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Automated sea ice charting from Synthetic Aperture Radar (SAR) has been researched for more than a decade and still, we are not close to unlocking the full potential of automated solutions in terms of resolution and accuracy. The central complications arise from ground truth data not being readily available in the polar regions. In this paper, we build a dataset from 20 near coincident X-Band SAR acquisitions and as many Airborne Laser Scanner (ALS) measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), between October and May. This dataset is then used to assess the accuracy and robustness of five machine learning based approaches, by deriving classes from the freeboard, surface roughness (standard deviation at 0.5 m correlation length) and reflectance. It is shown that there is only a weak correllation of the radar backscatter and the sea ice topography. Accuracies between 40 % and 69 % percent and robustnesses between 68 % and 85 % give a realistic insight into modern classifiers' performance across a range of ice conditions over 8 months. It also marks the first time algorithms are trained entirely with labels from coincident measurements, allowing for a probabilistic class retrieval. The results show that segmentation models able to learn from the class distribution significantly perform pixel-wise classification approaches.

Since 2010, multiple cyber incidents on industrial infrastructure, such as Stuxnet and CrashOverride, have exposed the vulnerability of Industrial Control Systems (ICS) to cyber threats. The industrial systems are commissioned for longer duration amounting to decades, often resulting in non-compliance to technological advancements in industrial cybersecurity mechanisms. The unavailability of network infrastructure information makes designing the security policies or configuring the cybersecurity countermeasures such as Network Intrusion Detection Systems (NIDS) challenging. An empirical solution is to self-learn the network infrastructure information of an industrial system from its monitored network traffic to make the network transparent for downstream analyses tasks such as anomaly detection. In this work, a Python-based industrial communication paradigm-aware framework, named PROFINET Operations Enumeration and Tracking (POET), that enumerates different industrial operations executed in a deterministic order of a PROFINET-based industrial system is reported. The operation-driving industrial network protocol frames are dissected for enumeration of the operations. For the requirements of capturing the transitions between industrial operations triggered by the communication events, the Finite State Machines (FSM) are modelled to enumerate the PROFINET operations of the device, connection and system. POET extracts the network information from network traffic to instantiate appropriate FSM models (Device, Connection or System) and track the industrial operations. It successfully detects and reports the anomalies triggered by a network attack in a miniaturized PROFINET-based industrial system, executed through valid network protocol exchanges and resulting in invalid PROFINET operation transition for the device., To be published in the proceedings of EAI TRIDENTCOM 2022

Relating the sea-ice surface to the under-ice topography is a timely scientific effort in the Arctic. This relation is crucial for estimating the ice thickness distribution for large‐scale modelling, for assessing the mechanical force that ships need to overcome, for risk evaluation of offshore structures, for determining roughness characteristics to derive wind and water drag coefficients for dynamics modelling, for sound scattering, and for the confinement of under-ice oil spills. Existing relations are based on numerical modelling assuming estimates of snow depth, snow density, and ice density or are based on field observations confined to specific areas and short time periods. MOSAiC provided the first year-long, high-resolution dataset of sea-ice draft derived from a multibeam echosounder. In combination with co-located freeboard estimates from airborne mapping of the surface, we construct the 3D sea-ice topography to study the evolution of sea-ice geometry both at the surface and underside. We can obtain direct and high precision relations between draft and freeboard on an almost weekly basis for an ice floe continuously drifting from the North Pole to Fram Strait during winter, spring, and summer. A precise evaluation of total ice thickness, ice density, freeboard, draft and their respective relations on small scales is crucial information to future satellite remote sensing ice thickness retrievals, a key asset of climate monitoring in the Arctic.

Oral presentation at the 2nd MOSAiC science conference showing the MOSAiC airborne laser scanner product overview and first results of seasonal surface roughness

The SIN’XS project is a three-year activity (May 2022 – May 2025) funded by ESA in the frame of the Polar Science Cluster.In light of rapid changes of the Arctic and Antarctic sea ice cover, continued and improved observations, understanding, and predictions of its thickness are particularly important for a range of fields from climate studies to offshore operations in ice. Systematic and accurate ice thickness observations are now available from several satellite missions. However, they differ in used processing algorithms and assumptions, temporal and spatial coverage and resolution, and applicability to stakeholder needs like modelling and assimilation, numerical weather prediction, and ship routing. These differences between products have so far complicated the consistent use of the various data products and there is little consensus about Arctic and Antarctic Sea ice volume variability and change. The Sea Ice-thickness product iNter-comparison eXerciSe (SIN’XS) will identify some of these gaps by carrying out in-depth intercomparisons of a wide range of satellite ice thickness products from altimetry and other methods, in close collaboration with an international community of scientific and operational sea ice experts, and in partnership with the WMO Global Cryosphere Watch (GCW).It will develop joint protocols for the intercomparison of ice thickness products and their validation, based on established approaches from the QA4EO project and by further developing a framework for Fiducial Reference Measurements (FRMs). SIN’XS will develop an online system to engage the community with data submission and to support scientific analysis of the data sets and intercomparisons., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The porosity of sea ice is a crucial property for understanding processes and feedback mechanisms of the polar-climate system, as it strongly affects the physical, ecological, and biogeochemical properties of sea ice. Sea ice porosity is also closely related to surface flooding and the solid fraction of platelet ice in Antarctica.A direct method for measuring sea ice porosity is electromagnetic (EM) induction sounding, which can be applied on the ground or from airborne platforms. We present advancements in data collection, calibration, and inversion procedures. In this study, we modified the Phyton EMagPy API for fast and adaptable inversion of sea ice surveys and use inverted EM data to study the porosity of sea ice in the Arctic and Antarctic.Over Arctic summer sea, we retrieve the macro-scale porosity of rotten ice and the salinity and depth evolution of melt ponds. The salinity and porosity evolution could be reproduced with varying success, but the addition of several layers in the inversion improved the thickness information compared to a single-layer subsurface model, affecting ice mass balance.For high-resolution EM data over Antarctic fast ice we retrieve the thickness and solid fraction of a variable sub-ice platelet layer below the solid sea ice. The inverted thicknesses of sea ice and the platelet layer as well as their conductivities are in good agreement with independent, coincident in-situ drill hole and CTD measurements., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Fluid power: digital, reliable, sustainable : 13th International Fluid Power Conference : 13th-15th June 2022 : proceedings — Open Access version / Editor: Katharina Schmitz 13. International Fluid Power Conference, IFK 2022, Aachen, Germany, 13 Jun 2022 - 15 Jun 2022; Aachen : RWTH Aachen University 206-215 (2023)., Published by RWTH Aachen University, Aachen

