the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A sea ice deformation and rotation rates dataset (2017–2023) from the Environment and Climate Change Canada Automated Sea Ice Tracking System (ECCC-ASITS)
Abstract. Sea ice forms a thin but horizontally extensive boundary between the ocean and the atmosphere, with a complex crust-like dynamics characterized by intermittent sea ice deformations. The heterogeneity and localisation of these sea ice deformations are important characteristics of the sea ice cover that can be used to evaluate the performance of dynamical sea-ice models against observations across multiple spatial and temporal scales. Here, we present a new pan-Arctic sea-ice deformation and rotation rates (SIDRR) dataset derived from the RADARSAT Constellation Mission (RCM) and Sentinel-1 (S1) synthetic aperture radar (SAR) imagery from 01 September 2017 to 31 August 2023. The SIDRR estimates are derived from contour integrals of triangulated ice motion data, obtained from the Environment and Climate Change Canada automated sea ice tracking system (ECCC-ASITS). The SIDRR dataset is not regularised, and consist in stacked data from multiple SAR images computed on a range of temporal (0.5–6 days) and spatial (4–10 km) scales. It covers the entire Arctic Ocean and all peripheral seas except the Okhotsk Sea. Uncertainties associated with the propagation of tracking errors on the deformation values are included. We show that rectangular patterns of deformation features are visible when the sampled deformation rates are lower than the propagation error. This limits the meaningful information the can be extracted in areas with low SIDRR values, but allows for the characterisation of SIDRR in Linear Kinematic Features. The spatial coverage and range of resolutions of the SIDRR dataset provides an interesting opportunity to investigate regional and seasonal variability of sea-ice deformation statistics across scales, and can be used to determine metrics for model evaluation.
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Status: open (until 17 Oct 2024)
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RC1: 'Comment on essd-2024-227', Anton Korosov, 13 Sep 2024
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The manuscript describes the methodology and the dataset of sea ice deformation and rotation derived from S1 and RCN-based sea ice drift. It is very well written and provides all the necessary information for users of the new dataset. Aspects of deformation computation and uncertainty estimates are covered in good detail. Artefacts appearing on ice deformation maps due to the coarse resolution of SAR data used for ice drift retrieval are well highlighted.
It is an excellent work, and I have only two minor comments on the manuscript's text. However, I have a major comment on the provided data. I recommend updating the dataset before the manuscript can be published, and I provide detailed instructions on how it can be done efficiently.
Minor comments on the text
Line 61. The phrase "combination of S1 and RCM SAR" reads as ice drift was derived from pairs containing S1 and RCM images. However, that was not the case. S1 was paired with S1 and RCM with RCM. Please rephrase for clarity.
Line 265: 'will BE assessed'
Major comment on the dataset
The dataset's structure is such that start/end coordinates of all three nodes of an element are provided for each deformation value. Therefore, the coordinates are duplicated for two neighbour elements. Therefore, the same coordinates are provided (duplicated) as many times as a node is shared by neighbour elements. On average, the dataset size is three -- four times larger than it should be. Moreover, this structure is not convenient to use for plotting and analysis.
For illustration purposes, let's consider two elements (triangles) with four nodes (two nodes are shared). Then, the current data structure can be presented as:
element_index = [1, 2]
longitudes_corner1 = [lon1, lon2]
longitudes_corner2 = [lon2, lon4]
longitudes_corner3 = [lon3, lon3]
Here the number with lon inside the longitude vector indicates the node index.
It is clear that values of lon2 and lon3 are stored in the dataset twice. The same happens with all start / end longitudes and latitudes and with indices of elements.
I propose to use a more efficient data structure instead:
longitudes = [lon1, lon2, lon3, lon4]
triangulation = [[1, 2, 3], [2, 3, 4]]
Longitudes is a vector with unique values of coordinates for each node.
Triangulation is a 3 x N array with indices of corners for each element.
Transformation of data from the current structure to the new structure can be efficiently performed in a few lines of code. Here I provide just a very brief snippet using Python:
# stack indices of all nodes for one image pair
ids = np.hstack([ids1, ids2, ids3])
# find unique indices and inversion rule_, unq_idx, unq_inv = np.unique(ids, return_index=True, return_inverse=True)# reduce the data (keep only unique values of coordinates)start_lat = np.hstack([start_lat1, start_lat2, start_lat3])[unq_idx]# create triangulation from the inversion ruletri = unq_inv.reshape(3, -1).TDetailed explanations are provided in the notebook (see also the corresponding pull request to the McGill-sea-ice/SIDRRpy repository):https://github.com/akorosov/SIDRRpy/blob/main/optimize_data_structure.ipynbAs exemplified in the notebook, providing a ready-to-use triangulation also facilitates plotting (and further analysis of deformation fields):plt.tripcolor(start_lon, start_lat, shr, triangles=tri)Citation: https://doi.org/10.5194/essd-2024-227-RC1
Data sets
Sea ice deformation and rotation rates (SIDRR) from the ECCC-ASITS Mathieu Plante, Jean-François Lemieux, L. Bruno Tremblay, Amélie Bouchat, Damien Ringeisen, Philippe Blain, Stephen Howell, Mike Brady, Alexander S. Komarov, Béatrice Duval, Lekima Yakuden, and Frédérique Labelle https://doi.org/10.5281/zenodo.11520803
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