Landfast sea ice formation and deformation near Barrow, Alaska: variability and implications for ice stability

Thesis (M.S.) University of Alaska Fairbanks, 2013Climate change in the Arctic is having large and far-reaching effects. Sea ice is declining in annual extent and thinning with a warming of the atmosphere and the ocean. As a result, sea ice dynamic behaviour and processes are undergoing major changes, interacting with socio-economic changes underway in the Arctic. Near Barrow, Alaska, landfast sea ice is an integral part of native lñupiaq culture and impacts the natural resource extraction and maritime industries. Events known as breakouts of the landfast ice, in which stable landfast ice becomes mobile and detaches from the coast, have been occurring more frequently in recent years in northern Alaska. The current study investigates processes contributing to breakout events near Barrow, and environmental conditions related to the detachment of landfast sea ice from the coast. In this study, synoptic scale sea level pressure patterns are classified in an attempt to identify atmospheric preconditioning and drivers of breakout events. An unsupervised classification approach, so called Self-Organizing Maps, is employed to sort daily sea level pressure distributions across the study area into commonly observed patterns. The results did not point to any particular distributions which favored the occurrence of breakouts. Because of the comparatively small number of breakout events tracked at Barrow to date (nine events between 2006 and 2010), continued data collection may still yield data that support a relationship between breakout events and large scale sea level pressure distributions. Two case studies for breakout events in the 2008/09 and 2009/10 ice seasons help identify contributing and controlling factors for shorefast ice fragmentation and detachment. Observational data, primarily from components of the Barrow Sea Ice Observatory, are used to quantify stresses acting upon the landfast ice. The stability of the landfast ice cover is estimated through the calculation of the extent of grounded pressure ridges, which are stabilizing features of landfast ice. Using idealized ridge geometries and convergence derived from velocity fields obtained by coastal radar, effective grounding depths can be calculated. Processes acting to destabilize or precondition the ice cover are also observed. For a medium-severity breakout that occurred on March 24, 2010, the calculated atmospheric and oceanic stresses on the landfast ice overcame the estimated grounding strength of ridge keels, although interaction with rapidly moving pack ice cannot be ruled out as the primary breakout cause. For another medium-severity breakout that took place on February 27, 2009, the landfast ice was preconditioned by reducing the draft of grounded ridge keels, with subsequent detachment from the shore during the next period of oceanic and atmospheric conditions favoring a breakout. For both of these breakouts, in addition to their potential role in destabilizing the landfast ice by overcoming the ridge grounding strength, current and/or wind forcing on the landfast ice were found to be important factors in moving the stationary ice away from shore.Chapter 1. Introduction to Barrow, Alaska and local sea ice conditions -- 1.1. Introduction -- 1.2. Barrow, Alaska and local sea ice conditions -- 1.3. The Barrow sea ice observatory -- 1.4. Thesis overview -- Chapter 2. Using self-organizing maps to identify regional weather patterns contributing to landfast sea ice breakouts near Barrow, Alaska -- 2.1. Introduction -- 2.2. Purpose -- 2.3. Background on self-organizing maps -- 2.4. Methods and data -- 2.5. Results -- 2.6. Discussion -- 2.7. Conclusions -- Chapter 3. Two case studies of landfast sea ice breakouts near Barrow, Alaska -- 3.1. Introduction -- 3.2. Background -- 3.2.1 Drift and dynamics of sea ice -- 3.2.2. Breakout events: ridge failure -- 3.2.3. Breakout events: failure in tension -- 3.2.4. Changes in sea level -- 3.3. Data for breakout case studies -- 3.3.1. Sea ice mass balance site -- 3.3.2. Marine radar and webcam -- 3.3.3. Satellite products -- 3.3.4. Offshore moorings -- 3.3.5. Local ice observations -- 3.4. Methods -- 3.4.1. Detection of breakout events -- 3.4.2. Tracking sea ice through radar imagery -- 3.4.3. Estimation of grounded ridge extent -- 3.5. Breakout events -- 3.5.1. February 27, 2009 breakout event: pre-breakout ice conditions -- 3.5.2. February 27, 2009 breakout event: conditions during the breakout event -- 3.5.3. February 27, 2009 breakout event: discussion -- 3.5.4. March 24, 2010 breakout event: pre-breakout ice conditions -- 3.5.5. March 24, 2010 breakout event: conditions during the breakout event -- 3.5.6. March 24, 2010 breakout event: discussion -- 3.6. Discussion of errors -- 3.7. Conclusions -- Chapter 4. Landfast sea ice breakout events: general conclusions -- List of symbols -- References

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Generalized CMAC adaptive ensembles for concept-drifting data streams

