North and South Poles

<p>This comprehensive dataset combines all-sky auroral observations with high-resolution small-scale auroral data and is a resource for space weather research, magnetospheric studies and atmospheric science. The captured all-sky dataset classifies auroral displays into four different morphological types: arcs (smooth, curtain-like structures), folds (folded, undulating structures), radial corona (ray projections from a central point) and hotspots (locally bright regions).
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Greenland Ice Sheet is one of the key factors influencing global climate change. Its slight variations can lead to significant changes in sea level, making quantitative research on its mass balance of great scientific importance.
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Greenland Ice Sheet is one of the key factors influencing global climate change. Its slight variations can lead to significant changes in sea level, making quantitative research on its mass balance of great scientific importance.
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Greenland Ice Sheet is one of the key factors influencing global climate change. Its slight variations can lead to significant changes in sea level, making quantitative research on its mass balance of great scientific importance.
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output mat: 60-day SR-SICFormer forecasting data
label mat: 60-day remote sensing ground truth
nan_mat: land-sea mask
maks: lat-lon mask
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SeaIceWeather Dataset
This is the SeaIceWeather dataset, collected for training and evaluation of deep learning based de-weathering models. To the best of our knowledge, this is the first such publicly available dataset for the sea ice domain. This dataset is linked to our paper titled: Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images. The paper can be accessed at: https://doi.org/10.1109/jsen.2024.3376518
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We present a finite-element-based cohesive zone model for simulating the nonlinear fracture process driving the propagation of water-filled surface crevasses in floating ice tongues. The fracture process is captured using an interface element whose constitutive behavior is described by a bilinear cohesive law, and the bulk rheology of ice is described by a nonlinear elasto-viscoplastic model. The additional loading due to meltwater pressure within the crevasse is incorporated by combining the ideas of poromechanics and damage mechanics.
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SI-STSAR-7 is a labeled spatiotemporal dataset for sea ice classification based on SAR images. The dataset is produced from 80 Sentinel-1 A/B SAR scenes during the two freezing periods of Hudson Bay from October 2019 to May 2020 and from October 2020 to April 2021, which are provided by the Copernicus Open Access Center. The Sentinel-1 SAR images were preprocessed with noise reduction and incidence angle dependence correction before use.
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