)

Bri

gh

tnes

s T

emp

erat

ure

(K

)

TBm19V

TBm37V

Can the emissivity constantly increase?

Why Tb are constantly increasing?

y = 0.0003x - 62.157-90

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1997 1998 1999 2001 2002 2003 2005 2006 2007

Time (year)

Air

te

mp

era

ture

(°C

)