Feasibility Study on Estimation of Sea Ice Drift from KOMPSAT-5 and COSMO-SkyMed SAR Images

Estimating the sea ice drift field is of importance in both scientific study and activities in the polar ocean. Ice motion is being tracked at large scale (10 km and larger) on a daily basis; however, a higher resolution product is desirable for more reliable monitoring of rapid changes in sea ice. The use of wide-swath SAR has been extensively studied; yet, recent high-resolution X-band SAR sensors have not been tested enough. We examine the feasibility of KOMPSAT-5 and COSMO-SkyMed for retrieving sea ice motion by using the dataset of the MOSAiC expedition. The ice drift match-ups extracted from consecutive SAR image pairs and buoys for more than seven months in the central Arctic were used for a performance evaluation and validation. In addition to individual tests for KOMPSAT-5 and COSMO-SkyMed, a cross-sensor combination of two sensors was tested to overcome the drawback, a relatively long revisit time of high-resolution SAR. The experimental results show that higher accuracies are achievable from both single- and cross-sensor configurations of high-resolution X-band SARs compared to wide-swath C-band SARs, and that sub-daily monitoring is feasible from the cross-sensor approach.

Calibration of sea ice drift forecasts using random forest algorithms

Developing accurate sea ice drift forecasts is essential to support the decision-making of maritime end-users operating in the Arctic. In this study, two calibration methods have been developed for improving 10 d sea ice drift forecasts from an operational sea ice prediction system (TOPAZ4). The methods are based on random forest models (supervised machine learning) which were trained using target variables either from drifting buoy or synthetic-aperture radar (SAR) observations. Depending on the calibration method, the mean absolute error is reduced, on average, between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift. Overall, the algorithms trained with buoy observations have the best performances when the forecasts are evaluated using drifting buoys as reference. However, there is a large spatial variability in these results, and the models trained with buoy observations have particularly poor performances for predicting the speed of sea ice drift near the Greenland and Russian coastlines compared to the models trained with SAR observations.

Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions

Arctic sea ice drift motion affects the global material balance, energy exchange and climate change and seriously affects the navigational safety of ships along certain channels. Due to the Arctic's special geographical location and harsh natural conditions, observations and broad understanding of the Arctic sea ice motion are very limited. In this study, sea ice motion data released by the National Snow and Ice Data Center (NSIDC) were used to analyze the climatological, spatial and temporal characteristics of the Arctic sea ice drift from 1979 to 2018 and to understand the multiscale variation characteristics of the three major Arctic sea ice drift patterns. The empirical orthogonal function (EOF) analysis method was used to extract the three main sea ice drift patterns, which are the anticyclonic sea ice drift circulation pattern on the scale of the Arctic basin, the average sea ice transport pattern from the Arctic Ocean to the Fram Strait, and the transport pattern moving ice between the Kara Sea (KS) and the northern coast of Alaska. By using the ensemble empirical mode decomposition (EEMD) method, each temporal coefficient series extracted by the EOF method was decomposed into multiple timescale sequences. We found that the three major drift patterns have four significant interannual variation periods of approximately 1, 2, 4 and 8 years. Furthermore, the second pattern has a significant interdecadal variation characteristic with a period of approximately 19 years, while the other two patterns have no significant interdecadal variation characteristics. Combined with the atmospheric and oceanic geophysical variables, the results of the correlation analysis show that the first EOF sea ice drift pattern is mainly related to atmospheric environmental factors, the second pattern is related to the joint action of atmospheric and oceanic factors, and the third pattern is mainly related to oceanic factors. Our study suggests that the ocean environment also has a strong correlation with sea ice movement. Especially for some sea ice transport patterns, the correlation even exceeds atmospheric forcing.

