The challenge of real-time automatic mapping for environmental monitoring network management

The automatic interpolation of environmental monitoring network data such as air quality or radiation levels in real-time setting poses a number of practical and theoretical questions. Among the problems found are (i) dealing and communicating uncertainty of predictions, (ii) automatic (hyper)parameter estimation, (iii) monitoring network heterogeneity, (iv) dealing with outlying extremes, and (v) quality control. In this paper we discuss these issues, in light of the spatial interpolation comparison exercise held in 2004

Contrasting the modelled sensitivity of the Amundsen Sea Embayment ice streams

Present-day mass loss from the West Antarctic ice sheet is centred on the Amundsen Sea Embayment (ASE), primarily through ice streams, including Pine Island, Thwaites and Smith glaciers. To understand the differences in response of these ice streams, we ran a perturbed parameter ensemble, using a vertically-integrated ice flow model with adaptive mesh refinement. We generated 71 sets of three physical parameters (basal traction coefficient, ice viscosity stiffening factor and sub-shelf melt rate), which we used to simulate the ASE for 50 years. We also explored the effects of different bed geometries and basal sliding laws. The mean rate of sea-level rise across the ensemble of simulations is comparable with current observed rates for the ASE. We found evidence that grounding line dynamics are sensitive to features in the bed geometry: simulations using BedMap2 geometry resulted in a higher rate of sea-level rise than simulations using a rougher geometry, created using mass conservation. Modelled grounding-line retreat of all the three ice streams was sensitive to viscosity and basal traction, while the melt rate was more important in Pine Island and Smith glaciers, which flow through more confined ice shelves than Thwaites, which has a relatively unconfined shelf

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities

Modelling frontal discontinuities in wind fields

A Bayesian procedure for the retrieval of wind vectors over the ocean using satellite borne scatterometers requires realistic prior near-surface wind field models over the oceans. We have implemented carefully chosen vector Gaussian Process models; however in some cases these models are too smooth to reproduce real atmospheric features, such as fronts. At the scale of the scatterometer observations, fronts appear as discontinuities in wind direction. Due to the nature of the retrieval problem a simple discontinuity model is not feasible, and hence we have developed a constrained discontinuity vector Gaussian Process model which ensures realistic fronts. We describe the generative model and show how to compute the data likelihood given the model. We show the results of inference using the model with Markov Chain Monte Carlo methods on both synthetic and real data

Millennial‐Scale Vulnerability of the Antarctic Ice Sheet to Regional Ice Shelf Collapse

The response of the Antarctic Ice Sheet to ice shelf collapse is explored with a high resolution ice sheet model. Rapid melting is applied to each of its major present day drainage basins in turn , to determine which parts of the ice sheet are most vulnerable to change in oceanic forcing, over the next 1000 years. We findthat West Antarctica can be largely deglaciated over a millenium, leading to more than two metres of sea level rise, if any of its major ice shelved disintegrated. The response of East Antarctica is more muted, but not negligible

Fusing Quantitative Requirements Analysis with Model-based Systems Engineering

A vision is presented for fusing quantitative requirements analysis with model-based systems engineering. This vision draws upon and combines emergent themes in the engineering milieu. “Requirements engineering” provides means to explicitly represent requirements (both functional and non-functional) as constraints and preferences on acceptable solutions, and emphasizes early-lifecycle review, analysis and verification of design and development plans. “Design by shopping” emphasizes revealing the space of options available from which to choose (without presuming that all selection criteria have previously been elicited), and provides means to make understandable the range of choices and their ramifications. “Model-based engineering” emphasizes the goal of utilizing a formal representation of all aspects of system design, from development through operations, and provides powerful tool suites that support the practical application of these principles. A first step prototype towards this vision is described, embodying the key capabilities. Illustrations, implications, further challenges and opportunities are outlined

Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units

The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale's principle, which ensures that biological neurons are either exclusively excitatory or inhibitory. Dale's principle is typically left out of ANNs because its inclusion impairs learning. This is problematic, because one of the great advantages of ANNs for neuroscience research is their ability to learn complicated, realistic tasks. Here, by taking inspiration from feedforward inhibitory interneurons in the brain we show that we can develop ANNs with separate populations of excitatory and inhibitory units that learn just as well as standard ANNs. We call these networks Dale's ANNs (DANNs). We present two insights that enable DANNs to learn well: (1) DANNs are related to normalization schemes, and can be initialized such that the inhibition centres and standardizes the excitatory activity, (2) updates to inhibitory neuron parameters should be scaled using corrections based on the Fisher Information matrix. These results demonstrate how ANNs that respect Dale's principle can be built without sacrificing learning performance, which is important for future work using ANNs as models of the brain. The results may also have interesting implications for how inhibitory plasticity in the real brain operates

Proglacial Lakes Control Glacier Geometry and Behavior During Recession

Ice‐contact proglacial lakes are generally absent from numerical model simulations of glacier evolution, and their effects on ice dynamics and on rates of deglaciation remain poorly quantified. Using the BISICLES ice flow model, we analyzed the effects of an ice‐contact lake on the Pukaki Glacier, New Zealand, during recession from the Last Glacial Maximum. The ice‐contact lake produced a maximum effect on grounding line recession >4 times further and on ice velocities up to 8 times faster, compared to simulations of a land‐terminating glacier forced by the same climate. The lake contributed up to 82% of cumulative grounding line recession and 87% of ice velocity during the first 300 years of the simulations, but those values decreased to just 6% and 37%, respectively, after 5,000 years. Numerical models that ignore lake interactions will, therefore, misrepresent the rate of recession especially during the transition of a land‐terminating to a lake‐terminating environment

ERP Systems and the University as an 'Unique' Organisation

Enterprise Resource Planning (ERP) systems are widely used by large corporations around the world. Recently universities have turned to ERP as a means of replacing existing management and administration computer systems. In this article we provide analysis of the rollout of an ERP system in one particular institution in the UK, the particular focus being on how the development, implementation and use of both generic and university specific functionality is mediated and shaped by a fundamental and long standing tension within universities: this is the extent to which higher education institutions are organisations much like any other and the extent to which they are ‘unique’. The aim of this article is not to attempt to settle this issue of similarity/difference in one way or another. Rather, it seeks to illustrate the value of taking discussions of similarity relationships surrounding the university and other organisations as the topic of analysis. One way of working with these kinds of issues without resolving them is to consider their ‘distribution’ and where ERP shifts the responsibility for their final resolution. This is a novel and insightful way of understanding how ERP systems are refashioning the identity of universities. We suggest, moreover, that ERP software is ‘accompanied’ by such tensions in which ever site it is implemented. The research presented here is based on a participant observation study carried over the period of three years