This data set provides processed Ku- and Ka-band fully-polarimetric backscatter and derived polarimetric parameters from hourly scans, acquired using the KuKa radar, during Legs 1, 2, 4 and 5 of the 2019-2020 MOSAiC International Arctic Drift Expedition. Scans were acquired during winter (Legs 1 and 2), advanced melt (leg 4) and freeze-up (Leg 5) seasons, from various Remote Sensing (RS) sites, located in the MOSAiC ice floe. The first deployment of the KuKa radar was on 18 October 2019 at RS1 site and the radar was retreated (due to ice break up) on 18th November. The radar was redeployed on 29th November at RS2 site until 13th December when cracks were observed at the site and the instrument was turned off and moved to a safe location. The radar was redeployed at RS3 site and started measuring again on 21st December 2019 until 31st January 2020, after which the radar was taken off the RS site to conduct maintenance. The radar was not operational during Leg 3. During Leg 4, the radar was operational between 25th June and 19th July 2020, and later retreated back to the ship, for deployment in Leg 5. The radar was deployed on 24th August 2020 and operational until the end of the MOSAiC expedition. The dataset was collected by MOSAiC Team ICE participants and processed by Vishnu Nandan at the University of Manitoba, Canada. This work was funded in part through NERC grant NE/S002510/1, the Canada 150 Chair Program and the European Space Agency PO 5001027396. The authors thank Marine Environmental Observation, Prediction and Response Network (MEOPAR) Postdoctoral Fellowship grant to Vishnu Nandan. The authors also thank the crew of R/V Polarstern and all scientific members of the MOSAiC expedition for their support in field logistics and field data collection. ***** PLEASE BE ADVISED TO USE VERSION 2.0 DATA ***** The VERSION 2.0 data set (see ''Related Data Set Metadata'' link below) has been corrected for a bug that was found in the original KuKa radar processing chain.

This data set provides processed Ku- and Ka-band fully-polarimetric backscatter and derived polarimetric parameters from hourly scans, acquired using the KuKa radar, during Legs 1, 2, 4 and 5 of the 2019-2020 MOSAiC International Arctic Drift Expedition. Scans were acquired during winter (Legs 1 and 2), advanced melt (Leg 4) and freeze-up (Leg 5) seasons, from various Remote Sensing (RS) sites, located in the MOSAiC ice floe. The first deployment of the KuKa radar was on 18 October 2019 at RS1 site and the radar was retreated (due to ice break up) on 18th November. The radar was redeployed on 29th November at RS2 site until 13th December when cracks were observed at the site and the instrument was turned off and moved to a safe location. The radar was redeployed at RS3 site and started measuring again on 21st December 2019 until 31st January 2020, after which the radar was taken off the RS site to conduct maintenance. The radar was not operational during Leg 3. During Leg 4, the radar was operational between 25th June and 19th July 2020, and later retreated back to the ship, for deployment in Leg 5. The radar was deployed on 24th August 2020 and operational until the end of the MOSAiC expedition. The dataset was collected by MOSAiC Team ICE participants and processed by Vishnu Nandan at the University of Manitoba, Canada. This work was funded in part through NERC grant NE/S002510/1, the Canada 150 Chair Program and the European Space Agency PO 5001027396. The authors thank Marine Environmental Observation, Prediction and Response Network (MEOPAR) Postdoctoral Fellowship grant to Vishnu Nandan. The authors also thank the crew of R/V Polarstern and all scientific members of the MOSAiC expedition for their support in field logistics and field data collection.

This dataset presents biweekly gridded sea ice thickness and uncertainty for the Arctic derived from the European Space Agency''s satellite CryoSat-2. An associated ''developer''s product'' also includes intermediate parameters used or output in the sea ice thickness processing chain. Data are provided as biweekly grids with a resolution of 80 km, mapped onto a Northern Polar Stereographic Grid, covering the Arctic region north of 50 degrees latitude, for all months of the year between October 2010 and July 2020. CryoSat-2 Level 1b Baseline-D observed radar waveforms have been retracked using two different approaches, one for the ''cold season'' months of October-April and the second for ''melting season'' months of May-September. The cold season retracking algorithm uses a numerical model for the SAR altimeter backscattered echo from snow-covered sea ice presented in Landy et al. (2019), which offers a physical treatment of the effect of ice surface roughness on retracked ice and ocean elevations. The method for optimizing echo model fits to observed CryoSat-2 waveforms, retracking waveforms, classifying returns, and deriving sea ice radar freeboard are detailed in Landy et al. (2020). The melting season retracking algorithm uses the SAMOSA+ analytical echo model with optimization to observed CryoSat-2 waveforms through the SARvatore (SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation) service available through ESA Grid Processing on Demand (GPOD). The method for classifying radar returns and deriving sea ice radar freeboard in the melting season are detailed in Dawson et al. (2022). The melting season sea ice radar freeboards require a correction for an electromagnetic range bias, as described in Landy et al. (2022). After applying the correction, year-round freeboards are converted to sea ice thickness using auxiliary satellite observations of the sea ice concentration and type, as well as snow depth and density estimates from a Lagrangian snow evolution scheme: SnowModel-LG (Stroeve et al., 2020; Liston et al., 2020). The sea ice thickness uncertainties have been estimated based on methods described in Landy et al. (2022). NetCDF files contain detailed descriptions of each parameter. Funding was provided by the NERC PRE-MELT grant NE/T000546/1 and the ESA Living Planet Fellowship Arctic-SummIT grant ESA/4000125582/18/I-NS.