A new sea ice state dependent parameterization for the free drift of sea ice

Free drift estimates of sea ice motion are necessary to produce a seamless observational record combining buoy and satellite-derived sea ice motion vectors. We develop a new parameterization for the free drift of sea ice based on wind forcing, wind turning angle, sea ice state variables (concentration and thickness) and ocean current (as a residual). Building on the fact that the spatially varying standard wind-ice transfer coefficient (considering only surface wind stress) has a structure as the spatial distribution of sea ice thickness, we introduce a wind-ice transfer coefficient that scales linearly with thickness. Results show a mean error of −0.5 cm/s (low-speed bias) and a root-mean-square error of 5.1 cm/s, considering daily buoy drift data as truth. This represents a 31 % reduction of the error on drift speed compared to the free drift estimates used in the Polar Pathfinder dataset. The thickness-dependent wind transfer coefficient provides an improved seasonality and long-term trend of the sea ice drift speed, with a minimum (maximum) drift speed in May (October), compared to July (January) for the constant wind transfer coefficient parameterizations which simply follow the peak in mean surface wind stresses. The trend in sea ice drift in this new model is +0.45 cm/s decade−1 compared with +0.39 cm/s decade−1 from the buoy observations, whereas there is essentially no trend in the standard free drift parameterization (−0.01 cm/s decade−1) or the Polar Pathfinder free drifts (−0.03 cm/s decade−1). The wind turning angle that minimize the cost function is equal of 25°, with a mean and root-mean square error of +2.6° and 51° on the direction of the drift, respectively. The residual from the minimization procedure (i.e. the ocean currents) resolves key large scale features such as the Beaufort Gyre and Transpolar Drift Stream, and is in good agreement with ocean state estimates from the ECCO, GLORYS and PIOMAS ice-ocean reanalyses, and geostrophic currents from dynamical ocean topography, with a root-mean-square difference of 2.4, 2.9, 2.6 and 3.8 cm/s, respectively. Finally, a repeat of the analysis on a two sub-section of the time series (pre- and post-2000) clearly shows the acceleration of the Beaufort Gyre (particularly along the Alaskan coastline) and an expansion of the gyre in the post-2000 concurrent with a thinning of the sea ice cover and observations acceleration of the ice drift speed and ocean current. This new dataset is publicly available for complementing merged observations-based sea ice drift datasets that includes satellite and buoy drift records.

Towards a swath-to-swath sea-ice drift product for the Copernicus Imaging Microwave Radiometer mission

Across spatial and temporal scales, sea-ice motion has implications for ship navigation, the sea-ice thickness distribution, sea-ice export to lower latitudes and re-circulation in the polar seas, among others. Satellite remote sensing is an effective way to monitor sea-ice drift globally and daily, especially using the wide swaths of passive microwave missions. Since the late 1990s, many algorithms and products have been developed for this task. Here, we investigate how processing sea-ice drift vectors from the intersection of individual swaths of the Advanced Microwave Scanning Radiometer 2 (AMSR2) mission compares to today's status quo (processing from daily averaged maps of brightness temperature). We document that the “swath-to-swath” (S2S) approach results in many more (2 orders of magnitude) sea-ice drift vectors than the “daily map” (DM) approach. These S2S vectors also validate better when compared to trajectories of on-ice drifters. For example, the RMSE of the 24 h winter Arctic sea-ice drift is 0.9 km for S2S vectors and 1.3 km for DM vectors from the 36.5 GHz imagery of AMSR2. Through a series of experiments with actual AMSR2 data and simulated Copernicus Imaging Microwave Radiometer (CIMR) data, we study the impact that geolocation uncertainty and imaging resolution have on the accuracy of the sea-ice drift vectors. We conclude by recommending that a swath-to-swath approach is adopted for the future operational Level-2 sea-ice drift product of the CIMR mission. We outline some potential next steps towards further improving the algorithms and making the user community ready to fully take advantage of such a product.

Estimating instantaneous sea-ice dynamics from space using the bi-static radar measurements of Earth Explorer 10 candidate Harmony

This article describes the observation techniques and suggests processing methods to estimate dynamical sea-ice parameters from data of the Earth Explorer 10 candidate Harmony. The two Harmony satellites will fly in a reconfigurable formation with Sentinel-1D. Both will be equipped with a multi-angle thermal infrared sensor and a passive radar receiver, which receives the reflected Sentinel-1D signals using two antennas. During the lifetime of the mission, two different formations will be flown. In the stereo formation, the Harmony satellites will fly approximately 300 km in front and behind Sentinel-1, which allows for the estimation of instantaneous sea-ice drift vectors. We demonstrate that the addition of instantaneous sea-ice drift estimates on top of the daily integrated values from feature tracking have benefits in terms of interpretation, sampling and resolution. The wide-swath instantaneous drift observations of Harmony also help to put high-temporal-resolution instantaneous buoy observations into a spatial context. Additionally, it allows for the extraction of deformation parameters, such as shear and divergence. As a result, Harmony's data will help to improve sea-ice statistics and parametrizations to constrain sea-ice models. In the cross-track interferometry (XTI) mode, Harmony's satellites will fly in close formation with an XTI baseline to be able to estimate surface elevations. This will allow for improved estimates of sea-ice volume and also enables the retrieval of full, two-dimensional swell-wave spectra in sea-ice-covered regions without any gaps. In stereo formation, the line-of-sight diversity allows the inference of swell properties in both directions using traditional velocity bunching approaches. In XTI mode, Harmony's phase differences are only sensitive to the ground-range direction swell. To fully recover two-dimensional swell-wave spectra, a synergy between XTI height spectra and intensity spectra is required. If selected, the Harmony mission will be launched in 2028.