Visual Analysis of Variability and Features of Climate Simulation Ensembles

This PhD thesis is concerned with the visual analysis of time-dependent scalar field ensembles as occur in climate simulations. Modern climate projections consist of multiple simulation runs (ensemble members) that vary in parameter settings and/or initial values, which leads to variations in the resulting simulation data. The goal of ensemble simulations is to sample the space of possible futures under the given climate model and provide quantitative information about uncertainty in the results. The analysis of such data is challenging because apart from the spatiotemporal data, also variability has to be analyzed and communicated. This thesis presents novel techniques to analyze climate simulation ensembles visually. A central question is how the data can be aggregated under minimized information loss. To address this question, a key technique applied in several places in this work is clustering. The first part of the thesis addresses the challenge of finding clusters in the ensemble simulation data. Various distance metrics lend themselves for the comparison of scalar fields which are explored theoretically and practically. A visual analytics interface allows the user to interactively explore and compare multiple parameter settings for the clustering and investigate the resulting clusters, i.e. prototypical climate phenomena. A central contribution here is the development of design principles for analyzing variability in decadal climate simulations, which has lead to a visualization system centered around the new Clustering Timeline. This is a variant of a Sankey diagram that utilizes clustering results to communicate climatic states over time coupled with ensemble member agreement. It can reveal several interesting properties of the dataset, such as: into how many inherently similar groups the ensemble can be divided at any given time, whether the ensemble diverges in general, whether there are different phases in the time lapse, maybe periodicity, or outliers. The Clustering Timeline is also used to compare multiple climate simulation models and assess their performance. The Hierarchical Clustering Timeline is an advanced version of the above. It introduces the concept of a cluster hierarchy that may group the whole dataset down to the individual static scalar fields into clusters of various sizes and densities recording the nesting relationship between them. One more contribution of this work in terms of visualization research is, that ways are investigated how to practically utilize a hierarchical clustering of time-dependent scalar fields to analyze the data. To this end, a system of different views is proposed which are linked through various interaction possibilities. The main advantage of the system is that a dataset can now be inspected at an arbitrary level of detail without having to recompute a clustering with different parameters. Interesting branches of the simulation can be expanded to reveal smaller differences in critical clusters or folded to show only a coarse representation of the less interesting parts of the dataset. The last building block of the suit of visual analysis methods developed for this thesis aims at a robust, (largely) automatic detection and tracking of certain features in a scalar field ensemble. Techniques are presented that I found can identify and track super- and sub-levelsets. And I derive “centers of action” from these sets which mark the location of extremal climate phenomena that govern the weather (e.g. Icelandic Low and Azores High). The thesis also presents visual and quantitative techniques to evaluate the temporal change of the positions of these centers; such a displacement would be likely to manifest in changes in weather. In a preliminary analysis with my collaborators, we indeed observed changes in the loci of the centers of action in a simulation with increased greenhouse gas concentration as compared to pre-industrial concentration levels

Application of UV-C irradiation prevented a severe outbreak of proliferative kidney disease in rainbow trout aquaculture

There is an urgent need to establish protocols on how to protect salmonids in aquaculture from outbreaks of proliferative kidney disease (PKD). For this purpose, systems for a continuous application of peracetic acid (PAA, 0.1 mg l−1) and of ultraviolet C light (UV-C, 323.5− 158.6 mW s cm−2) were installed in the inlet of raceway-channels within a sub-unit of a commercial rainbow trout Oncorhynchus mykiss farm. After 127 d of rearing, a fish health examination was conducted. Fish in the control and PAA treatment groups showed signs of PKD. In contrast, fish in the UV-C treatment group showed almost no signs of disease based on clinical examinations and necropsy. This observation indicates that UV-C irradiation could be a promising tool to protect fish from PKD in the future

Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016

Visual Analysis of Variability and Features of Climate Simulation Ensembles

This PhD thesis is concerned with the visual analysis of time-dependent scalar field ensembles as occur in climate simulations. Modern climate projections consist of multiple simulation runs (ensemble members) that vary in parameter settings and/or initial values, which leads to variations in the resulting simulation data. The goal of ensemble simulations is to sample the space of possible futures under the given climate model and provide quantitative information about uncertainty in the results. The analysis of such data is challenging because apart from the spatiotemporal data, also variability has to be analyzed and communicated. This thesis presents novel techniques to analyze climate simulation ensembles visually. A central question is how the data can be aggregated under minimized information loss. To address this question, a key technique applied in several places in this work is clustering. The first part of the thesis addresses the challenge of finding clusters in the ensemble simulation data. Various distance metrics lend themselves for the comparison of scalar fields which are explored theoretically and practically. A visual analytics interface allows the user to interactively explore and compare multiple parameter settings for the clustering and investigate the resulting clusters, i.e. prototypical climate phenomena. A central contribution here is the development of design principles for analyzing variability in decadal climate simulations, which has lead to a visualization system centered around the new Clustering Timeline. This is a variant of a Sankey diagram that utilizes clustering results to communicate climatic states over time coupled with ensemble member agreement. It can reveal several interesting properties of the dataset, such as: into how many inherently similar groups the ensemble can be divided at any given time, whether the ensemble diverges in general, whether there are different phases in the time lapse, maybe periodicity, or outliers. The Clustering Timeline is also used to compare multiple climate simulation models and assess their performance. The Hierarchical Clustering Timeline is an advanced version of the above. It introduces the concept of a cluster hierarchy that may group the whole dataset down to the individual static scalar fields into clusters of various sizes and densities recording the nesting relationship between them. One more contribution of this work in terms of visualization research is, that ways are investigated how to practically utilize a hierarchical clustering of time-dependent scalar fields to analyze the data. To this end, a system of different views is proposed which are linked through various interaction possibilities. The main advantage of the system is that a dataset can now be inspected at an arbitrary level of detail without having to recompute a clustering with different parameters. Interesting branches of the simulation can be expanded to reveal smaller differences in critical clusters or folded to show only a coarse representation of the less interesting parts of the dataset. The last building block of the suit of visual analysis methods developed for this thesis aims at a robust, (largely) automatic detection and tracking of certain features in a scalar field ensemble. Techniques are presented that I found can identify and track super- and sub-levelsets. And I derive “centers of action” from these sets which mark the location of extremal climate phenomena that govern the weather (e.g. Icelandic Low and Azores High). The thesis also presents visual and quantitative techniques to evaluate the temporal change of the positions of these centers; such a displacement would be likely to manifest in changes in weather. In a preliminary analysis with my collaborators, we indeed observed changes in the loci of the centers of action in a simulation with increased greenhouse gas concentration as compared to pre-industrial concentration levels

Continuous Flow Synthesis of Carbonylated Heterocycles via Pd-Catalyzed Oxidative Carbonylation Using CO and O2 at Elevated Temperatures and Pressures

peer reviewedA continuous-flow Pd-catalyzed oxidative carbonylation protocol utilizing CO and O2 gas for the synthesis of carbonylated heterocycles is described. The optimization of temperature, pressure, CO/O2 ratio, catalyst loading, and reaction time resulted in process intensified conditions for this transformation. The optimized continuous flow conditions (120 °C, 20 bar pressure, 24 min residence time) were used to prepare a number of benzoxazolone, 2-benzoxazolidinone, and other biologically and synthetically important five- and six-membered carbonylated heterocycles in good overall yield and purity (14 examples). The continuous-flow process enables the safe and scalable oxidative carbonylation using CO/O2 under elevated pressures and temperatures

Continuous Flow Synthesis of Carbonylated Heterocycles via Pd-Catalyzed Oxidative Carbonylation Using CO and O₂ at Elevated Temperatures and Pressures

A continuous-flow Pd-catalyzed oxidative carbonylation protocol utilizing CO and O₂ gas for the synthesis of carbonylated heterocycles is described. The optimization of temperature, pressure, CO/O₂ ratio, catalyst loading, and reaction time resulted in process intensified conditions for this transformation. The optimized continuous flow conditions (120 °C, 20 bar pressure, 24 min residence time) were used to prepare a number of benzoxazolone, 2-benzoxazolidinone, and other biologically and synthetically important five- and six-membered carbonylated heterocycles in good overall yield and purity (14 examples). The continuous-flow process enables the safe and scalable oxidative carbonylation using CO/O₂ under elevated pressures and temperatures

PlantCollections - A Community Solution

PlantCollections - A Community Solution is the first free application developed to serve the needs of scientists, students and gardeners that provides access on the World Wide Web to information stored in plant record databases of botanic gardens and arboreta. This information supports research into plants; the environments in which they are found; and their growth, propagation, ornamental characteristics and causes of death in gardens

Development of a Continuous-Flow Sonogashira Cross-Coupling Protocol using Propyne Gas under Process Intensified Conditions

The development of a continuous-flow Sonogashira cross-coupling protocol using propyne gas for the synthesis of a key intermediate in the manufacturing of a β-amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitor, currently undergoing late stage clinical trials for a disease-modifying therapy of Alzheimer’s disease, is described. Instead of the currently used batch manufacturing process for this intermediate that utilizes TMS-propyne as reagent, we herein demonstrate the safe utilization of propyne gas, as a cheaper and more atom efficient reagent, using an intensified continuous-flow protocol under homogeneous conditions. The flow process afforded the target intermediate with a desired product selectivity of ∼91% (vs the bis adduct) after a residence time of 10 min at 160 °C. The continuous-flow process compares favorably with the batch process, which uses TMS-propyne and requires overnight processing, TBAF as an additive, and a significantly higher loading of Cu co-catalyst

Topological Data Analysis Made Easy with the Topology ToolKit, What is New?

IEEE VIS Tutorials 2020 http://ieeevis.org/year/2020/info/tutorialsInternational audienc