Fluid power: digital, reliable, sustainable : 13th International Fluid Power Conference : 13th-15th June 2022 : proceedings — Open Access version / Editor: Katharina Schmitz 13. International Fluid Power Conference, IFK 2022, Aachen, Germany, 13 Jun 2022 - 15 Jun 2022; Aachen : RWTH Aachen University 1160-1171 (2023)., Published by RWTH Aachen University, Aachen

Background In 2019, the German Multiple Sclerosis (MS) Society was certified as a provider for ?Funktionstraining? (FT). FT is an exercise training program developed for people with disabilities or people at risk of disability, takes place weekly and is led by a qualified FT-therapist. It is prescribed by a physician (between 12 and 24 months) and is part of outpatient medical rehabilitation. Due to the COVID-19 pandemic, the tele-rehabilitation concept ‘Funktionstraining-online’ (FT-online) was developed for pwMS. This study examines the FT-online from a participants' point of view within an online survey. Methods We developed an online questionnaire that included data of sample characteristics, participants' experiences with FT-online in terms of usability and training content. Further, the survey captured the quality of FT-online and the FT-therapist and compared carefully FT-online with face-to-face training. The standardised questionnaires Self-Efficacy Scale - Short Form (ASKU), the Multiple Sclerosis Questionnaire for Physiotherapists MSQPT and the Fatigue-Questionnaire (WeiMuS) were included. The 103 interviewed people with MS (pwMS) participated in FT-online at least for three months. Results Data analyses are in process. The response rate was 75% (n = 78). The analysed sample is 53.4±8.1 years old with a disease duration of 18±8.7 years (male=7, female=71). Preliminary results in the ASKU certify a low self-efficacy in the sample (2.25 ±0.67). Qualitative results show the advantages for FT-online in the independence of mobility and infrastructure, flexibility, compatibility within job and family, group feeling in times of social distancing and an increase in quality of life. The disadvantages described by the sample are mainly technical problems and lack of socialising. 35% (n = 27) participants experienced both: FT-online and face-to-face. 44.4% (n = 12) of this sample prefer FT-online, 33.3% (n = 9) prefer the face-to-face training course and 22.2% (n = 6) rate both kinds of training equal. Conclusions The implementation of FT-online was an important step to ensure the exercise-related physical activity in pwMS during the COVID-19 pandemic. The descriptive analyses show high potential for FT-online training in pwMS. We are aware of the bias in current data, because of the pandemic situation both the free choice of FT-offers and the pandemic circumstances held influence in study outcomes. First results lead to the conclusion that FT-online may be beneficial for a broad range of persons with neurologic diseases, movement disorders and fatigue episodes and should be considered for future medical rehabilitation.

Sea-ice thickness is a key factor and indicator in understanding the impact of the global climate change. Deriving basin-wide sea-ice thickness estimates from satellite laser and radar altimetry relies on freeboard measurements. The freeboard-to-thickness conversion in turn requires information of snow mass and the density of the sea-ice layer that have unknown spatio-temporal variabilities and trends directly translating into the uncertainty of decadal sea-ice thickness data records. In addition, inter-mission biases arise from, e.g., different sensor types and frequencies as well as varying footprint sizes affected by surface roughness across regions and seasons. Therefore, carrying out validation and inter-calibration studies is crucial for reliable and continuous observation of the Earth’s cryosphere. To achieve this, it is beneficial to have simultaneous measurements of freeboard, snow depth, and sea-ice thickness, which provide reference data for both direct satellite observations and geophysical target parameters. Here, we present Alfred Wegener Institute’s (AWI) IceBird program, which is a series of fixed-wing aircraft campaigns to measure Arctic sea ice and to monitor its change. During two late-winter campaigns in the western Arctic Ocean in 2017 and 2019, we have carried out surveys with the unique scientific instrument configuration including an airborne laser scanner (ALS) for surface topography and freeboard measurements, a tethered electromagnetic induction sounding instrument (EM-Bird) for total (snow+ice) thickness measurements, and an ultrawideband frequency-modulated continuous-wave microwave radar to measure snow thickness. Therefore, we are able to observe all three bounding interfaces in the sea-ice–snow system in high resolution along survey tracks on regional scales. During the ship-based drift expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) between October 2019 and September 2020, helicopter surveys were carried out in high spatio-temporal resolution throughout the year, including the polar night, to measure freeboard and roughness with the ALS both in local grid pattern and in larger scale. Coincident EM-Bird ice thickness data and information from snow measurements on the ground will help linking these parameters and monitor them and their effect on satellite retrievals for a full seasonal cycle. The individual parameters are important for describing and monitoring the state of the Arctic sea ice and validating retrievals from satellite data, but combined they offer further possibilities to characterise sea ice. By assuming isostatic equilibrium, we are able to estimate up-to-date bulk density values for different sea-ice types from the IceBird data and to derive a parametrisation of sea-ice bulk density based on sea-ice freeboard. These data allow us to explore spatio-temporal variations in sea-ice parameters observable from space and to evaluate the validity of the freeboard-to-thickness conversion in satellite altimetry through comparison against dedicated satellite overpasses and orbit collections.