In this paper we propose to use an adaptive ensemble learning framework with different levels of diversity to handle streams of data in non-stationary scenarios in which concept drifts are present. Our adaptive system consists of two ensembles, each one with a different level of diversity (from high to low), and, therefore, with different and complementary capabilities, that are adaptively combined to obtain an overall system of improved performance. In our approach, the ensemble members are generalized CMACs, a linear-in-the-parameters network. The ensemble of CMACs provides a reasonable trade-off between expressive power, simplicity, and fast learning speed. At the end of the paper, we provide a performance analysis of the proposed learning framework on benchmark datasets with concept drifts of different levels of severity and speed.This work is partially funded by grant CASI-CAM-CM (S2013/ICE-2845), DGUI-Comunidad de Madrid, and grants DAMA (TIN2015-70308-REDT), MINECO, and Macro-ADOBE (TEC 2015-67719-P), MINECO-FEDER-EU

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Development and Applications of Machine Learning Methods for Hyperspectral Data

Die hyperspektrale Fernerkundung der Erde stützt sich auf Daten passiver optischer Sensoren, die auf Plattformen wie Satelliten und unbemannten Luftfahrzeugen montiert sind. Hyperspektrale Daten umfassen Informationen zur Identifizierung von Materialien und zur Überwachung von Umweltvariablen wie Bodentextur, Bodenfeuchte, Chlorophyll a und Landbedeckung. Methoden zur Datenanalyse sind erforderlich, um Informationen aus hyperspektralen Daten zu erhalten. Ein leistungsstarkes Werkzeug bei der Analyse von Hyperspektraldaten ist das Maschinelle Lernen, eine Untergruppe von Künstlicher Intelligenz. Maschinelle Lernverfahren können nichtlineare Korrelationen lösen und sind bei steigenden Datenmengen skalierbar. Jeder Datensatz und jedes maschinelle Lernverfahren bringt neue Herausforderungen mit sich, die innovative Lösungen erfordern. Das Ziel dieser Arbeit ist die Entwicklung und Anwendung von maschinellen Lernverfahren auf hyperspektrale Fernerkundungsdaten. Im Rahmen dieser Arbeit werden Studien vorgestellt, die sich mit drei wesentlichen Herausforderungen befassen: (I) Datensätze, welche nur wenige Datenpunkte mit dazugehörigen Ausgabedaten enthalten, (II) das begrenzte Potential von nicht-tiefen maschinellen Lernverfahren auf hyperspektralen Daten und (III) Unterschiede zwischen den Verteilungen der Trainings- und Testdatensätzen. Die Studien zur Herausforderung (I) führen zur Entwicklung und Veröffentlichung eines Frameworks von Selbstorganisierten Karten (SOMs) für unüberwachtes, überwachtes und teilüberwachtes Lernen. Die SOM wird auf einen hyperspektralen Datensatz in der (teil-)überwachten Regression der Bodenfeuchte angewendet und übertrifft ein Standardverfahren des maschinellen Lernens. Das SOM-Framework zeigt eine angemessene Leistung in der (teil-)überwachten Klassifikation der Landbedeckung. Es bietet zusätzliche Visualisierungsmöglichkeiten, um das Verständnis des zugrunde liegenden Datensatzes zu verbessern. In den Studien, die sich mit Herausforderung (II) befassen, werden drei innovative eindimensionale Convolutional Neural Network (CNN) Architekturen entwickelt. Die CNNs werden für eine Bodentexturklassifikation auf einen frei verfügbaren hyperspektralen Datensatz angewendet. Ihre Leistung wird mit zwei bestehenden CNN-Ansätzen und einem Random Forest verglichen. Die beiden wichtigsten Erkenntnisse lassen sich wie folgt zusammenfassen: Erstens zeigen die CNN-Ansätze eine deutlich bessere Leistung als der angewandte nicht-tiefe Random Forest-Ansatz. Zweitens verbessert das Hinzufügen von Informationen über hyperspektrale Bandnummern zur Eingabeschicht eines CNNs die Leistung im Bezug auf die einzelnen Klassen. Die Studien über die Herausforderung (III) basieren auf einem Datensatz, der auf fünf verschiedenen Messgebieten in Peru im Jahr 2019 erfasst wurde. Die Unterschiede zwischen den Messgebieten werden mit qualitativen Methoden und mit unüberwachten maschinellen Lernverfahren, wie zum Beispiel Principal Component Analysis und Autoencoder, analysiert. Basierend auf den Ergebnissen wird eine überwachte Regression der Bodenfeuchte bei verschiedenen Kombinationen von Messgebieten durchgeführt. Zusätzlich wird der Datensatz mit Monte-Carlo-Methoden ergänzt, um die Auswirkungen der Verschiebung der Verteilungen des Datensatzes auf die Regression zu untersuchen. Der angewandte SOM-Regressor ist relativ robust gegenüber dem Rauschen des Bodenfeuchtesensors und zeigt eine gute Leistung bei kleinen Datensätzen, während der angewandte Random Forest auf dem gesamten Datensatz am besten funktioniert. Die Verschiebung der Verteilungen macht diese Regressionsaufgabe schwierig; einige Kombinationen von Messgebieten bilden einen deutlich sinnvolleren Trainingsdatensatz als andere. Insgesamt zeigen die vorgestellten Studien, die sich mit den drei größten Herausforderungen befassen, vielversprechende Ergebnisse. Die Arbeit gibt schließlich Hinweise darauf, wie die entwickelten maschinellen Lernverfahren in der zukünftigen Forschung weiter verbessert werden können

