Preprints

Filtering by Subject: Applied Statistics

ENSO Drives Child Undernutrition in the Global Tropics

Jesse K Anttila-Hughes, Amir Sultan Jina, Gordon C McCord

Published: 2020-11-12
Subjects: Applied Statistics, Climate, Environmental Public Health, Environmental Sciences, Environmental Studies, Other Environmental Sciences, Public Health, Social and Behavioral Sciences, Sustainability

The El Nino Southern Oscillation (ENSO) is a principal component of global climate variability known to influence a host of social and economic outcomes, but its systematic effects on human health remain poorly understood. We estimate ENSO’s association with child nutrition at global scale by combining variation in ENSO intensity from 1986-2018 with children’s height and weight from 186 surveys [...]

RECENT RAPID SEA LEVEL RISE IN DUBLIN BAY BASED ON TIDE GAUGE ANALYSIS

Amin Shoari Nejad, Andrew Parnell, Alice Greene, Brian P Kelleher, Gerard McCarthy

Published: 2020-05-27
Subjects: Applied Statistics, Climate, Earth Sciences, Life Sciences, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Other Life Sciences, Physical Sciences and Mathematics, Planetary Sciences, Statistics and Probability

We analysed tide gauges at Dublin port and its environs over the period 1938-2018. With three different tide gauges in the vicinity of the main Dublin port gauge, we merged the data sets and validated them against each other. The recordings of all four tide gauges were found to be in good agreement between 2003-2015, though this was markedly less so from 2016 to the present. We estimate the sea [...]

A 2,000-year Bayesian NAO reconstruction from the Iberian Peninsula

Armand Hernández, Guiomar Sánchez-López, Sergi Pla-Rabes, Laia Comas-Bru, Andrew Parnell, Niamh Cahill, Adelina Geyer, Ricardo M Trigo, Santiago Giralt

Published: 2019-11-27
Subjects: Applied Statistics, Climate, Earth Sciences, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Physical Sciences and Mathematics, Statistics and Probability

The North Atlantic Oscillation (NAO) is the major atmospheric mode that controls winter European climate variability because its strength and phase determine regional temperature, precipitation and storm tracks. The NAO spatial structure and associated climatic impacts over Europe are not stationary making it crucial to understanding its past evolution in order to improve the predictability of [...]

Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks

Jesper Sören Dramsch, Anders Nymark Christensen, Colin MacBeth, Mikael Lüthje

Published: 2019-10-31
Subjects: Applied Statistics, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics, Statistics and Probability

We present a novel 3D warping technique for the estimation of 4D seismic time-shift. This unsupervised method provides a diffeomorphic 3D time shift field that includes uncertainties, therefore it does not need prior time-shift data to be trained. This results in a widely applicable method in time-lapse seismic data analysis. We explore the generalization of the method to unseen data both in the [...]

Trends of hydroclimatic intensity in Colombia

Oscar Mesa, Viviana Urrea, Andrés Ochoa

Published: 2019-05-08
Subjects: Applied Statistics, Civil and Environmental Engineering, Climate, Earth Sciences, Engineering, Environmental Engineering, Environmental Sciences, Hydrology, Life Sciences, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Risk Analysis, Statistics and Probability, Water Resource Management

Prediction of changes in precipitation in upcoming years and decades caused by global climate change associated with the greenhouse effect, deforestation and other anthropic perturbations is a practical and scientific problem of high complexity and huge consequences. To advance toward this challenge we look at the daily historical record of all available rain gauges in Colombia to estimate an [...]

Are Detected Trends in Flood Magnitude and Shifts in the Timing of Floods of A Major River Basin in India, Linked To Anthropogenic Stressors?

Nandamuri Yamini Rama, Poulomi Ganguli, Chandranath Chatterjee

Published: 2019-04-10
Subjects: Applied Statistics, Civil and Environmental Engineering, Earth Sciences, Engineering, Hydraulic Engineering, Hydrology, Mathematics, Physical Sciences and Mathematics, Statistics and Probability

Analyzing of trends in flood magnitude and the timing of the dates of flood occurrences of large river basins across the globe are essential for understanding changes in water availability (high or low flows) and assessing the fidelity of global hydrological models. Our research is motivated by the recent six major consecutive floods in Mahanadi (years: 2001, 2003, 2006, 2008, 2011 and 2013) [...]