An integrated sensor platform including an inertial navigation system (INS) and a commercial airborne laser scanner (ALS) among other sensor was mounted in the cargo compartment in one of the Polarstern helicopters during MOSAiC. ALS data was acquired from more than 60 flights between October 2019 and September 2020 with a range of survey types intended to map changes of the sea ice surface during the full annual cycle at high spatial resolution and coverage. Here, we provide an overview of the collected data, the challenge of achieving centimeter elevation accuracy with a helicopter platform at high polar latitudes as well as the content and specifications of ALS data products. The high spatial resolution and repeated coverage of the larger area around Polarstern allow studying various surface features (e.g. pressure ridges, floes, melt ponds, snow drifts, etc.), their seasonal evolution, and their impact on atmosphere and ocean. Finally, we outline methods for planned applications, such as identifying individual floes and surface types using both measured freeboard and surface reflectance. Collocated helicopter-based optical and infrared imagery allow analyzing sea ice properties in further applications and to upscale comparable in-situ observations.

The SIN’XS project, led by NOVELTIS in collaboration with AWI, LEGOS and UCL, is a three-year activity (May 2022 – May 2025) funded by ESA in the frame of the Polar Science Cluster, with the objective to foster collaborative research and interdisciplinary networking actions. In light of rapid changes of the Arctic and Antarctic sea ice cover, continued and improved observations, understanding, and predictions of its thickness are particularly important for a range of fields from climate studies to offshore operations in ice. Systematic and accurate ice thickness observations are now available from several satellite missions. However, they differ in used processing algorithms and assumptions, temporal and spatial coverage and resolution, and applicability to stakeholder needs like modelling and assimilation, numerical weather prediction, and ship routing. These differences between products have so far complicated the consistent use of the various data products, and there is little consensus about Arctic and Antarctic Sea ice volume variability and change. The Sea Ice-thickness product iNter-comparison eXerciSe (SIN’XS) will identify some of these gaps by carrying out in-depth intercomparisons of a wide range of satellite ice thickness products from altimetry and other methods, in close collaboration with an international community of scientific and operational sea ice experts, and in partnership with the WMO Global Cryosphere Watch (GCW). It will develop joint protocols for the intercomparison of ice thickness products and their validation, using established approaches from the GEO/CEOS Quality Assurance framework for Earth Observation (QA4EO) and by further developing a framework for Fiducial Reference Measurements (FRMs). SIN’XS will develop an online system to engage the community with data submission and to support scientific analysis and impact assessment of the data sets and intercomparisons. In this poster, we will present the main objectives, the tools and the first outcomes of the project.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

In the polar oceans in winter, fractures and leads are the hotspots of exchange between the ocean and atmosphere which are otherwise well separated by sea ice. By altering the heat, gas, and momentum fluxes they play a crucial role in atmospheric, ecological, and oceanic processes. At the same time, leads represent a part of the present state of strain of the ice cover, opening up the possibility to study ice rheology. The transient nature of leads and their narrow appearance has set limits to the detection of leads from satellites. Different approaches using active and passive sensors from the microwave and infrared spectrum are employed so far to observe leads by means of satellite data. They make use of the strong contrast between leads and the surrounding ice pack in (i) surface temperature, (ii) microwave backscatter, (iii) emission or (iv) a change in ice drift speed. With the increasing availability of high-resolution SAR data for the Arctic, we explored the potential to use SAR derived sea ice deformation to estimate lead fractions. We calculated sea ice drift and divergence with a spatial resolution of 1.4 km from daily Sentinel-1 scenes. We obtained the divergence-based lead fraction of a region by summing up all positive divergence pixels multiplied by the respective time step length. We derived a second lead fraction product from the deformation fields that calculates the position of linear kinematic features (LKFs) first. The advantage is a skilled noise reduction, and a tracking algorithm of the deformation zones. We compared divergence- and LKF-based lead fractions to several other established lead fraction products in the Transpolar Drift along the drift track of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) between October 2019 to April 2020. We used lead fractions from helicopter-borne infrared surveys at a grid resolution of 5 m, classified Sentinel-1 (SAR) scenes at 80 m, MODIS (thermal infrared) at 1 km, AMSR2 (passive microwaves) at 3.25 km, and CryoSat-2 (altimeter in Ku-band) at 12.5 km. Since the methods rely on different physical properties of the water and ice in leads and are affected by different constraints, derived mean lead fractions vary by 1-2 magnitudes between the products. For example, infrared, SAR and microwave radiometer-based algorithms do not only detect open-water leads but also leads with thin ice up to a certain thickness, which differs between the products. Common lead events were identified across products. The time series mostly indicated a phase of increased lead activity during freeze-up in autumn 2019 and spring 2020. We used the different lead fraction time series to estimate new ice formation in the leads and compared the results to ice thickness and oceanographic measurements obtained during the MOSAiC campaign. Results yield lower and upper bounds for ice formation and brine expulsion in and from leads. Due to the wide range of lead fractions obtained from different methods, we conclude that the specific lead fraction product must be chosen depending on research question. Divergence- and LKF-based lead fractions provide valuable information in addition to established lead fraction products at high spatial resolution and independent of cloud coverage.

The net productivity of sea ice is determined by the physical and geochemical characteristics of the ice–ocean system and the activity of organisms inhabiting the ice. Differences in habitat suitability between first-year and multi-year sea ice can affect the ice algal community composition and acclimation state, introducing considerable variability to primary production within each ice type. In this study, we characterized the biogeochemical variability between adjacent first-year and multi-year sea ice floes in the Lincoln Sea of the Canadian High Arctic, during the May 2018 Multidisciplinary Arctic Program—Last Ice sampling campaign. Combining measurements of transmitted irradiance from a remotely operated underwater vehicle with laboratory-based oxygen optode incubations, this work shows widespread heterotrophy (net oxygen uptake) in the bottom 10 cm of both ice types, particularly in thick multi-year ice (>2.4 m) and early morning of the 24-h day. Algal acclimation state and species composition varied between ice types despite similar net community production due to widespread light and nutrient limitation. The first-year ice algal community was increasingly dominated over spring by the potentially toxin-producing genus Pseudonitzschia that was acclimated to high and variable light conditions characteristic of a thinner ice habitat with mobile snow cover. In comparison, the multi-year ice harbored more shade-acclimated algae of mixed composition. This work highlights the potential for heterotrophy in sea ice habitats of the High Arctic, including first measurements of such O2-uptake in multi-year ice floes. Observed differences in photophysiology between algae of these sea ice types suggests that a shift toward higher light availability and a younger sea ice cover with climate change does not necessarily result in a more productive system. Instead, it may favor future sea ice algal communities of different species composition, with lower photosynthetic potential but greater resilience to stronger and more variable light conditions.

