ALBERT

Description: Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing and an active 5-class ice thickness distribution. The model data is sampled for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008 and covers the entire Arctic Ocean. A detailed description of the model configuration and the data-set is provided in: Hutter, N. and Losch, M.: Feature-based comparison of sea-ice deformation in lead-resolving sea-ice simulations, The Cryosphere, https://doi.org/10.5194/tc-2019-88, accepted for publication, 2019. A detailed description of the algorithms deriving the data set is provided in: Hutter, N., Zampieri, L., and Losch, M.: Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm, The Cryosphere, 13, 627-645, https://doi.org/10.5194/tc-13-627-2019, 2019.

Description: The Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition took place between October 2019 and September 2020 giving the rare opportunity to monitor sea-ice properties over a full annual cycle. Here we present 24 high-resolution orthomosaics and 14 photogrammetric digital elevation models of the sea-ice surface around the icebreaker RV Polarstern between March and September 2020. The dataset is based on 〉34.000 images acquired by a helicopter-borne optical camera system with survey flights covering areas between 1.8 and 96.5 km^2 around the vessel. Depending on the flight pattern and altitude of the helicopter, ground resolutions of the orthomosaics range between 0.03 and 0.5 m. By combining the photogrammetric products with contemporaneously acquired airborne laser scanner reflectance measurements selected orthomosaics could be corrected for cloud shadows which facilitates their usage for sea-ice and melt pond classification algorithms. The presented dataset is a valuable data source for the interdisciplinary MOSAiC community building a temporal and spatially resolved baseline to accompany various remote sensing and in situ research projects. Central Observatory (CO) grid flights are available as single grids while transect and triangle flights were segmented into 2 km x 2 km data tiles to provide the user with manageable file sizes. Next to the orthomosaic and DEM data we provide confidence maps of the respective survey flights. All data is stored in GeoTIFF file format and gridded to 0.5 m spatial resolution. For the CO grid flights we also provide orthomosaic data at full spatial resolution within a 3 km square centered at Polarstern. The naming convention of the final data products is: Date, #Flight, DShip ID, followed by DEM, confidence or orthomosaic, and hr for high resolution (0.5 m) or fr for full resolution. As we provide brightness corrected orthomosaics these are termed l2 for level 2 products and if the data were corrected for the effect of cloud shadows we added a l2b product. All datasets were projected to a polar stereographic reference system centered at 45° W and vertically referenced to the Global Mean Sea Surface grid provided by DTU Space. The final datasets are stored in one zip archive per survey flight.

Description: Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation data obtained during the MOSAiC expedition from Sentinel-1 SAR data (von Albedyl & Hutter, 2023). The data-set spans the winter months between October 2019 and May 2020. A detailed description of the data-set and of the algorithms deriving it is provided in Hutter et al. (2019). We used the updated version of the algorithm for the data processing (Hutter, 2023). The dataset is closer described in Ringeisen et al. (2023).

Description: Divergent sea ice motion breaks the ice and opens fractures and leads. Depending on the air temperature, those open-water areas can quickly refreeze. The open water or thin ice in leads play a crucial role in the heat and gas exchange between the ocean and the atmosphere, impacting atmospheric, ecological, and oceanic processes. Leads can be detected from space, using different types of instruments, e.g., thermal infrared, passive microwave, active microwave, or optical sensors. The retrieval methods have different sensitivities, especially concerning the minimum lead width and the maximum ice thickness, different spatial resolutions, and different limits. We presented a time series of lead fractions from different lead products (Oct 2019 - May 2020) along the drift of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Transpolar Drift. We compared 7 different lead products based on 1. accumulated divergence derived from SAR images, 2. divergence in linear kinematic features, 3. classified SAR data, 4. thermal infrared data from MODIS, 5. passive microwave data from AMSR-2, 6. radar altimetry from CryoSat-2 (lead fractions and total lead count), and 7. thermal infrared data from helicopter surveys. We extracted daily lead fractions in a circle with a radius of 50 km along the drift of MOSAiC. Data is available from 5 October 2019 to 15 May 2020 with shorter time series for some of the sensors. We found that the mean lead fractions varied by 1 magnitude across different lead products due to different physical lead and sea ice properties observed by the sensors and methodological factors such as spatial resolution. Thus, the choice of lead product should align with the specific application. Each file contains time and lead fraction for a circular area (radius 50 km) around the MOSAiC position of the particular time stamp. The thermal infrared data from helicopter surveys are available from doi:10.1594/PANGAEA.951569.

Description: Sea ice deformation is a crucial process in the polar climate system and, thus, it is an important cross-cutting theme for all disciplines of the interdisciplinary research expedition MOSAiC. Because sea ice deformation is highly localized and intermittent, drift and deformation with a high spatial and temporal resolution and a large spatial coverage are required for a comprehensive description of the sea ice dynamics. We provide a regularly gridded, high-resolution drift and deformation dataset that can be used for several potential applications. Drift fields were obtained from Sentinel-1, HH polarization SAR images acquired in enhanced wide mode. These had a pixel resolution of 50 m in Polar Stereographic North projection (latitude of true scale: 70 N, center longitude: 45 W). We used an ice-tracking algorithm introduced by Thomas et al. (2008, 2011) and modified by Hollands and Dierking (2011) to derive drift from sequential pairs. Typically, the time between two scenes was one day, with a few exceptions of 2-3 days, and the size of the scenes was on average 200 x 200 km. Images are available for the entire study period, except for the time between 14 January and 15 March 2020, when the ship was north of the latitudinal coverage of the satellite. The resulting drift data set was defined on a regular grid with a spatial resolution of 700 m. Next, we calculate the spatial derivatives from the regularly spaced drift field following von Albedyll et al. (2021). Divergence, convergence, shear, and total deformation are then derived from the spatial derivatives of the velocity field. To reduce noise in the divergence fields, we filter the drift data with a directional filter that detects the direction with the smallest variation at each pixel and smooths along, but not across this orientation, with a 1-d kernel. The direction is chosen to minimize the standard deviation in a neighborhood of 7 pixels. This way, noise is reduced while preserving the strong gradients in the velocity field that are indicative of deformation. We provide the filtered divergence variable together with the unfiltered divergence values. For better distribution, the drift and deformation data were re-grided to one common grid in North Stereographic Projection (bounding box corresponding to 180/60°N: -3314693.24 -3314693.24 3314693.24 3314693.24) with 700 m resolution and combined into one per time step.

Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.

Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.

Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.

Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.

Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.