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Passive microwave measurements of sea ice
Leif Toudal Pedersen, DMI
Natalia Ivanova, NERSC
Thomas Lavergne, met.no
Rasmus Tonboe, DMI
Roberto Saldo, DTU
Marko Mäkynen, FMI
Georg Heygster, U-Bremen
Anja Rösel, U-Hamburg
Stefan Kern, U-Hamburg
Gorm Dybkjær, DMI
Algorithm evaluation
• Sensitivity to atmosphere
– Open water dataset
– Simulated data incl RTM corrected TBs from RRDP
– Met/ocean data screening
• Sensitivity to emissivity variations
– 100% ice dataset
– Simulated data
– Snow/ice/atmosphere data screening
• Summer performance (snow/ice melt and melt-ponds)
• SMMR vs SSMI vs AMSR-E performance
• Thin ice performance
• Potential resolution
Convergence at ca. 100%
• Deformation field between 20100109 and 20100110 from ENVISAT ASAR WSM
Dataset of daily data from June 2007to present exists from PolarView/MyOcean at DTU
Red: ConvergenceBlue: Divergence
Artificial data combinations
• Some algorithms have cut-offs/non-linearities that does not allow a thorough validation at only 0 and 100% ice.
• We have generated a dataset of 15% ice and 85% ice using our 0% and 100%
• SIC15 = 0.85*SIC0(t)+0.15*SIC100(avgFY)
• SIC85 = 0.85*SIC100(t)+0.15*SIC0(avgW)
Algorithm comparison, SIC=15%, AMSR, no WF
Near 9
0GHz
Near 9
0GHz
lin, d
yn ASIP90
(NRL+N90
Lin)/2
(CF+N90
L)/2
(NRL+CF+N90
Lin)/3
Bootstra
p P (C
P)P37
(NT+CF+N90
Lin)/3
ECICE
P18 PR
Bristo
l
NASA Tea
m 2
(CF+N90
L*CF)/(
1+CF)
NASA Tea
m (N
T)
OSIS
AF-2
OSIS
AF
(NT+CF)/2 P10
(CF+N90
L*CF**
2)/(1
+CF**2)
(CF+N90
L*CF**
3)/(1
+CF**3)
OSIS
AF-3
UMas
s-AES
TUD
NORSEX
CalVal
Bootstra
p F (C
F)
One
chan
nel (6
H)
IOM
ASA IRT
0
5
10
15
20
25
30
35
Algorithm comparison, SIC=85%
PRP18 P37 P10
Bootstra
p P
(NRL+N90
Lin)/2 P90
Near 9
0GHz
lin, d
yn
IOM
ASA IRT
Bootstra
p F
CalVal
UMas
s-AES
TUD
NORSEX
Near 9
0GHz
NASA Tea
m
One
chan
nel (6
H)ASI
(NRL+CF+N90
Lin)/3
ECICE
(CF+N90
L*CF**
3)/(1
+CF**3)
(CF+N90
L)/2
(CF+N90
L*CF**
2)/(1
+CF**2)
(CF+N90
L*CF)/(
1+CF)
(NT+CF+N90
Lin)/3
Bristo
l
OSIS
AF
OSIS
AF-2
OSIS
AF-3
(NT+CF)/2
NASA Tea
m 2
0
5
10
15
20
25
30
35
NT2
• Has a bias of 10-15% at 85% ice, so a lot of datapoints at 85% are truncated at 100%
• Real performance at SIC=85% is something like 10% (estimated from SSMI results that are less biased)
Weather filters
• Weather filters are supposed to remove open water points that show ice because of atmospheric influence (set SIC=0) .
• We have established further artificial datasets at 20, 25 and 30% SIC for testing of weather filters.
Weather filters
• Tested and we found that they remove ice up to sic>25%
• We therefore generated a subset of RRDP with appended ERA Interim– Calculated corrections of TBs due to wind,
water vapour and temp. Tried CLW but it was bad
• Applied all algorithms to this new set
Atmospheric correction
Upwelling + surface contrib.
Reflected downwelling contrib.
Reflected sky contrib.
Tap = εTs + n
IOMASA IRT
• Simple assimilation of TB (0Dvar)
• RTM + surface emisivity forward model
• Climatology as background state
• Using only 6, 10, 18, 23 and 37
• Not 89 pt.
• No SSMI pt
AMSR - February 4, 2006
Ice concentration MY-fraction Ice temperature ”Error”
SSTWater Vapour Cloud liquid water Wind Speed
RRDP results, SIC=0
• Small RMS error
• 1.71%
• Small bias
• -0.05%
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 80
1000
2000
3000
4000
5000
6000
7000
Integrated retrieval of SIC=0, all year
Frequency
CF(rtm) vs IOMASA(irt) for SIC=0
0,0
50,0
100,0
150,0
200,0
-10,0 -5,0 0,0 5,0 10,0
Histogram
Bootstrap_f
Co
un
t
0,0
500,0
1000,0
1500,0
2000,0
-0,1 -0,1 0,0 0,1 0,1
Histogram
SICx
Co
un
t
Stdev=2.50 (before atm 4.3) Stdev=1.71
Ice conc in fractions of 1Ice conc in %
Algorithm comparison, SIC=15%, AMSR, no WF
Near 9
0GHz
Near 9
0GHz
lin, d
yn ASIP90
(NRL+N90
Lin)/2
(CF+N90
L)/2
(NRL+CF+N90
Lin)/3
Bootstra
p P (C
P)P37
(NT+CF+N90
Lin)/3
ECICE
P18 PR
Bristo
l
NASA Tea
m 2
(CF+N90
L*CF)/(
1+CF)
NASA Tea
m (N
T)
OSIS
AF-2
OSIS
AF
(NT+CF)/2 P10
(CF+N90
L*CF**
2)/(1
+CF**2)
(CF+N90
L*CF**
3)/(1
+CF**3)
OSIS
AF-3
UMas
s-AES
TUD
NORSEX
CalVal
Bootstra
p F (C
F)
One
chan
nel (6
H)
IOM
ASA IRT
0
5
10
15
20
25
30
35
0.04
0.05
0.06
0.07
0.08
0.09 0.1
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19 0.2
0.21
0.22
0.23
0.24
0.25
0.26
0.27
0.28
0.29
0.0
0.2
0.4
0.6
0.8
1.0
1.2
ASI
CF
Bristol
NT
NT2
N90
NORSEX
6H
TUD
AES
SIC(SMOS Ice Thickness)
Concentration of 100% 10 cm ice
10HV PR NT
37HV 6H
N90L
NORSEX CFAES
N90 ASI0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
10HV PR NT
37HV 6H
N90L
NORSEX CFAES
N90 ASI0
0.2
0.4
0.6
0.8
1
1.2
Concentration of 100% 20 cm ice
SIC vs (1-OW)OW is melt ponds + leads
Combination of melted snow/ice that causes overestimation and OW that does the opposite
Conclusions
• We select a relatively simple and linear algorithm (CF or OSISAF)
• We perform atmospheric correction to TBs to reduce atm noise
• We apply dynamic tie-points to accomodate residual sensor drift and seasonal cycle in signatures