Arctic sea ice thickness variability and change and their dependence on the atmospheric and oceanic forcing are at the core of research in Subtopic 2.1, Theme: Ongoing and Future Arctic and Antarctic Climate Change. Our research is particularly focused on a better process understanding and representation in models, and observations during MOSAiC play a strong role. The poster gives examples of such process studies focused on Arctic sea ice thickness variability and change. We outline observations of the long-term and regional variability and change of sea ice thickness using satellite remote sensing, airborne surveying, and ice mass balance buoys. Thermodynamic growth and its interaction with the atmosphere over leads and level ice serves as an example for our joint research interests. The poster also gives examples of causes of sea ice thinning, like increased ocean heat flux to the ice due to Atlantification, and consequences, e.g., for reduced sea ice volume transport through Fram Strait.

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Light transmission through Arctic sea ice and snow has an important impact on energy partitioning at the atmosphere-ice-ocean interface and the ice-associated ecosystem. Thus, it is crucial to understand which parameters determine the temporal and spatial variation of sea ice transmission. Ice and snow imprint characteristic features on the spectral shape of transmitted light. Here, we aim to use these spectral features to retrieve snow depth from hyperspectral under-ice light measurements. Transmitted spectral radiance was measured underneath a 100 m long transect on level landfast First-Year-Ice (FYI) using a remotely operated vehicle (ROV). Measurements took place off the northern coast of Ellesmere Island close to the Canadian Forces Station Alert in May 2018. Co-located measurements of ice and snow thickness were acquired with an electromagnetic induction device, a Magna-Probe and a terrestrial laser scanner. The small variation in FYI thickness allows separating the spectral effect of snow depth on transmitted radiance spectra. We retrieve snow depth from spectral transflectance data using an inverse algorithm based on normalized difference indices (NDI). We further fit multiplicative exponential functions to the measured spectra to retrieve wavelength-dependent extinction coefficients of sea ice and snow. Fitted values of broadband bulk extinction coefficients range from 1.8 to 3.5 m-1 for sea ice and from 7.4 to 17.2 m-1 for snow. Mean differences in fitted/calculated and measured modal snow depths are 6 cm for the multiplicative exponential functions and 5 cm using NDIs. 41% of the fitted snow depths lie within 5 cm of the measured snow depths using the multiplicative exponential functions and 42% for the NDI-method. The accuracy of snow depth retrieved from our optical approaches is limited to +/-5 cm, as the variation of snow depth within the optical sensor footprint is between 2.5 and 5.0 cm. Our results show how this optical approach allows to derive large-scale snow depth information from under-ice optical spectra e.g. during autonomous long-range under-ice missions, despite its limited spatial resolution and absolute accuracy.

The reconstruction of past Antarctic sea ice coverage through the application of diatom assemblages is often hampered in near coastal environments due to silica dissolution effects. The more recently established approach of using highly branched isoprenoid biomarkers to identify past sea ice conditions seems a valid method to overcome this limitation and that may also provide insight into ice-shelf dynamics. Here, we evaluate the so-called PIPSO25 index applied to modern surface sediments from the Amundsen Sea, the Drake Passage and Bransfield Strait, and the Weddell Sea. The comparison of biomarker-based sea ice estimates with satellite-derived sea ice concentrations supports the potential of the proxy approach. In a next step, we generated biomarker records using two sediment cores from the western and eastern Weddell Sea to track sea ice variability over late Pleistocene glacial-interglacial cycles. Consideration of additional data such as XRF and multi-sensor core logging records as well as micropaleontological investigations enables a comprehensive assessment of the environmental changes in the Weddell Sea in response to large-scale climate transitions. While magnetic susceptibility and density data obtained for both cores display similar patterns, we note distinct differences between the biomarker records highlighting local feedback mechanisms affecting sea ice cover.

Dynamic processes can contribute more than 50% to the total thickness of deformed sea ice. They gain importance in the context of a changing Arctic, in which reduced ice thickness and increased drift speed enhance deformation events that might compensate the thermodynamic thinning. The case study of an unusual, large polynya that opened and then closed by freezing and convergence north of the coast of Greenland in late winter 2018 offers us the unique chance to study the effects of dynamics on ice thickness in detail. We calculated drift and deformation fields for the closing phase of the polynya from a time series of daily Synthetic Aperture Radar (SAR) satellite images and measured the accumulated effects of dynamic and thermodynamic ice growth with an airborne electromagnetic (AEM) ice thickness survey one month after the closing began. We observed that strong ice convergence decreased the area of the former polynya by a factor of 2.5 while the AEM survey indicated mean and modal thicknesses of the one-month old ice of 1.96 and 0.95 m, respectively. This is in close agreement with the calculated thermodynamic growth and with the dynamic thickening expected from the polynya area decrease. Further, we identified characteristic differences in the shapes of ice thickness distributions in different regions of the closing polynya that are linked to the deformation histories of the ice. We reconstructed the deformation history by combining Lagrangian backward trajectories with the deformation fields derived from high-resolution SAR imagery. We found a linear proportionality between convergence and thickness change in good agreement with ice thickness redistribution theory. Further, we identified a proportionality between the e-folding of the tails of the different ice thickness distributions and the magnitude of the total deformation experienced by the ice. Lastly, we developed a simple volume-conserving model to derive dynamic ice thicknesses change from high-resolution SAR deformation tracking. Model and AEM observations agree reasonably well and the derived ice thickness distribution reproduce main characteristics like mode, width, and e-folding of the observed distribution. Hence, this shows that high-resolution SAR deformation observations are capable of producing realistic ice thickness distributions.

