scholarly journals Classification of Arctic Sea Ice Surface Types During the Melt Season in High-Resolution IceBridge Imagery

2019 ◽  
Author(s):  
Ellen Buckley ◽  
Sinéad Farrell ◽  
Kyle Duncan ◽  
Laurence Connor ◽  
John Kuhn ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Surface ◽  
Arctic Sea

scholarly journals Classification of Arctic Sea Ice Surface Types During the Melt Season in High-Resolution IceBridge Imagery

2019 ◽  
Author(s):  
Ellen Buckley ◽  
Sinéad Farrell ◽  
Kyle Duncan ◽  
Laurence Connor ◽  
John Kuhn ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Surface ◽  
Arctic Sea

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

2021 ◽  
Author(s):  
Julia Kaltenborn ◽  
Viviane Clay ◽  
Amy R. Macfarlane ◽  
Joshua Michael Lloyd King ◽  
Martin Schneebeli
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Snow Layer ◽  
Arctic Sea ◽  
Execution Speed

<p>Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.</p><p>Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.</p><p>We will test and compare the different algorithms and evaluate the algorithms’ effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.</p><p>The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms’ comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation. </p><p> </p><p>King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. <em>The Cryosphere</em>, <em>14</em>(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.</p>

scholarly journals Characterizing Arctic sea ice topography using high-resolution IceBridge data

2016 ◽  
Vol 10 (3) ◽  
pp. 1161-1179 ◽  
Author(s):  
Alek A. Petty ◽  
Michel C. Tsamados ◽  
Nathan T. Kurtz ◽  
Sinead L. Farrell ◽  
Thomas Newman ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Surface Feature ◽  
Ice Thickness ◽  
First Year ◽  
Data Set ◽  
Surface Features ◽  
Developed Surface ◽  
Arctic Sea

Abstract. We present an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009 to 2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes. The results demonstrate that Arctic sea ice topography exhibits significant spatial variability, mainly driven by the increased surface feature height and volume (per unit area) of the multi-year ice that dominates the Central Arctic region. The multi-year ice topography exhibits greater interannual variability compared to the first-year ice regimes, which dominates the total ice topography variability across both regions. The ice topography also shows a clear coastal dependency, with the feature height and volume increasing as a function of proximity to the nearest coastline, especially north of Greenland and the Canadian Archipelago. A strong correlation between ice topography and ice thickness (from the IceBridge sea ice product) is found, using a square-root relationship. The results allude to the importance of ice deformation variability in the total sea ice mass balance, and provide crucial information regarding the tail of the ice thickness distribution across the western Arctic. Future research priorities associated with this new data set are presented and discussed, especially in relation to calculations of atmospheric form drag.

scholarly journals Seasonal changes in Arctic sea-ice morphology

2001 ◽  
Vol 33 ◽  
pp. 171-176 ◽  
Author(s):  
Donald K. Perovich ◽  
Jacqueline A. Richter-Menge ◽  
Walter B. Tucker
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Surface ◽  
Arctic Sea ◽  
Dynamic Forcing ◽  
Break Up

AbstractThe morphology of the Arctic sea-ice cover undergoes large changes over an annual cycle. These changes have a significant impact on the heat budget of the ice cover, primarily by affecting the distribution of the solar radiation absorbed in the ice-ocean system. In spring, the ice is snow-covered and ridges are the prominent features. The pack consists of large angular floes, with a small amount of open water contained primarily in linear leads. By the end of summer the ice cover has undergone a major transformation. The snow cover is gone, many of the ridges have been reduced to hummocks and the ice surface is mottled with melt ponds. One surface characteristic that changes little during the summer is the appearance of the bare ice, which remains white despite significant melting. The large floes have broken into a mosaic of smaller, rounded floes surrounded by a lace of open water. Interestingly, this break-up occurs during summer when the dynamic forcing and the internal ice stress are small During the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment we had an opportunity to observe the break-up process both on a small scale from the ice surface, and on a larger scale via aerial photographs. Floe break-up resulted in large part from thermal deterioration of the ice. The large floes of spring are riddled with cracks and leads that formed and froze during fall, winter and spring. These features melt open during summer, weakening the ice so that modest dynamic forcing can break apart the large floes into many fragments. Associated with this break-up is an increase in the number of floes, a decrease in the size of floes, an increase in floe perimeter and an increase in the area of open water.

High resolution simulations of Arctic sea ice, 1979?1993

2003 ◽  
Vol 22 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Wieslaw Maslowski ◽  
William H. Lipscomb
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea

scholarly journals Meltwater sources and sinks for multiyear Arctic sea ice in summer

2021 ◽  
Vol 15 (9) ◽  
pp. 4517-4525
Author(s):  
Don Perovich ◽  
Madison Smith ◽  
Bonnie Light ◽  
Melinda Webster
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Arctic Sea Ice ◽  
Sources And Sinks ◽  
Ice Melt ◽  
Ice Surface ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Ice Mass Balance ◽  
Surface Heat Budget

Abstract. On Arctic sea ice, the melt of snow and sea ice generate a summertime flux of fresh water to the upper ocean. The partitioning of this meltwater to storage in melt ponds and deposition in the ocean has consequences for the surface heat budget, the sea ice mass balance, and primary productivity. Synthesizing results from the 1997–1998 SHEBA field experiment, we calculate the sources and sinks of meltwater produced on a multiyear floe during summer melt. The total meltwater input to the system from snowmelt, ice melt, and precipitation from 1 June to 9 August was equivalent to a layer of water 80 cm thick over the ice-covered and open ocean. A total of 85 % of this meltwater was deposited in the ocean, and only 15 % of this meltwater was stored in ponds. The cumulative contributions of meltwater input to the ocean from drainage from the ice surface and bottom melting were roughly equal.

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