SSM/I Sea Ice Concentrations in the

Marginal Ice ZoneA Comparison of Four Algorithms with

AVHRR Imagerysubmitted to IEEE Trans. Geosci. and Rem.

Sensing4 June 2004

Walt MeierNSIDC/CIRES

Research Scientist

Motivation

• Most previous algorithm comparisons have involved isolated case studies (a few days)

• Comparisons have involved one or two algorithms

• Comparisons often encompass primarily regions of compact ice where errors are expected to be smallest

This Study• Large scale independent comparison of SSM/I ice

concentration algorithms– Four algorithms– Several days– Winter and summer– Three regions

• Focus on marginal/seasonal ice zone– Region of operational interest– High small-scale variability both in space and time– Region of large seasonal and interannual variability– Algorithms have most difficulty in such regions– Models of air-sea exchange most sensitive in such areas

Barents

E. Greenland

Baffin

Map of Study Regions

AVHRR Imagery

• Images with considerable cloud free areas collected over one year period– June 2001 – August 2001– November 2001 – March 2002

• Images collected from Eastern Arctic– Barents Sea– Baffin Bay– East Greenland sea

• 2.5 km resolution on NSIDC polar stereographic grid

• >750 total scenes collected; 48 used in study

AVHRR Concentration: Summer

• Mixing Method• June 2001 – August 2001• Assume Channel 2 (0.72-1.10 m) albedo reflects

amount of ice present in a pixel• Tiepoints defined for 100% ice and 100% water• Ice concentration derived from linear interpolation

between tiepoints• Tiepoints determined locally in each image

– Account for changes in sun and satellite angle and local ice changes

• Similar methodology used in several past comparisons, e.g.: Comiso and Steffen, 2001,Zibordi et al., 1995, Emery et al., 1991, Steffen and Schweiger, 1990

AVHRR Concentration: Winter• Threshold Method• December 2001 – March 2002• Assume surface temperature is below freezing, thus

ice is continually forming• Channel 4 (10.3-11.3 m) brightness temperature

indicates if ice is present in pixel or not• Ice/water threshold temperature (~271 K) defined

– If Tb > threshold, Concentration = 100%– If Tb < threshold, Concentration =0%

• Threshold chosen locally within each individual AVHRR image

• Similar methodology used in several past comparisons, e.g.: Comiso and Steffen, 2001, Zibordi et al., 1995, Emery et al., 1991, Steffen and Schweiger, 1990

SSM/I Concentration Fields

• 25 km fields on NSIDC polar stereographic grid– Algorithms run on 24-hour composite brightness

temperature fields acquired from NOAA at the National Ice Center

• Subsampled to same region as AVHRR images

• Rebinned (no interpretation) to same 2.5 km resolution as AVHRR for pixel-to-pixel comparison

• Weather filters used to eliminate false ice signals over open water (same filters used for all algorithms)

SSM/I Algorithms

• Bootstrap (BT): 19V, 19H, 37V– e.g., Comiso et al., 1997

• Cal/Val (CV): 19V, 37V (37V, H near ice edge)– e.g., Ramseier et al., 1988

• NASA Team (NT): 19V, 19H, 37V– e.g., Cavalieri et al., 1984

• NASA Team 2 (N2): 19V, 19H, 37V, 85V, 85H– Markus and Cavalieri, 2000

NASA Team 2

• Newest algorithm• Uses 85 GHz channels in addition to

standard 19 and 37 GHz channels– 85 GHz susceptible to atmosphere– N2 uses inverse radiative transfer model to

find ‘best-fit’ of 11 standard atmospheres– Atmosphere subtracted out from Tb signal– 85 GHz more sensitive to surface

inhomogeneities potentially more accurate if no atmospheric problems

• Standard algorithm for AMSR-E in the Arctic

SSM/I – AVHRR DifferenceTotal BT CV N2 NT13897pixels

Mean -5.3 1.8 -1.2 -9.0

St. Dev.

12.9 13.9 13.7 14.6

Summer BT CV N2 NT4125pixels

Mean -6.1 -4.3 -2.6 -10.5

St. Dev.

14.6 16.9 15.7 15.9

Winter BT CV N2 NT9772pixels

Mean -5.0 0.7 -0.6 -8.4

St. Dev.

12.2 12.3 12.7 13.9

Values in yellow are the lowest difference or are within 95% confidence level of lowest difference.

SSM/I-AVHRRMean

Differences

Differences for each case (numbered on x-axis) for each season.

Error bars indicate 95% confidence levels.

Summer

Winter%

D

iffere

nce

%

Diff

ere

nce

SSM/I-AVHRRSt. Dev.

Differences

Differences for each case (numbered on x-axis) for each season.

Error bars indicate 95% confidence levels.

Summer

Winter%

D

iffere

nce

%

Diff

ere

nce

Case Study

Barents Sea17 June 2001

0 20 40 60 80 100%

BT72%

NT68%

CV81%

N274%

AV79%

0 20 40 60 80 100%

0 20 40 60 80 100%

AV BT CV N2 NT

99.6% 99.2% 100.0% 97.1% 89.8%

0 20 40 60 80 100%

Case Study

E. Greenland Sea27 February 2002

0 20 40 60 80 100%

AV BT CV N2 NT

96% 86% 93% 94% 83%

0 20 40 60 80 100%

Clouds• Previous comparison limited to clear sky

regions• Clouds prevalent

– Over 8 months of images in the three regions (~750 total)

– <60 had enough clear sky to make comparisons• Algorithms likely do not perform as well under

thick clouds, particularly N2• To investigate potential effects of clouds, a

regional case study was conducted– Meier, W.N., T. Maksym, and M.L. Van Woert, Evaluation of Arctic operational

passive microwave products: A case study in the Barents Sea during October 2001, Proc. 16th Int’l Symposium on Ice, Dunedin, NZ, 2-6 Dec 2002, pp. 213-222.

– Barents Sea, October 2001 – USCGC Healy cruise– SSM/I concentrations compared with Radarsat

imagery– N2 did not show any noticeable degradation

1 Oct. BS CV

N2 NT

NSSM/I Contour Intervals

• 5%• 15%• 50%• 90%

© CSA 2001

OLS

Underestimates ice edge

11 Oct BS CV

N2 NT

SSM/I Contour Intervals

• 5%• 15%• 50%• 90%

© CSA 2001

OLS

Misses ice

Captures lower concentration

Conclusions• Performance of algorithms varies depending

on season, ice conditions, etc.– Overall, NASA Team 2 and Bootstrap have lowest

differences from AVHRR• N2 tends to have lowest bias• Bootstrap tends to have lowest difference SD

– Cal/Val tends to overestimate concentration due to saturation to 100% concentration, especially in summer

– NT is inferior algorithm in most situations

• Algorithms yield similar difference SD values, due at least in part to low resolution of sensor no matter what algorithm is used, resolution limits the effectiveness of SSM/I

Acknowledgements

• Canadian Space Agency for Radarsat imagery

• DMSP and NOAA for OLS and SSM/I data

• Søren Anderson, Danish Meteorology Institute, for AVHRR data

• Midshipman Nathan Bastar for initial analysis