As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.

Light transmittance through Arctic sea ice and snow has an important impact on both the ocean heat content and the ice-associated ecosystem. The partitioning of the radiation is a key factor of the mass and energy balance of Arctic sea ice. It is therefore crucial to measure sea ice transmittance and understand which parameters determine its variation on temporal and spatial scales. Ice and snow imprint characteristic features in the spectral shape of transmitted light. Transmitted spectral irradiance was recorded at the underside of levelled landfast First-Year-Ice (FYI) in a refrozen lead using a hyper-spectral radiometer mounted on a remotely operated vehicle (ROV) during the Last Ice Area campaign off Alert in the Lincoln Sea in May 2018. The main benefits of using the ROV are large spatial coverage in comparably short survey times and non-destructive measurements under sea ice. Snow depth was obtained using a Magna Probe and a Terrestrial Laser Scanner measured the surface topography. The total ice thickness was recorded with a ground-based electromagnetic induction sounding device whereas an upward-looking single-beam sonar also mounted on the ROV recorded ice draft. This unique co-located data set enables to categorize groups of spectral transmittances. Due to the relatively constant FYI thickness it was possible to separate the spectral effect of snow depth on the light transmittance. Further we discuss how to retrieve snow depth and ice thickness based only on spectral transmittance data by developing a new observation-based inverse algorithm. Three methods are envisioned: First, to fit a multiplicative exponential function to the spectra which includes wavelength-dependent extinction coefficients of snow and sea ice. Second, to follow a statistical approach using normalized difference indices (NDIs) to construct spectral correlation coefficients between the NDIs with snow depth and ice thickness. Third, to generate synthetic spectra from snow depth and ice thickness using the radiative transfer model AccuRT and compare those with the observed spectra. Expected results are accurate snow depth and sea ice thickness (as well as melt pond depth and coverage).

Moored upward-looking Acoustic Doppler Current Profilers (ADCPs) can be used to observe sea ice draft. While previous studies relied on the availability of auxiliary pressure sensors to measure the instrument depth of the ADCP, we present an adaptive approach that infers instrument depth from the ADCPs default bottom track (BT) mode measurements of error velocity and range. We demonstrate that this method can be used to obtain daily mean sea ice draft time series with an estimated uncertainty of 0.1 m. The ADCP-derived ice draft time series are validated with data from adjacent Upward-Looking Sonar (ULS) moorings in the Laptev Sea. This new approach provides a low-cost opportunity to derive daily mean ice draft time series accessing existing ADCP data. Applying this method to ADCP data from the sparsely-sampled Laptev Sea allows an extension of mooring-based in situ ice draft measurements from 2 to about 13 years. The Laptev Sea is an important region for net ice production and a major contributor to the Transpolar Drift System. Recent studies show that sea ice area and volume exports from the Laptev Sea are increasing. This increased export accelerates summer sea ice retreat in the Laptev Sea and has far-reaching consequences for the entire Arctic sea ice balance. The newly acquired Laptev Sea ice draft data archive is used to analyse seasonal and interannual changes in sea ice thickness from 2003 to 2016. In addition, it provides unique data for the comparison with sea ice thickness data records derived from ENVISAT, CryoSat-2 and SMOS satellite measurements in an area where large-scale validation data sets are currently unavailable.

Observations of sea-ice concentration are available from satellites year-round and almost weather-independently using passive microwave radiometers at resolutions down to 5 km. Thermal infrared radiometers provide data with a resolution of 1 km but only under cloud-free conditions. We use the best of the two satellite measurements and merge thermal infrared and passive microwave sea-ice concentrations. This yields a merged sea-ice concentration product combining the gap-free spatial coverage of the passive microwave sea-ice concentration and the 1 km resolution of the thermal infrared sea-ice concentration. The benefit of the merged product is demonstrated by observations of a polynya which opened north of Greenland in February 2018. We find that the merged sea-ice concentration product resolves leads at sea-ice concentrations between 60 % and 90 %. They are not resolved by the coarser passive microwave sea-ice concentration product. The benefit of the merged product is most pronounced during the formation of the polynya. Next, the environmental conditions during the polynya event are analysed. The polynya was caused by unusual southerly winds during which the sea ice drifted northward instead of southward as usual. The daily displacement was 50 % stronger than normal. The polynya was associated with a warm-air intrusion caused by a high-pressure system over the Eurasian Arctic. Surface air temperatures were slightly below 0 ∘C and thus more than 20 ∘C higher than normal. Two estimates of thermodynamic sea-ice growth yield sea-ice thicknesses of 60 and 65 cm at the end of March in the area opened by the polynya. This differed from airborne sea-ice thickness measurements, indicating that sea-ice growth processes in the polynya are complicated by rafting and ridging. A sea-ice volume of 33 km3 was produced thermodynamically.

In February and March 2018 a unique polynya of open water had opened in the Wandel Sea north of Greenland due to unusually strong southerly winds. The polynya subsequently refroze and the area of the growing young ice strongly decreased by ice convergence due to northerly winds, constituting a natural, well-constrained, full-scale ice deformation experiment. We have carried out an airborne electromagnetic ice thickness survey of the young ice one month after the polynya began to close. It showed a thickness distribution with a modal ice thickness of 1 m and mean thickness of 2 m, representative of the contributions of thermodynamic and dynamic growth in the one month since the ice had begun to grow. We used time series of Sentinel 1 SAR images to back-track the surveyed young ice to its location of initial formation. Results showed that the area of young ice approximately halved between the time of its formation and the time of the ice thickness survey. This is in good qualitative agreement with the result of the thickness survey, showing that its mean thickness was twice its modal thickness, i.e. mean ice thickness doubled while the region's area halved. These results and the SAR observed deformation processes provide valuable information for improving representation of ice rheology and thickness redistribution in sea ice models.

