This open access proceedings volume brings selected, peer-reviewed contributions presented at the Stochastic Transport in Upper Ocean Dynamics (STUOD) 2021 Workshop, held virtually and in person at ...the Imperial College London, UK, September 20–23, 2021. The STUOD project is supported by an ERC Synergy Grant, and led by Imperial College London, the National Institute for Research in Computer Science and Automatic Control (INRIA) and the French Research Institute for Exploitation of the Sea (IFREMER). The project aims to deliver new capabilities for assessing variability and uncertainty in upper ocean dynamics. It will provide decision makers a means of quantifying the effects of local patterns of sea level rise, heat uptake, carbon storage and change of oxygen content and pH in the ocean. Its multimodal monitoring will enhance the scientific understanding of marine debris transport, tracking of oil spills and accumulation of plastic in the sea. All topics of these proceedings are essential to the scientific foundations of oceanography which has a vital role in climate science. Studies convened in this volume focus on a range of fundamental areas, including: Observations at a high resolution of upper ocean properties such as temperature, salinity, topography, wind, waves and velocity; Large scale numerical simulations; Data-based stochastic equations for upper ocean dynamics that quantify simulation error; Stochastic data assimilation to reduce uncertainty. These fundamental subjects in modern science and technology are urgently required in order to meet the challenges of climate change faced today by human society. This proceedings volume represents a lasting legacy of crucial scientific expertise to help meet this ongoing challenge, for the benefit of academics and professionals in pure and applied mathematics, computational science, data analysis, data assimilation and oceanography.
Motivated by the need for accurate traffic flow prediction in transportation management, we propose a functional data method to analyze traffic flow patterns and predict future traffic flow. In this ...study we approach the problem by sampling traffic flow trajectories from a mixture of stochastic processes. The proposed functional mixture prediction approach combines functional prediction with probabilistic functional classification to take distinct traffic flow patterns into account. The probabilistic classification procedure, which incorporates functional clustering and discrimination, hinges on subspace projection. The proposed methods not only assist in predicting traffic flow trajectories, but also identify distinct patterns in daily traffic flow of typical temporal trends and variabilities. The proposed methodology is widely applicable in analysis and prediction of longitudinally recorded functional data.
The purpose of this study is to explore primary school children’s understandings about the shape of the Earth. The sample is consisted of 124 first-graders from five primary schools located in an ...urban city of Turkey. The data of the study were collected through children’s drawings and semi-structured interviews. Results obtained from the drawings showed that only one third of the participants have drawn scientifically acceptable images of the earth. However, the subsequent semi-structured interviews revealed that more children have scientific knowledge about the shape of the Earth. The results also revealed that cartoons, story books and daily life experiences are the reasons for children’s misconceptions.
Directional data arise in various contexts such as oceanography (wave directions) and meteorology (wind directions), as well as with measurements on a periodic scale (weekdays, hours, etc.). Our ...contribution is to introduce a model-based approach to handle periodic data in the case of measurements taken at spatial locations, anticipating structured dependence between these measurements. We formulate a wrapped Gaussian spatial process model for this setting, induced from a customary linear Gaussian process. We build a hierarchical model to handle this situation and show that the fitting of such a model is possible using standard Markov chain Monte Carlo methods. Our approach enables spatial interpolation (and can accommodate measurement error). We illustrate with a set of wave direction data from the Adriatic coast of Italy, generated through a complex computer model.
From the famous poem The Garden by Andrew Marvell, to that of Seamus Heaney’s Digging, gardens have been depicted as idyllic places, as in classical pastoral poetry and Renaissance poetry and ...symbolic of ideas about identity, the past and memory. In what is now suggested by the scientists as the appropriate term for the controversial last geological period, some starting it with The Industrial Revolution and some dating it as early as the Agricultural Revolution and the Neolithic Age, “the anthropocene”, the human outlook on gardens and nature as a whole has to be reassessed. The globally catastrophic threat of the immanent extinction of humans as a species loudly drawn attention to by Slavoj Zizek in his 2012 text Welcome to the Anthropocene, calls for a further repositioning of the human than the ecocritical approaches up to now. In this light the whole world can be seen as Eden, the ‘Garden of Bliss’ about to be lost by humans who have inextricably doomed themselves in capitalism. This paper will look at the depiction of gardens in various examples of literature such as the Epic of Gılgamesh, religious poems, Romantic Poetry, Bacon’s Essay on Gardens, Shakespeare’s plays and Lewis Carrol’s Alice in Wonderland within an anthropocentric framework.
The National Research Council in the US has challenged the scientific community to build upon the rapidly advancing knowledge about the planet and an explosively developing technology to frame a new ...multidisciplinary paradigm for research in geophysics and global biology. The international scientific community-particularly the International Council of Scientific Unions (ICSU), a nongovernmental, scientific organization that may be thought of as constituting the world scientific community, is also becoming involved with a new, integrative program exploring natural processes that affect the total Earth system. Here, Malone discuss the ICSU's proposal for the International Geosphere Biosphere Program (IGBP). (Reprint 1986)
Hal Lindsey & Carlson's, 1970 book, The Late Great Planet Earth, was the best-selling non-fiction book of the 1970s. In it, using the eschatology of premillennial dispensationalism commonly believed ...by American evangelicals, he conflates biblical prophecy with current geopolitical conflicts. He exploits the uncertainty of the nuclear age, civil rights movement, and 'wars and rumours of wars' in Asia by giving readers a certain explanation: Christ will soon return. Within his book, Lindsey provides two maps depicting his narrative for the battle of Armageddon. The maps are devoid of borders, and only show troop movement via thick black arrows. This article focuses on these arrows and their geopolitical function. The article argues, beyond symbolizing mobility, that arrows on maps also symbolize future anticipatory cartographic temporalities. It is theorized that Lindsey's arrows potentiate and help actualize a narrow geopolitical future.
Perspectives on COVID-19 Dunkley, Arthur
The International journal of risk & safety in medicine,
2020, Volume:
31, Issue:
4
Journal Article
Peer reviewed
Open access
A few ideas that deserve to see the light of day. I wrote this essay on 23/3/2020. I have started updating in the form of post scripts. Please relate the information to the date on which it was ...written.
Precipitation is a complex physical process that varies in space and time. Predictions and interpolations at unobserved times and/or locations help to solve important problems in many areas. In this ...paper, we present a hierarchical Bayesian model for spatio-temporal data and apply it to obtain short term predictions of rainfall. The model incorporates physical knowledge about the underlying processes that determine rainfall, such as advection, diffusion and convection. It is based on a temporal autoregressive convolution with spatially colored and temporally white innovations. By linking the advection parameter of the convolution kernel to an external wind vector, the model is temporally nonstationary. Further, it allows for nonseparable and anisotropic covariance structures. With the help of the Voronoi tessellation, we construct a natural parametrization, that is, space as well as time resolution consistent, for data lying on irregular grid points. In the application, the statistical model combines forecasts of three other meteorological variables obtained from a numerical weather prediction model with past precipitation observations. The model is then used to predict three-hourly precipitation over 24 hours. It performs better than a separable, stationary and isotropic version, and it performs comparably to a deterministic numerical weather prediction model for precipitation and has the advantage that it quantifies prediction uncertainty.
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