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TRaNSMIT - A towable RF system for non-invasive sensing and measurement of Arctic sea ice thickness

Lead Research Organisation: UNIVERSITY OF MANCHESTER
Department Name: Electrical and Electronic Engineering

Abstract

Sea ice is a key indicator of climate change and as such is one of the World Meteorological Institute's Essential Climate Variables (ECV). Monitoring ECV are deemed essential by the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC) as these datasets provide the empirical evidence to understand and predict the evolution of climate, to guide mitigation and adaptation measures, to assess risks and enable attribution of climate events to underlying causes, and to underpin climate services. However, knowledge of the thickness of sea ice lags other ECVs as currently we can currently only measure it conducting labour-intensive surveys such as drilling for samples. This approach lacks the speed and scalability of non-destructive sensing methods, which have the potential to yield a far greater insight into ice thickness trends over much larger areas as a result.

The Arctic is warming at more than twice as fast as any other region of our planet and possibly the most dramatic changes are those associated with Arctic sea ice. Satellite records have revealed a significant decrease in sea ice extent in all months, especially in summer. There has been a reduction in summer sea ice extent from about 7 million km2 in the late 1970s to around 3.4 million km2 in 2012; a reduction of over 50%. Complex computer models predict that the Arctic Ocean is on track to become ice free in summer in the 2030s.

Whilst the decaying sea ice cover opens up opportunities for economic development in the Arctic, such as exploitation of natural resources, fisheries, tourism and shipping, its loss has immediate implications for the sustainability of many northern local and indigenous communities, their economies, health and well-being. In many ways, sea ice can be viewed as the glue that binds these northern communities together because it is utilised both for commercial (hunting/fishing) and social (transport network) means. However, the sea ice is changing; annually, it is melting earlier and forming later and as a result it is becoming thinner and less stable. These dramatic changes impact the safety of people on the ice, but also the hunting ability of the Inuit, thus threatening the cultural survival of these people. It is therefore imperative to further advance the monitoring of sea ice thickness through the development of more accurate and easier to use monitoring systems. A low-cost ability to accurately quantify the thickness of sea ice with a high resolution can benefit a range of stakeholders, from the wider scientific community, through to industry and to local and indigenous Arctic communities.

Presently there is no single modality of sensing technology which can return accurate, high resolution, sea ice thickness measurements under a range of sea ice conditions. The central hypothesis of this proposal is that there is potential to affect a step change in our capability to measure sea ice thickness by fusing multiple sensor modalities, and by investigating the potential for novel forms of signal processing and detection algorithms, including the implementation of inversion techniques. This will include the ability to separately quantify the total snow depth, and sea ice thickness, with further confidence information being returned to quantify any ambiguities caused by the presence of brine pockets.

The proposed programme of research will investigate this hypothesis by implementing a dual modality sensor consisting of both electromagnetic induction and ground-penetrating radar technologies. These modalities are sensitive to different aspects of the environment, and together can be used to determine sea ice thickness non-invasively and through routine transit over the ice. Our goal is to be able to provide processing methods and appropriate instrumentation which has the potential to collect data on a scale which is impossible to realise using current methods.
 
Title AR and sand-pit measurements for polar antenna design 
Description Dataset for an investigation into the measurement of snow and sea ice using different antenna polarizations. Sea ice is a dispersive and anisotropic medium that can be significantly attenuating at microwave frequencies. An Arctic environment was simulated in CST and emulated in an experiment using sand, clay and road aggregate. The dataset includes data from simulations, characterization of the axial ratio, noise and S-parameters of prototype spiral antennas, and the measurement of the experimental surrogate Arctic environment the prototype spiral antennas and Vivaldi dual-polarized horn antennas at horizontal and vertical polarizations.Structure:XY_Data folder: contains S-Parameter and positional data from different trial runs of clay buried under sand with spiral antennas and the horn antennas. noRAM, closeRAM, farRAM, dualRAM fodlers: contains S21 data at different angles for inversion to axial ratio (AR) for different RAM configurations for the spiral antennas (no RAM backing, backing at 2 mm, backing at 40 mm and backing at both 2 and 40 mm)archSpiral2_sparams_corrected: S-Parameters measured of the spiral antennas directly couplednoise_archSpiral2_corrected: VNA noise floorimpulse2: input signal to the antennasvertpot, horiport, spiralport, spiralortport: simulated data from CST regarding the measurement of anisotropic sea ice with vertically and horizontally polarized antennasPermittivity_ice_case_8: data characterizing the electrical behaviour of first year Arctic sea ice at -20 degrees C with spherical brine inclusions used to define the simulated sea ice in CST 
Type Of Material Database/Collection of data 
Year Produced 2025 
Provided To Others? Yes  
URL https://figshare.manchester.ac.uk/articles/dataset/AR_and_sand-pit_measurements_for_polar_antenna_de...
 
Title Approximating the performance of a time domain pulsed induction EMI sensor with multiple frequency domain FEM simulations for improved modelling of Arctic EMI sensing of ice thickness 
Description One of the key challenges with developing pulsed induction (PI) electromagnetic induction (EMI) sensors for use in the arctic is the inaccessibility of the environment, which makes in-situ testing prohibitively expensive. To mitigate this, sensor development can be streamlined through creation of a robust simulation strategy with which to optimize features such as coil turns and geometry. Building on work which previously presented a method for simulating an arctic PI sensor via a time domain finite element model (FEM), this paper presents a method for approximating a time-domain simulation with multiple frequency domain simulations. A comparison between the fast Fourier transform (FFT) of a time domain simulation, a collection of frequency domain simulations is presented. These are validated against empirical data with a PI sensor over seawater, with an air gap used as proxy for sea ice. Using the method described, a range of coils have been simulated with dimensions from 0.5 × 0.5 m up to 1.0 × 2.0 m, which demonstrates the ability of this approach to allow a comparison of sensor performance over a wider parameter space. For a parametric sweep over 10 sensor-to-seawater lift off distances, the improvement from a time domain simulation (of a 402 µs window) to a frequency domain simulation (comprising 100 discrete frequencies) represents a reduction in simulation time from 38,013 minutes to 141 minutes. 
Type Of Material Database/Collection of data 
Year Produced 2025 
Provided To Others? Yes  
URL https://figshare.manchester.ac.uk/articles/dataset/Approximating_the_performance_of_a_time_domain_pu...