The classification of Arctic sea ice types and the determination of surface temperature using advanced very high resolution radiometer data

1994 ◽  
Vol 99 (C3) ◽  
pp. 5201 ◽  
Author(s):  
Robert Massom ◽  
Josefino C. Comiso
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Surface Temperature ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Very High

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

scholarly journals Determination of surface turbulent fluxes over leads in Arctic sea ice

1997 ◽  
Vol 102 (C2) ◽  
pp. 3331-3343 ◽  
Author(s):  
Afshan Alam ◽  
Judith A. Curry
Keyword(s):  
Sea Ice ◽  
Turbulent Fluxes ◽  
Arctic Sea Ice ◽  
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 Direct determination of the air-sea CO2 gas transfer velocity in Arctic sea ice regions

2017 ◽  
Vol 44 (8) ◽  
pp. 3770-3778 ◽  
Author(s):  
John Prytherch ◽  
Ian M. Brooks ◽  
Patrick M. Crill ◽  
Brett F. Thornton ◽  
Dominic J. Salisbury ◽  
...  
Keyword(s):  
Sea Ice ◽  
Direct Determination ◽  
Arctic Sea Ice ◽  
Gas Transfer ◽  
Transfer Velocity ◽  
Co2 Gas ◽  
Gas Transfer Velocity ◽  
Arctic Sea
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