These are the data files for the PLOS ONE journal article "Two-Sided Matching for mentor-mentee allocations - Algorithms and manipulation strategies". Three files are provided: - Data.csv: An overview of the original preferences of mentors and mentee, a data dictionary, and two summary tables used to create figures in the manuscript - MatchingTables.csv: The outcome matching tables for each simulated scenario and repetition - Preferences.csv: The (un)manipulated preferences that were used as input to calculate the solution for each simulated scenario and repetition.

The snow layer on sea ice has high importance for polar climate as it affects heat, radiation, and fresh-water budgets. Additionally, snow loading is a critical parameter for the sea-ice freeboard-to-thickness conversion for satellite radar and laser altimeters. Despite its importance, there is a lack of snow observations spanning different spatial and temporal scales, thus introducing a significant source of uncertainty to altimetric sea-ice thickness retrievals. The ultra-wideband microwave radar (UWBM) Snow Radar, a 2–18 GHz airborne frequency-modulated continuous-wave (FMCW) radar developed by the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas, can accurately detect the air/snow and snow/ice interfaces to measure snow thickness. Since 2009, an airborne Snow Radar has been operated onboard NASA’s Operation IceBridge (OIB) campaigns. In 2017, the UWBM Snow Radar was operated for the first time on an Alfred Wegener Institute (AWI) research aircraft, together with an airborne laser scanner for surface topography and freeboard measurements and an electromagnetic induction sounding instrument (EM Bird) to measure total ice thickness. The AWI airborne surveys operate at a low survey altitude (60 m a.g.l.) and slow aircraft speed, enabling fine-resolution mapping of the snow layer. Furthermore, the unique instrument setup on board the AWI research aircraft and the concurrent measurements of snow freeboard, total sea-ice thickness and snow depth allow us to directly investigate the freeboard-to-thickness conversion on regional scales for the first time. Here, we evaluate the performance of the radar installation and present radar-derived snow depth retrieved with a wavelet technique from recent airborne campaigns, PAMARCMiP2017 and IceBird winter 2019, over Arctic sea ice in the Greenland, Lincoln, Beaufort and Chukchi Seas and the central Arctic Ocean in March–April of the respective years.

Additional file 1 The file contains eight figures (Figures S1–S8) and four tables (Tables S1-S4) as supplementary results.

In 2018, the dominant greenhouse gases released into Earth's atmosphere-carbon dioxide, methane, and nitrous oxide-continued their increase. The annual global average carbon dioxide concentration at Earth's surface was 407.4 ± 0.1 ppm, the highest in the modern instrumental record and in ice core records dating back 800 000 years. Combined, greenhouse gases and several halogenated gases contribute just over 3 W m−2 to radiative forcing and represent a nearly 43% increase since 1990. Carbon dioxide is responsible for about 65% of this radiative forcing. With a weak La Niña in early 2018 transitioning to a weak El Niño by the year's end, the global surface (land and ocean) temperature was the fourth highest on record, with only 2015 through 2017 being warmer. Several European countries reported record high annual temperatures. There were also more high, and fewer low, temperature extremes than in nearly all of the 68-year extremes record. Madagascar recorded a record daily temperature of 40.5°C in Morondava in March, while South Korea set its record high of 41.0°C in August in Hongcheon. Nawabshah, Pakistan, recorded its highest temperature of 50.2°C, which may be a new daily world record for April. Globally, the annual lower troposphere temperature was third to seventh highest, depending on the dataset analyzed. The lower stratospheric temperature was approximately fifth lowest. The 2018 Arctic land surface temperature was 1.2°C above the 1981-2010 average, tying for third highest in the 118-year record, following 2016 and 2017. June's Arctic snow cover extent was almost half of what it was 35 years ago. Across Greenland, however, regional summer temperatures were generally below or near average. Additionally, a satellite survey of 47 glaciers in Greenland indicated a net increase in area for the first time since records began in 1999. Increasing permafrost temperatures were reported at most observation sites in the Arctic, with the overall increase of 0.1°-0.2°C between 2017 and 2018 being comparable to the highest rate of warming ever observed in the region. On 17 March, Arctic sea ice extent marked the second smallest annual maximum in the 38-year record, larger than only 2017. The minimum extent in 2018 was reached on 19 September and again on 23 September, tying 2008 and 2010 for the sixth lowest extent on record. The 23 September date tied 1997 as the latest sea ice minimum date on record. First-year ice now dominates the ice cover, comprising 77% of the March 2018 ice pack compared to 55% during the 1980s. Because thinner, younger ice is more vulnerable to melting out in summer, this shift in sea ice age has contributed to the decreasing trend in minimum ice extent. Regionally, Bering Sea ice extent was at record lows for almost the entire 2017/18 ice season. For the Antarctic continent as a whole, 2018 was warmer than average. On the highest points of the Antarctic Plateau, the automatic weather station Relay (74°S) broke or tied six monthly temperature records throughout the year, with August breaking its record by nearly 8°C. However, cool conditions in the western Bellingshausen Sea and Amundsen Sea sector contributed to a low melt season overall for 2017/18. High SSTs contributed to low summer sea ice extent in the Ross and Weddell Seas in 2018, underpinning the second lowest Antarctic summer minimum sea ice extent on record. Despite conducive conditions for its formation, the ozone hole at its maximum extent in September was near the 2000-18 mean, likely due to an ongoing slow decline in stratospheric chlorine monoxide concentration. Across the oceans, globally averaged SST decreased slightly since the record El Niño year of 2016 but was still far above the climatological mean. On average, SST is increasing at a rate of 0.10° ± 0.01°C decade−1 since 1950. The warming appeared largest in the tropical Indian Ocean and smallest in the North Pacific. The deeper ocean continues to warm year after year. For the seventh consecutive year, global annual mean sea level became the highest in the 26-year record, rising to 81 mm above the 1993 average. As anticipated in a warming climate, the hydrological cycle over the ocean is accelerating: dry regions are becoming drier and wet regions rainier. Closer to the equator, 95 named tropical storms were observed during 2018, well above the 1981-2010 average of 82. Eleven tropical cyclones reached Saffir-Simpson scale Category 5 intensity. North Atlantic Major Hurricane Michael's landfall intensity of 140 kt was the fourth strongest for any continental U.S. hurricane landfall in the 168-year record. Michael caused more than 30 fatalities and $25 billion (U.S. dollars) in damages. In the western North Pacific, Super Typhoon Mangkhut led to 160 fatalities and $6 billion (U.S. dollars) in damages across the Philippines, Hong Kong, Macau, mainland China, Guam, and the Northern Mariana Islands. Tropical Storm Son-Tinh was responsible for 170 fatalities in Vietnam and Laos. Nearly all the islands of Micronesia experienced at least moderate impacts from various tropical cyclones. Across land, many areas around the globe received copious precipitation, notable at different time scales. Rodrigues and Réunion Island near southern Africa each reported their third wettest year on record. In Hawaii, 1262 mm precipitation at Waipā Gardens (Kauai) on 14-15 April set a new U.S. record for 24-h precipitation. In Brazil, the city of Belo Horizonte received nearly 75 mm of rain in just 20 minutes, nearly half its monthly average. Globally, fire activity during 2018 was the lowest since the start of the record in 1997, with a combined burned area of about 500 million hectares. This reinforced the long-term downward trend in fire emissions driven by changes in land use in frequently burning savannas. However, wildfires burned 3.5 million hectares across the United States, well above the 2000-10 average of 2.7 million hectares. Combined, U.S. wildfire damages for the 2017 and 2018 wildfire seasons exceeded $40 billion (U.S. dollars).

Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here, we compile time series of snowmelt onset dates on perennial Antarctic sea ice from 1992 to 2014 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. Describing snow melt processes, we define two transition stages: A weak backscatter rise indicating the initial warming and metamorphosis of the snowpack (pre-melt), followed by a rapid rise indicating the onset of thaw-freeze cycles (snowmelt). Results show large interannual variability with average pre-melt and snowmelt onset dates of 29 November and 10 December, respectively, without any significant trends over the study period. Related to different signal frequencies, we show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. This offset has been considered when constructing the time series. Combining the observed successive timing of melt events retrieved from radar scatterometers with melt onset dates retrieved from 37 GHz passive microwave radiometers, allows us to develop a conceptual model which illustrates how the evolution of seasonal snow temperature profiles affects different microwave bands with different penetration depths. These suggest that future multi-frequency microwave satellite missions could be used to resolve melt processes throughout the vertical snow column.

Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. Around Antarctica, snowmelt on pack ice is weak and very different than in the Arctic, with most snow surviving the summer. It is therefore important to understand the mechanisms that drive snowmelt, both at different times of the year and in different regions around Antarctica. Doing so, we compile time series of snowmelt onset dates on perennial Antarctic sea ice from 1992 to 2014 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. Describing snow melt processes, we define two transition stages: A weak backscatter rise indicating the initial warming and metamorphism of the snowpack (pre-melt), followed by a rapid rise indicating the onset of thaw-freeze cycles in the interior snowpack (snowmelt). We compare these with pan-Antarctic temporary snowmelt onset dates in the uppermost snowpack retrieved from diurnal variations in the brightness temperatures from passive microwave (PMW) observations. Results show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. Snowmelt onset dates from the shortwave PMW observations (37 GHz) are later by 13 and 5 days than those from the scatterometers, respectively. Based on the observed successive timing of melt events retrieved from different sensors and microwave bands, we developed a conceptual model of the temporal evolution of snow temperature and metamorphism and their effect on different microwave wavelengths during the spring/summer transition. These results suggest that future multi-frequency microwave satellite missions could be used to resolve melt processes throughout the vertical snow column. Overall, results show that the magnitude and timing of seasonal and diurnal variations in Antarctic snow on sea ice are highly dependent on latitude, with earlier and more frequent snowmelt in the north. All retrieved melt onset dates show large interannual variability but no significant decadal trends.

Snow on sea ice alters the properties of the underlying ice cover as well as associated exchange processes at the interfaces between atmosphere, sea ice and ocean. As Antarctic snow cover persists during most of the year, it contributes significantly to the sea-ice mass budget due to physical (seasonal) transition processes within the snowpack. Therefore, several studies have shown the importance of comprehensive understanding of snow properties for large-scale estimates in the ice-covered oceans. However, field studies reveal not only a strong seasonality but especially spatial variations from small to large scales in the Antarctic snowpack. It is therefore the overall aim of the proposed project to locate and quantify internal snow melt, snow metamorphism, and snow-ice formation in the Antarctic snowpack on different spatial scales. Results will improve our understanding of processes and interactions in the snowpack as well as at the snow/ice interface associated with seasonal and inter-annual variations in the sea-ice mass budget of the Southern Ocean.

Objective: The present study investigated, whether computer-aided therapy in patients with Parkinson's disease is equivalent/non-inferior to conventional Lee Silvermann Voice Treatment (LSVT)-BIG-therapy in respect to motor outcome as measured by the Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) and quality of life as measured by the Parkinson's Disease Questionnaire (PDQ-39). Methods: In this controlled, rater-blinded study, 34 patients were included and 24 patients randomized to train seven standard exercises of the BIG-therapy either by a computer (BeBIG-group) or by a certified LSVT-BIG therapist (ThBIG-group) over four weeks. Equivalence was assessed by comparing the confidence interval of the BeBIG-group to the equivalence margin of the ThBIG-group. Results: There were no significant group differences in respect to age, disease duration, L-dopa equivalent daily dose or clinical stage of the disease. Both groups profited significantly from the therapy as demonstrated by an improvement in the MDS-UPDRS-III of 9.17 point in the BeBIG-group and of 8.92 points in the ThBIG-group. There was a non-significant decrease in the PDQ-39 of 9.23 points in the BeBIG-group and 4.23 points in the ThBIG-group. However, equivalence could not be demonstrated as the improvement of the BeBIG-group exceeded the confidence interval of the ThBIG-group. Conclusion: Physical training by a computer as well as by a therapist improves motor symptoms and quality of life in Parkinson's disease. Both therapies are not equivalent, superiority of the computerized training can however not be concluded, as the study was only designed to test for non-inferiority. Therefore, computerized training can be considered as an add-on-therapy.

