Helicopter-borne measurements of sea ice thickness, using a small and lightweight, digital EM system

Abstract Ice thickness is one of the most critical physical indicators in the ice science and engineering. It is therefore very necessary to develop in-situ automatic observation technologies of ice thickness. This paper proposes the principle of three new technologies of in-situ automatic observations of sea ice thickness and provides the findings of laboratory applications. The results show that the in-situ observation accuracy of the monitor apparatus based on the Magnetostrictive Delay Line (MDL) principle can reach ±2 mm, which has solved the “bottleneck” problem of restricting the fine development of a sea ice thermodynamic model, and the resistance accuracy of monitor apparatus with temperature gradient can reach the centimeter level and research the ice and snow substance balance by automatically measuring the glacier surface ice and snow change. The measurement accuracy of the capacitive sensor for ice thickness can also reach ±4 mm and the capacitive sensor is of the potential for automatic monitoring the water level under the ice and the ice formation and development process in water. Such three new technologies can meet different needs of fixed-point ice thickness observation and realize the simultaneous measurement in order to accurately judge the ice thickness.

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Study on the sea ice thickness observation in the sea of okhotsk by using dual-frequency and fully polarimetric airborne SAR (Pi-SAR) data

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 ◽

10.1109/igarss.2004.1370121 ◽

2004 ◽

Author(s):

K. Nakamura ◽

H. Wakabayashi ◽

M. Nakayama ◽

K. Naoki ◽

T. Toyota ◽

...

Keyword(s):

Sea Ice ◽

Sea Of Okhotsk ◽

Ice Thickness ◽

Dual Frequency ◽

Sea Ice Thickness ◽

The Sea Of Okhotsk ◽

Sar Data ◽

Airborne Sar

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Assimilation of SMOS sea ice thickness in the regional ice prediction system

International Journal of Remote Sensing ◽

10.1080/01431161.2021.1897183 ◽

2021 ◽

Vol 42 (12) ◽

pp. 4583-4606

Author(s):

Mukesh Gupta ◽

Alain Caya ◽

Mark Buehner

Keyword(s):

Sea Ice ◽

Ice Thickness ◽

Prediction System ◽

Sea Ice Thickness

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Antarctic sea-ice thickness and volume estimates from ice charts between 1995 and 1998

Annals of Glaciology ◽

10.3189/2015aog69a763 ◽

2015 ◽

Vol 56 (69) ◽

pp. 383-393 ◽

Cited By ~ 3

Author(s):

E. Rachel Bernstein ◽

Cathleen A. Geiger ◽

Tracy L. Deliberty ◽

Mary D. Lemcke-Stampone

Keyword(s):

Sea Ice ◽

Regional Scale ◽

Weighted Average ◽

Ice Thickness ◽

Average Thickness ◽

Sea Ice Thickness ◽

Subjective Analysis ◽

Stage Of Development ◽

Volume Estimates ◽

The Impact

AbstractThis work evaluates two distinct calculations of central tendency for sea-ice thickness and quantifies the impact such calculations have on ice volume for the Southern Ocean. The first calculation, area-weighted average thickness, is computed from polygonal ice features and then upscaled to regions. The second calculation, integrated thickness, is a measure of the central value of thickness categories tracked across different scales and subsequently summed to chosen regions. Both methods yield the same result from one scale to the next, but subsequent scales develop diverging solutions when distributions are strongly non-Gaussian. Data for this evaluation are sea-ice stage-of-development records from US National Ice Center ice charts from 1995 to 1998, as proxy records of ice thickness. Results show regionally integrated thickness exceeds area-weighted average thickness by as much as 60% in summer with as few as five bins in thickness distribution. Year-round, the difference between the two calculations yields volume differences consistently >10%. The largest discrepancies arise due to bimodal distributions which are common in ice charts based on current subjective-analysis protocols. We recommend that integrated distribution be used for regional-scale sea-ice thickness and volume estimates from ice charts and encourage similar testing of other large-scale thickness data archives.