The drumlin problem : streamlined subglacial bedforms in southern Sweden

This thesis investigates stream-lined subglacial bedforms (often referred to as drumlins) in southern Sweden. The broad aim of this is to contribute to the solution of the ‘drumlin problem’. The term drumlin has come to be applied to a wide range of features whose internal architecture (core) and overall morphology are seen to vary greatly. This range in characteristics is in part responsible for the various competing theories of drumlin formation, each different type of core and morphology generating a new idea for how it came about. Here the new Swedish national height model, a high resolution LiDAR derived digital elevation model, in combination with detailed sedimentological work is used to characterise streamlined terrain in southern Sweden and investigate the formation processes associated with it. The findings of this are that drumlinoids in southern Sweden are predominantly rock cored. Soft cored features tend to be significantly longer than rock cored features. In general, drumlinoids in southern Sweden are located at the lower end of the size spectrum in terms of global streamlined sub-glacial features. Additionally it has been found that drumlinoids can form rapidly at glacial margins as well as within the main body of ice sheets. And finally, the most important contextual geological factor in drumlinoid parameter (morphology) formation appears to be drift depth/properties. The bedrock type beneath a feature and the hydrological system as recorded in eskers do play a role, but the exact nature of this is not certain and the correlations are difficult to analyse. In addition to these findings a generalised conceptual model of drumlinoid formation is proposed and a discussion of the possible ways in which physical processes influence said formation is offered. It is suggested that chaotic behaviour and the role of scale might be useful to consider and that whilst it is something of semantic point, the use of the term drumlinoid is deliberate and important. This is because due to equifinality there are many landforms that researchers can split into different categories, e.g. rock drumlins, clone drumlins, emergent drumlins, downwards emergent drumlins or obstacle drumlins. These are all valid divisions as there are different physical processes involved in their formation. However these processes and the final landforms that result from them are all part of the sub-glacial continuum and so at one level must be considered part of the same family

Frost monitoring cyber-physical system: a survey on prediction and active protection methods

Frost damage in broadacre cropping and horticulture (including viticulture) results in substantial economic losses to producers and may also disrupt associated product value chains. Frost risk windows are changing in timing, frequency, and duration. Faced with the increasing cost of mitigation infrastructure and competition for resources (e.g., water and energy), multiperil insurance, and the need for supply chain certainty, producers are under pressure to innovate in order to manage and mitigate risk. Frost protection systems are cyber-physical systems (CPSs) consisting of sensors (event detection), intelligence (prediction), and actuators (active protection methods). The Internet-of-Things communication protocols joining the CPS components are also evaluated. In this context, this article introduces and reviews existing methods of frost management. This article focuses on active protection methods because of their potential for real-time deployment during frost events. For integrated frost prediction and active protection systems, prediction method, sensor types, and integration architecture are assessed, research gaps are identified and future research directions proposed

Stochastic resonance in the recovery of signal from agent price expectations

Contributions that noise can make to the objective of detecting signal in agent expectations for price in financial markets are examined. Although contrary to most assumptions on exogenous noise in financial markets as increasing both risk and uncertainty in the detection of signal, a basis for the contribution that noise can have to agent objectives in signal detection through stochastic resonance (SR) is well-documented across disciplines. After reviewing foundations for the micro-processing of expectations, a multi-component model of networked agents that includes a component of bounded rational processing and a component that has been cited as generating “herding” behavior in financial markets is offered. The signal-to-noise ratios in the proposed models provide a basis to investigate SR in an application to financial markets. Results with both deterministic and stochastic forms of the proposed model support SR as a process in which randomness can contribute to the recovery of signal in agent expectation. Additionally, predictive models that indicate the sensitivity of the occurrence of SR to the parameters of the models of agent expectations were estimated and cross-validated. The discriminative ability of the models is reported through Area Under the Receiver Operating Curve (AUROC) methodology. These results extend the cross-discipline demonstrations of SR to models of price in financial markets.</p