HR: 0800h
AN: C11B-0426    [Abstracts]
TI: Evaluating derived sea ice thickness estimates from two remote sensing data sets
AU: Ballagh, L M
EM: vtlisa@nsidc.org
AF: National Snow and Ice Data Center, CIRES, University of Colorado, Boulder, 449 UCB, Boulder, CO 80309, United States
AU: * Meier, W N
EM: walt@nsidc.org
AF: National Snow and Ice Data Center, CIRES, University of Colorado, Boulder, 449 UCB, Boulder, CO 80309, United States
AU: Barry, R G
EM: rbarry@nsidc.org
AF: National Snow and Ice Data Center, CIRES, University of Colorado, Boulder, 449 UCB, Boulder, CO 80309, United States
AU: Buttenfield, B P
EM: babs@colorado.edu
AF: Department of Geography, University of Colorado, Boulder, 260 UCB, Boulder, CO 80309- 0260, United States
AB: Satellites that monitor the polar regions collect a wealth of information about sea ice. While elevation data (ice freeboard + snow) are obtainable from certain satellites (e.g. ICESat), this estimate only measures approximately 10 percent of the total ice thickness. Significant uncertainties exist when extrapolating from ice freeboard to total ice thickness. Direct ice thickness measurements taken from on the ice are the most accurate but difficult to obtain, but submarines provide an effective method to monitor basin-wide draft. There is less uncertainty in extrapolating total thickness from the ice draft. Satellite imagery, while not providing direct measurement of ice thickness, can be used to infer ice type and hence an ice thickness range estimate based on interpretation of the imagery and ancillary data. This study compares Arctic ice thickness estimated from submarine data to ice thickness from an interpretive product that relies heavily on satellite data where the two data sets overlap spatially and temporally during the period 1996 through 1998. The first source (submarine data) is available from the National Snow and Ice Data Center, while the second source of raw NIC charts is available from the National Ice Center (NIC). Both data sources are converted to ice thickness prior to the evaluation. A raw data analysis is performed and ice thickness distribution maps are produced in ArcMap based on the ordinary kriging spatial interpolation technique. Results from the raw data analysis indicate a low correlation between the two data sets with the least agreement in the multiyear ice zone (>200 cm). The geostatistical results portray a similar ice thickness pattern in the Arctic, even though the submarine data contain more ice thickness variability than the NIC charts.
DE: 0700 CRYOSPHERE (4540)
DE: 0750 Sea ice (4540)
DE: 0758 Remote sensing
SC: Cryosphere [C]
MN: 2007 Fall Meeting