Light transmittance through Arctic sea ice has an important impact on both the ocean heat content and the ice associated ecosystem. Thus, it is crucial to investigate the optical properties of sea ice to assess the role of the surface energy budget and its change due to climate change. Measurements of spectral transmittance can be used to investigate the influence of surface and ice properties regulating radiative transfer, especially on a larger horizontal scale. Here, we concentrate on categorizing snow and sea ice based on spectral transmittance data. Transmitted radiance and irradiance are measured at the underside of sea ice using a remotely operated vehicle (ROV). The scientific payload also includes CTD, fluorometer, pH-, nitrate-, oxygen-, attenuation sensor, upward-looking single-beam sonar, and periodically a surface and under ice trawl for assessing the spatio-temporal variability of sea ice algae. Thus, data for all disciplines in sea ice research can be recorded. The main benefits using the ROV compared to point measurements are the larger spatial coverage in comparably short times and the undisturbed sampling even under very thin sea ice, with parameters all collected during the same time. Snow depth is derived from a combination of terrestrial laser scanner data and manual measurements, while ice draft is measured using the single-beam sonar. Here, we present first data from the Last Ice campaign off Alert in May 2018. This region is dominated by sea ice with a larger thickness due to dynamic thickening. We investigated different ice regimes, such as First Year Ice with a continuous thickness of about 1.5 m and structured Multi Year Ice with thicknesses up to 6 m over the duration of four weeks to study the differences between various ice types.

Sea ice proxies are important tools to reconstruct the climate and environmental history in polar regions. The novel sea ice proxy for the Southern Ocean is the biomarker IPSO25 (Ice Proxy Southern Ocean with 25 carbon atoms), a highly branched isoprenoid (HBI) diene produced by sea ice diatoms. With this poster we present the first approach of a sea ice index derived from IPSO25, called PIPSO25, for past sea ice reconstructions in Antarctica.

The use of unmanned aerial system (UAS) imagery in environmental sciences has rapidly increased due to the ease of use and low cost [1]. In general, the ecological informatics community widely uses spatial data analyses, especially via GIS. A growing number of applications now include UAS imagery, which can provide centimetre scale multispectral data [2]. Processing this information requires the application of image-based remote sensing techniques. Specifically, the differences between unsupervised and supervised image classification methods are discussed, with a focus on pixel and object-based computational methods [3]. Examples relevant to ecological studies are presented using multispectral imagery collected of river landscapes to illustrate how UAS data can be used to classify complex spatial features such as vegetation and submerged regions of different depths, including turbulent flows and complex lighting and shade conditions. REFERENCES: 1. Arif, M.S.M., Gülch, E., Tuhtan, J.A., Thumser, P., Haas, C., 2016. An investigation of image processing techniques for substrate classification based on dominant grain size using RGB images from UAV. Int. J. Remote Sens. 0, 1–23. https://doi.org/10.1080/01431161.2016.1249309 2. Hugenholtz, C.H., Whitehead, K., Brown, O.W., Barchyn, T.E., Moorman, B.J., LeClair, A., Riddell, K., Hamilton, T., 2013. Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model. Geomorphology 194, 16–24. https://doi.org/10.1016/j.geomorph.2013.03.023 3. Black, M., Carbonneau, P., Church, M., Warburton, J., 2014. Mapping sub-pixel fluvial grain sizes with hyperspatial imagery. Sedimentology 61, 691–711. https://doi.org/10.1111/sed.12072

Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. While there is strong surface melt on Arctic sea ice, snowmelt on Antarctic sea ice is weak with most snow surviving the summer. Here, we compile time series of snowmelt onset dates on perennial Antarctic sea ice from 1992 to 2014 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. Describing snow melt processes, we define two transition stages: A weak backscatter rise indicating the initial warming and metamorphosis of the snowpack (pre-melt), followed by a rapid rise indicating the onset of thaw-freeze cycles (snowmelt). Results show large interannual variability with average pre-melt and snowmelt onset dates of 29 November and 10 December, respectively, without any significant trends over the study period. Related to different signal frequencies, we show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. This offset has been considered when constructing the time series. As different signal frequencies result in different penetration depths, we hypothesize that the different sensors respond to typical snowmelt processes in different depths within the snow cover.

Snow on sea ice alters the properties of the underlying ice cover as well as associated exchange processes at the interfaces between atmosphere, sea ice, and ocean. As Antarctic snow cover persists during most of the year, it contributes significantly to the sea-ice mass budget due to comprehensive physical (seasonal) transition processes within the snowpack. It is therefore necessary to locate and quantify internal snowmelt, snow metamorphism, and snow-ice formation in the Antarctic snowpack on different spatial scales. Doing so, we present here, on the one hand, in-situ observations of physical snow properties from point measurements and transect lines during recent expeditions in the Weddell Sea from 2013 to 2018, covering summer and winter conditions. On the other hand, we used passive microwave (19 and 37 GHz signal frequency) as well as scatteroemter observations (5.6 and 13.4 GHz signal frequency) to describe snowmelt processes on Antarctic-wide scales. As different signal frequencies result in different penetration depths, we hypothesize that the different sensors respond to snow melt processes in different depths within the snow cover. The observed differences are consistent with the effects of short- and longwave radiation transmission and absorption, and the related occurrence of strong snow metamorphism, internal snow melt, and superimposed ice formation known to be important on Antarctic sea ice. Results of the study will improve our understanding on processes and interactions in the snowpack as well as at the snow/ice interface associated with seasonal and inter-annual variations in the sea-ice energy and mass budgets of the Southern Ocean.