Estimation of thin Sea-ice thickness from NOAA AVHRR data in a polynya off the Wilkes Land coast, East Antarctica

Tamura, Takeshi; Ohshima, Κay I.; Enomoto, Hiroyuki; Tateyama, Kazuyoshi; Muto, Atsuhiro; Ushio, Shuki; Massom, Robert A.

doi:10.3189/172756406781811745

Cited by 23 publications

(20 citation statements)

References 22 publications

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“…The relationship between SSM/I PR values and thin ice thicknesses may have a hemispheric or latitudinal bias due to differences in atmospheric conditions. Tamura et al (2006) examined the relationship between the PR values and the AVHRR thermal ice thicknesses in an Antarctic coastal polynya, where ice thickness data from in situ measurements were simul- Although the data are limited and the standard deviations are not small, the results of the present study are consistent with those of Tamura et al (2006).…”

Section: A Estimation Of Thin Ice Thicknesssupporting

confidence: 90%

“…Their estimation is applicable to sea ice with thicknesses Ͻ0.5 m (Yu and Rothrock 1996). Tamura et al (2006) confirmed the validity of the method by using ice thickness data obtained from in situ observations in an Antarctic coastal polynya. The present study follows their method.…”

Section: Datasupporting

confidence: 53%

“…At present, we have no ice thickness data on scales comparable to SSM/I grid cells. Surface temperature data from AVHRR with a spatial resolution of 1-4 km possibly can provide ice thickness estimation through heat flux calculations (Yu and Rothrock 1996;Drucker et al 2003;Tamura et al 2006). In this study, we use ice thickness information estimated from AVHRR data for comparison with and validation of the SSM/I retrievals.…”

Section: Introductionmentioning

confidence: 98%

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Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the Antarctic Ocean

Tamura

Ohshima

Markus

et al. 2007

Journal of Atmospheric and Oceanic Technology

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107

126

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Antarctic coastal polynyas are important areas of high sea ice production and dense water formation, and thus their detection including an estimate of thin ice thickness is essential. In this paper, the authors propose an algorithm that estimates thin ice thickness and detects fast ice using Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) data in the Antarctic Ocean. Detection and estimation of sea ice thicknesses of Ͻ0.2 m are based on the SSM/I 85-and 37-GHz polarization ratios (PR 85 and PR 37 ) through a comparison with sea ice thicknesses estimated from the Advanced Very High Resolution Radiometer (AVHRR) data. The exclusion of data affected by atmospheric water vapor is discussed. Because thin ice and fast ice (specifically ice shelves, glacier tongues, icebergs, and landfast ice) have similar PR signatures, a scheme was developed to separate these two surface types before the application of the thin ice algorithm to coastal polynyas. The probability that the algorithm correctly distinguishes thin ice from thick ice and from fast ice is ϳ95%, relative to the ice thicknesses estimated from AVHRR. Although the standard deviation of the difference between the thin ice thicknesses estimated from the SSM/I algorithm and AVHRR is ϳ0.05 m and thus not small, the estimated ice thicknesses from the microwave algorithm appear to have small biases and the accuracies are independent of region and season. A distribution map of thin ice occurrences derived from the SSM/I algorithm represents the Ross Sea coastal polynya being by far the largest among the Antarctic coastal polynyas; the Weddell Sea coastal polynyas are much smaller. Along the coast of East Antarctica, coastal polynyas frequently form on the western side of peninsulas and glacier tongues, downstream of the Antarctic Coastal Current.

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Section: A Estimation Of Thin Ice Thicknesssupporting

confidence: 90%

Section: Datasupporting

confidence: 53%

Section: Introductionmentioning

confidence: 98%

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Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the Antarctic Ocean

Tamura

Ohshima

Markus

et al. 2007

Journal of Atmospheric and Oceanic Technology

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107

126

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“…We assume that the heat flux between ice and atmosphere is a sum of net longwave radiation and turbulent heat flux. The similar ice thickness estimations using AVHRR data were made in the Beaufort Sea, Greenland Sea, Bering Sea, and off East Antarctica [ Yu and Rothrock , 1996; Drucker et al , 2003; Tamura et al , 2006], and these studies showed that the method can estimate ice thickness within the error of ±0.05 m from comparisons with in situ observed ice thickness. Figure 2b shows the AVHRR ice thickness.…”

