3,880 research outputs found
Investigating the dynamics of Greenland's glacier-fjord systems
Over the past two decades, Greenlandâs tidewater glaciers have dramatically retreated, thinned and accelerated, contributing significantly to sea level rise. This change in glacier behaviour is thought to have been triggered by increasing atmospheric and ocean temperatures, and mass loss from Greenlandâs tidewater glaciers is predicted to continue this century. Substantial research during this period of rapid glacier change has improved our understanding of Greenlandâs glacier-fjord systems. However, many of the processes operating in these systems that ultimately control the response of tidewater glaciers to changing atmospheric and oceanic conditions are poorly understood. This thesis combines modelling and remote sensing to investigate two particularly poorly-understood components of glacier-fjord systems, with the ultimate aim of improving understanding of recent glacier behaviour and constraining the stability of the ice sheet in a changing climate.
The research presented in this thesis begins with an investigation into the dominant controls on the seasonal dynamics of contrasting tidewater glaciers draining the Greenland Ice Sheet. To do this, high resolution estimates of ice velocity were generated and compared with detailed observations and modelling of the principal controls on seasonal glacier flow, including terminus position, ice mélange presence or absence, ice sheet surface melting and runoff, and plume presence or absence. These data revealed characteristic seasonal and shorter-term changes in ice velocity at each of the study glaciers in more detail than was available from previous remote sensing studies. Of all the environmental controls examined, seasonal evolution of subglacial hydrology (as inferred from plume observations and modelling) was best able to explain the observed ice flow variations, despite differences in geometry and flow of the study glaciers. The inferred relationships between subglacial hydrology and ice dynamics were furthermore entirely consistent with process-understanding developed at land-terminating sectors of the ice sheet. This investigation provides a more detailed understanding of tidewater glacier subglacial hydrology and its interaction with ice dynamics than was previously available and suggests that interannual variations in meltwater supply may have limited influence on annually averaged ice velocity.
The thesis then shifts its attention from the glacier part of the system into the fjords, focusing on the interaction between icebergs, fjord circulation and fjord water properties. This focus on icebergs is motivated by recent research revealing that freshwater produced by iceberg melting constitutes an important component of fjord freshwater budgets, yet the impact of this freshwater on fjords was unknown. To investigate this, a new model for iceberg-ocean interaction is developed and incorporated into an ocean circulation model.
This new model is first applied to Sermilik Fjord â a large fjord in east Greenland that hosts Helheim Glacier, one of the largest tidewater glaciers draining the ice sheet â to further constrain iceberg freshwater production and to quantify the influence of iceberg melting on fjord circulation and water properties. These investigations reveal that iceberg freshwater flux increases with ice sheet runoff raised to the power ~0.1 and ranges from ~500-2500 mÂł sâ»Âč during summer, with ~40% of that produced below the pycnocline. It is also shown that icebergs substantially modify the temperature and velocity structure of Sermilik Fjord, causing 1-5°C cooling in the upper ~100 m and invigorating fjord circulation, which in turn causes a 10-40% increase in oceanic heat flux towards Helheim Glacier. This research highlights the important role of icebergs in Greenlandâs iceberg congested fjords and therefore the need to include them in future studies examining ice sheet â ocean interaction.
Having investigated the effect of icebergs on fjord circulation in a realistic setting, this thesis then characterises the effect of submarine iceberg melting on water properties near the ice sheet â ocean interface by applying the new model to a range of idealised scenarios. This near-glacier region is one which is crucial for constraining ocean-driven retreat of tidewater glaciers, but which is poorly-understood. The simulations show that icebergs are important modifiers of glacier-adjacent water properties, generally acting to reduce vertical variations in water temperature. The iceberg-induced temperature changes will generally increase submarine melt rates at mid-depth and decrease rates at the surface, with less pronounced effects at greater depth. This highlights another mechanism by which iceberg melting can affect ice sheet â ocean interaction and emphasises the need to account for iceberg-ocean interaction when simulating ocean-driven retreat of Greenlandâs tidewater glaciers.
In summary, this thesis has helped to provide a deeper understanding of two poorly-understood components of Greenlandâs tidewater glacier-fjord systems: (i) interactions between subglacial hydrology and ice velocity, and; (ii) iceberg-ocean interaction. This research has enabled more precise interpretations of past glacier behaviour and can be used to inform model development that will help constrain future ice sheet mass loss in response to a changing climate."I must express my gratitude to the University of St Andrews and to the Scottish Alliance for Geoscience, Environment and Society (SAGES) for funding and supporting me as a research student."-- Fundin
Flood dynamics derived from video remote sensing
Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models.
Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science
In situ estimation of ice crystal properties at the South Pole using LED calibration data from the IceCube Neutrino Observatory
The IceCube Neutrino Observatory instruments about 1âkm3 of deep, glacial ice at the geographic South Pole. It uses 5160 photomultipliers to detect Cherenkov light emitted by charged relativistic particles. An unexpected light propagation effect observed by the experiment is an anisotropic attenuation, which is aligned with the local flow direction of the ice. We examine birefringent light propagation through the polycrystalline ice microstructure as a possible explanation for this effect. The predictions of a first-principles model developed for this purpose, in particular curved light trajectories resulting from asymmetric diffusion, provide a qualitatively good match to the main features of the data. This in turn allows us to deduce ice crystal properties. Since the wavelength of the detected light is short compared to the crystal size, these crystal properties include not only the crystal orientation fabric, but also the average crystal size and shape, as a function of depth. By adding small empirical corrections to this first-principles model, a quantitatively accurate description of the optical properties of the IceCube glacial ice is obtained. In this paper, we present the experimental signature of ice optical anisotropy observed in IceCube light-emitting diode (LED) calibration data, the theory and parameterization of the birefringence effect, the fitting procedures of these parameterizations to experimental data, and the inferred crystal properties.</p
Vortex streets to the lee of Madeira in a kilometre-resolution regional climate model
Atmospheric vortex streets are a widely studied dynamical effect of isolated mountainous islands. Observational evidence comes from case studies and satellite imagery, but the climatology and annual cycle of vortex shedding are often poorly understood. Using the non-hydrostatic limited-area COSMO model driven by the ERA-Interim reanalysis, we conducted a 10-year-long simulation over a mesoscale domain covering the Madeira and Canary archipelagos at high spatial (grid spacing of 1 km) and temporal resolutions. Basic properties of vortex streets were analysed and validated through a 6 d long case study in the lee of Madeira Island. The simulation compares well with satellite and aerial observations and with existing literature on idealised simulations. Our results show a strong dependency of vortex shedding on local and synoptic-flow conditions, which are to a large extent governed by the location, shape and strength of the Azores high. As part of the case study, we developed a vortex identification algorithm. The algorithm is based on a set of criteria and enabled us to develop a climatology of vortex shedding from Madeira Island for the 10-year simulation period. The analysis shows a pronounced annual cycle with an increasing vortex-shedding rate from April to August and a sudden decrease in September. This cycle is consistent with mesoscale wind conditions and local inversion height patterns
Assimilation of Meteosat Third Generation (MTG) Lightning Imager (LI) pseudo-observations in AROME-France â proof of concept
This study develops a lightning data assimilation (LDA) scheme for the regional, convection-permitting numerical weather prediction (NWP) model AROME-France. The LDA scheme intends to assimilate total lightning, i.e., cloud-to-ground (CG) and inter- and intra-cloud (IC), of the future Meteosat Third Generation (MTG) Lightning Imager (LI; MTG-LI). MTG-LI proxy data are created, and flash extent density (FED) fields are derived.
An FED forward observation operator (FFO) is trained based on modeled, column-integrated graupel mass from 24 storm days in 2018. The FFO is successfully verified for 2 independent storm days.
With the FFO, the LDA adapts a 1-dimensional Bayesian (1DBay) retrieval followed by a 3-dimensional variational (3DVar) assimilation approach that is currently run operationally in AROME-France for radar reflectivity data. The 1DBay retrieval derives relative humidity profiles from the background by comparing the FED observations to the FED inferred from the background. Retrieved relative humidity profiles are assimilated as sounding data.
The evaluation of the LDA comprises different LDA experiments and four case studies. It is found that all LDA experiments can increase the background integrated water vapor (IWV) in regions where the observed FED exceeds the FED inferred from AROME-France outputs. In addition, IWV can be reduced where spurious FED is modeled. A qualitative analysis of 6âh accumulated rainfall fields reveals that the LDA is capable of locating and initiating some local precipitation fields better than a radar data assimilation (RDA) experiment. However, the LDA also leads to rainfall accumulations that are too high at some locations. Fractions skill scores (FSSs) of 6âh accumulated rainfall are overall similar for the developed LDA and RDA experiments. An approach aiming at mitigating effects due to differences in the optical extents of lightning flashes and the area of the corresponding cloud was developed and included in the LDA; however, it does not always improve the FSS.</p
Laboratory Experiments to Understand Comets
In order to understand the origin and evolution of comets, one must decipher
the processes that formed and processed cometary ice and dust. Cometary
materials have diverse physical and chemical properties and are mixed in
various ways. Laboratory experiments are capable of producing simple to complex
analogues of comet-like materials, measuring their properties, and simulating
the processes by which their compositions and structures may evolve. The
results of laboratory experiments are essential for the interpretations of
comet observations and complement theoretical models. They are also necessary
for planning future missions to comets. This chapter presents an overview of
past and ongoing laboratory experiments exploring how comets were formed and
transformed, from the nucleus interior and surface, to the coma. Throughout
these sections, the pending questions are highlighted, and the perspectives and
prospects for future experiments are discussed.Comment: 36 pages, 13 figures, Chapter accepted for publication on February
24th 2023, now in press for the book Comets III, edited by K. Meech, M.