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

Philip E Bett, Hazel E. Thornton, Alberto Troccoli, Matteo De Felice, Emma Suckling, Laurent Dubus, Yves-Marie Saint-Drenan, David J. Brayshaw

Published: 2019-04-01
Subjects: Applied Statistics, Earth Sciences, Environmental Sciences, Physical Sciences and Mathematics, Physics, Probability, Statistics and Probability, Sustainability

We demonstrate the current levels of skill for seasonal forecasts of wind and irradiance in Europe, using forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that a simple linear regression-based method, using the hindcast and [...]

Deep Learning Application for 4D Pressure Saturation Inversion Compared to Bayesian Inversion on North Sea Data

Jesper Sören Dramsch, Gustavo Corte, Hamed Amini, Mikael Lüthje, Colin MacBeth

Published: 2019-02-21
Subjects: Applied Statistics, Earth Sciences, Geophysics and Seismology, Physical Sciences and Mathematics, Statistics and Probability

In this work we present a deep neural network inversion on map-based 4D seismic data for pressure and saturation. We present a novel neural network architecture that trains on synthetic data and provides insights into observed field seismic. The network explicitly includes AVO gradient calculation within the network as physical knowledge to stabilize pressure and saturation changes separation. [...]

Hierarchical Cluster Analysis by R language for Pattern Recognition in the Bathymetric Data Frame: a Case Study of the Mariana Trench, Pacific Ocean

Polina Lemenkova

Published: 2019-01-25
Subjects: Applied Statistics, Earth Sciences, Environmental Education, Environmental Monitoring, Environmental Sciences, Environmental Studies, Geographic Information Sciences, Geography, Geology, Geomorphology, Oceanography, Oceanography and Atmospheric Sciences and Meteorology, Other Earth Sciences, Other Oceanography and Atmospheric Sciences and Meteorology, Other Statistics and Probability, Physical and Environmental Geography, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science, Statistics and Probability, Tectonics and Structure

The geographic focus of the current study Mariana trench, the deepest point of the Earth located in the west Pacific Ocean. Mariana trench has unique structure and features formed in the complex process of the trench development. There is a range of the environmental factors affecting trench structure and functioning: bathymetry, geography, geology and tectonics. Current research aimed to study [...]

Statistics and segmentation: Using Big Data to assess Cascades Arc compositional variability

Bradley W Pitcher, Adam J.R. Kent

Published: 2018-09-24
Subjects: Applied Mathematics, Applied Statistics, Earth Sciences, Geochemistry, Geology, Multivariate Analysis, Physical Sciences and Mathematics, Statistics and Probability, Volcanology

Primitive lavas erupted in the Cascades arc of western North America demonstrate significant patterns of along-arc heterogeneity. Such compositional diversity may be the result of differences in mantle melting processes, subduction geometry, regional tectonics, or compositions of the slab, mantle or overlying lithosphere. Previous authors have partitioned the arc into four geochemically distinct [...]

Uncertainty in sea level rise projections due to the dependence between contributors

Dewi Le Bars

Published: 2018-03-08
Subjects: Applied Statistics, Climate, Earth Sciences, Environmental Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Probability, Statistical Models, Statistics and Probability

Sea level rises at an accelerating pace threatening coastal communities all over the world. In this context sea level projections are key tools to help risk mitigation and adaptation. Sea level projections are often made using models of the main contributors to sea level rise (e.g. thermal expansion, glaciers, ice sheets...). To obtain the total sea level these contributions are added, therefore [...]

Bootstrapped high quantile estimation --- An experiment with scarce precipitation data

Hung Tan Thai Nguyen, Harald Bernhard, Zhangsheng Lai

Published: 2018-02-25
Subjects: Applied Statistics, Physical Sciences and Mathematics, Probability, Statistics and Probability

This paper details team SUTD’s effort when participating in the “Prediction of extremal precipitation” challenge. We propose a framework that combines the generalized Pareto distribution, a bootstrap resampling scheme and inverse distance weights to capture spatial dependence. Our method reduces the quantile loss functions by 55.1% as compared to a naive benchmark, and shows improvement across [...]

Geostatistical modelling of cyclic and rhythmic facies architectures

Thomas Le Blévec, Olivier Dubrule, Cédric M. John, Gary J. Hampson

Published: 2017-11-07
Subjects: Applied Statistics, Earth Sciences, Physical Sciences and Mathematics, Sedimentology, Statistics and Probability

A pluri-Gaussian method is developed for facies variables in three dimensions to model vertical cyclicity, related to facies ordering, and rhythmicity. Cyclicity is generally characterized by shallowing or deepening-upward sequences and rhythmicity by a low range of variability in cycle thicknesses. Both of these aspects are commonly observed in shallow-marine carbonate successions, especially [...]

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