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The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

10.5194/egusphere-egu21-12240 ◽

2021 ◽

Author(s):

Isolde Glissenaar ◽

Jack Landy ◽

Alek Petty ◽

Nathan Kurtz ◽

Julienne Stroeve

Keyword(s):

Sea Ice ◽

Snow Depth ◽

Satellite Altimetry ◽

Region Of Interest ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Baffin Bay ◽

The Impact

The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.

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Interannual sea ice thickness variability in the Bay of Bothnia

The Cryosphere ◽

10.5194/tc-12-3459-2018 ◽

2018 ◽

Vol 12 (11) ◽

pp. 3459-3476 ◽

Cited By ~ 5

Author(s):

Iina Ronkainen ◽

Jonni Lehtiranta ◽

Mikko Lensu ◽

Eero Rinne ◽

Jari Haapala ◽

...

Keyword(s):

Sea Ice ◽

Interannual Variability ◽

Thickness Distribution ◽

Ice Thickness ◽

Data Sets ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Thickness Variability ◽

Fast Ice ◽

Ridged Ice

Abstract. While variations of Baltic Sea ice extent and thickness have been extensively studied, there is little information about drift ice thickness, distribution, and its variability. In our study, we quantify the interannual variability of sea ice thickness in the Bay of Bothnia during the years 2003–2016. We use various different data sets: official ice charts, drilling data from the regular monitoring stations in the coastal fast ice zone, and helicopter and shipborne electromagnetic soundings. We analyze the different data sets and compare them to each other to characterize the interannual variability, to discuss the ratio of level and deformed ice, and to derive ice thickness distributions in the drift ice zone. In the fast ice zone the average ice thickness is 0.58±0.13 m. Deformed ice increases the variability of ice conditions in the drift ice zone, where the average ice thickness is 0.92±0.33 m. On average, the fraction of deformed ice is 50 % to 70 % of the total volume. In heavily ridged ice regions near the coast, mean ice thickness is approximately half a meter thicker than that of pure thermodynamically grown fast ice. Drift ice exhibits larger interannual variability than fast ice.

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Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter

Journal of Geophysical Research Oceans ◽

10.1002/2014jc009963 ◽

2014 ◽

Vol 119 (10) ◽

pp. 6680-6692 ◽

Cited By ~ 37

Author(s):

Qinghua Yang ◽

Svetlana N. Losa ◽

Martin Losch ◽

Xiangshan Tian-Kunze ◽

Lars Nerger ◽

...

Keyword(s):

Sea Ice ◽

Ocean Model ◽

Ice Thickness ◽

Sea Ice Thickness

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Characteristics of Sea-ice thickness and Snow-depth distributions of the Summer landfast ice in Lützow-Holm Bay, East Antarctica

Annals of Glaciology ◽

10.3189/172756406781811240 ◽

2006 ◽

Vol 44 ◽

pp. 281-287 ◽

Cited By ~ 9

Author(s):

Shotaro Uto ◽

Haruhito Shimoda ◽

Shuki Ushio

Keyword(s):

Sea Ice ◽

Interannual Variability ◽

Snow Depth ◽

East Antarctica ◽

Ice Thickness ◽

First Year ◽

Total Thickness ◽

Sea Ice Thickness ◽

Landfast Ice ◽

Northeasterly Wind

AbstractSea-ice observations have been conducted on board icebreaker shirase as a part of the Scientific programs of the Japanese Antarctic Research Expedition. We Summarize these to investigate Spatial and interannual variability of ice thickness and Snow depth of the Summer landfast ice in Lützow-Holm Bay, East Antarctica. Electromagnetic–inductive observations, which have been conducted Since 2000, provide total thickness distributions with high Spatial resolution. A clear discontinuity, which Separates thin first-year ice from thick multi-year ice, was observed in the total thickness distributions in two voyages. Comparison with Satellite images revealed that Such phenomena reflected the past breakup of the landfast ice. Within 20–30km from the Shore, total thickness as well as Snow depth decrease toward the Shore. This is due to the Snowdrift by the Strong northeasterly wind. Video observations of Sea-ice thickness and Snow depth were conducted on 11 voyages Since December 1987. Probability density functions derived from total thickness distributions in each year are categorized into three types: a thin-ice, thick-ice and intermediate type. Such interannual variability primarily depends on the extent and duration of the Successive break-up events.

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