Section: Amsr‐e Thin Ice Thickness Algorithmmentioning

confidence: 66%

Thickness and production of sea ice in the Okhotsk Sea coastal polynyas from AMSR‐E

et al. 2009

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[1] From comparisons with thickness of sea ice from Advanced Very High Resolution Radiometer (AVHRR) and ice-profiling sonar data we have developed an Advanced Microwave Scanning Radiometer-EOS (AMSR-E) thin ice thickness algorithm for the Sea of Okhotsk. This algorithm can estimate ice thickness of 0.2 m without snow using the polarization ratio of AMSR-E brightness temperature at a 36.5 GHz channel from a linear relationship with AVHRR ice thickness. When a snow cover exists on the thin ice surface, as occurred a few times in each winter, it is shown that the algorithm cannot detect the thin ice. Sea ice and dense shelf water (DSW) production in coastal polynya are estimated on the basis of heat flux calculation with the daily AMSR-E ice thickness for three winters (December-March) of 2002-2003 to 2004-2005. The ice production is largest in the northwest shelf (NWS) polynya which accounts for $45% of the sum of ice production in major coastal polynyas. The ice production in major coastal polynyas would cover the maximum ice area of the Okhotsk Sea if the average ice thickness is assumed to be 1 m. Variability of the ice production is mainly modulated by air temperature. In the NWS polynya, which is the main DSW production area, the annual DSW formation rate is estimated to be $0.36 Sv.

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“…For PR36 of 0.057-0.083 (h36 of 10-20 cm), the RMSD for all polynyas is 5.8 cm, and the biases (hi minus h36) range from -3.6 to 1.6 cm among the three polynyas. As in the case of the AMSR-E thin ice thickness algorithm [19], the values of these deviations and biases are generally within the accuracy of hi (about ±5 cm) determined by comparisons with in situ observations [35], [46], [50]. Therefore, we used (3) and (4) as a unified algorithm for the estimation of thin ice thickness from AMSR2 data over the entire Antarctic Ocean.…”

Section: A a Thin Ice Thickness Algorithmmentioning

confidence: 81%

Sea-Ice Production in Antarctic Coastal Polynyas Estimated From AMSR2 Data and Its Validation Using AMSR-E and SSM/I-SSMIS Data

Nihashi

Ohshima

Tamura

2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Abstract-Antarctic coastal polynyas are very high sea-ice production areas. The resultant large amount of brine rejection leads to the formation of dense water. The dense water forms Antarctic Bottom Water, which is the densest water in the global overturning circulation and a key player in climate change as a significant sink for heat and carbon dioxide. In this study, an algorithm was developed that uses AMSR2 data (2012-present) to detect polynya area and estimate thin ice thickness by a method similar to that used to develop the algorithm for AMSR-E data. Landfast sea-ice areas were also detected using AMSR2 data. Ice production in the polynyas was estimated by a heat flux calculation using AMSR2 sea-ice data. In four major polynyas, AMSR2 ice production was compared with AMSR-E (2003-2011) ice production through comparison of them with SSM/I-SSMIS ice production. The comparison confirmed that the ice production from AMSR-E/2 data, which have higher spatial resolution than SSM/I-SSMIS data, can be used to analyze time series covering more than 10 years. For example, maps of annual ice production based on AMSR-E/2 data revealed detailed changes of the Mertz Polynya, where the ice production decreased significantly after the Mertz Glacier Tongue calving in 2010. Continuous monitoring of the coastal polynyas by the AMSR series sensors is essential for climate-change-related analyses in the Antarctic Ocean.

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Estimation of thin Sea-ice thickness from NOAA AVHRR data in a polynya off the Wilkes Land coast, East Antarctica

Cited by 23 publications

References 22 publications

Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the Antarctic Ocean

Estimation of Thin Ice Thickness and Detection of Fast Ice from SSM/I Data in the Antarctic Ocean

Thickness and production of sea ice in the Okhotsk Sea coastal polynyas from AMSR‐E

Sea-Ice Production in Antarctic Coastal Polynyas Estimated From AMSR2 Data and Its Validation Using AMSR-E and SSM/I-SSMIS Data

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