Combi, D. Bockelee-Morvan, S. Raymond and M. Zolensky, University of Arizona
Pres
Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability
Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far.
In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs.
We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design â one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases â one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes.
We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well
The use of Frequency domain Electro-magnetometer for the characterization of permafrost and ice layers.
openSince the industrial revolution human activities caused a record-breaking increase in the Earthâs average temperature due to the extensive use of greenhouse gases. [1] As global temperatures increase; glaciers have undergone a significant retreat in the past few decades.[2]
The Ice Memory project aims to preserve ice cores from glaciers worldwide, as a record of Earth's past climate. It involves drilling deep into glaciers, extracting ice cores, and storing them in a dedicated facility in Antarctica.
This is to prevent the potential loss of valuable climate archives due to glacier retreat which provides future scientists with valuable information for studying historical climate patterns and understanding the role of human activity in climate change.
geophysical investigations are typically required to determine the most suitable drilling positions for ice coring. the most common technique for this purpose is the so-called GPR. (Snow cover of several meters limits the use of ERT and active seismic methods.)
While each geophysical technique has certain advantages and limitations, combining them can provide a more detailed picture of changes within rock glaciers.
In the present study, electromagnetic prospecting in the frequency domain (FDEM) was performed together with the ground penetration radar (GPR). The former is not a commonly used method for studying glacier environments as FDEM has a lower resolution in the study of glaciers with respect to the GPR. However, as we will see in this study, it is a quick and convenient method to study this type of environment, as it provides a large coverage area in a cost-efficient manner, although with a lower resolution with respect to the GPR. Combining these two techniques provide a more detailed map of the glaciers. comparing the GPR and borehole data with the inverted FDEM datasets (CMD-DUO, GF-Instruments) confirms the effectiveness and applicability of FDEM methodology for investigating glacial bodies in mountainous regions.Since the industrial revolution human activities caused a record-breaking increase in the Earthâs average temperature due to the extensive use of greenhouse gases. [1] As global temperatures increase; glaciers have undergone a significant retreat in the past few decades.[2]
The Ice Memory project aims to preserve ice cores from glaciers worldwide, as a record of Earth's past climate. It involves drilling deep into glaciers, extracting ice cores, and storing them in a dedicated facility in Antarctica.
This is to prevent the potential loss of valuable climate archives due to glacier retreat which provides future scientists with valuable information for studying historical climate patterns and understanding the role of human activity in climate change.
geophysical investigations are typically required to determine the most suitable drilling positions for ice coring. the most common technique for this purpose is the so-called GPR. (Snow cover of several meters limits the use of ERT and active seismic methods.)
While each geophysical technique has certain advantages and limitations, combining them can provide a more detailed picture of changes within rock glaciers.
In the present study, electromagnetic prospecting in the frequency domain (FDEM) was performed together with the ground penetration radar (GPR). The former is not a commonly used method for studying glacier environments as FDEM has a lower resolution in the study of glaciers with respect to the GPR. However, as we will see in this study, it is a quick and convenient method to study this type of environment, as it provides a large coverage area in a cost-efficient manner, although with a lower resolution with respect to the GPR. Combining these two techniques provide a more detailed map of the glaciers. comparing the GPR and borehole data with the inverted FDEM datasets (CMD-DUO, GF-Instruments) confirms the effectiveness and applicability of FDEM methodology for investigating glacial bodies in mountainous regions
Optical Measurement of Airborne Particles on Unmanned Aircraft
Aerosols and clouds are persistent causes of uncertainty in climate and weather models,
which is due to their heterogeneous suspension and occurrence within the atmosphere, and
complex interactions which are chaotic and exist on small scales. Unmanned aerial vehicles
(UAVs) have grown in popularity, and are becoming more commonly used for general atmospheric
measurement, particularly measurement of aerosols and clouds. This thesis presents
and evaluates a synergy between two UAVs, a multi-rotor: the UH-AeroSAM octocopter and
a fixed-wing: the FMI-Talon, and an optical particle instrument: the Universal Cloud and
Aerosol Sounding System. Computational fluid dynamics with Lagrangian particle tracking
(CFD-LPT) was used as a tool for the characterisation of the velocity fields and particle
trajectories around both UAVs. In both instances CFD-LPT was used to develop an operational
envelope, with particular attention to angle of attack constraints and size distribution
perturbation, for the UAV â instrument synergy. UCASS was the first open path instrument
to be used on a UAV, and a good case has been made for its continued use, particularly
on fixed-wing UAVs, which exhibit less complex aerodynamics and superior stability in the
induced sampling airflow through the instrument
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