# Suchergebnis: Katalogdaten im Frühjahrssemester 2018

Data Science Master | ||||||

Interdisziplinäre Wahlfächer | ||||||

Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|---|

101-0478-00L | Measurement and Modelling of Travel Behaviour | W | 6 KP | 4G | K. W. Axhausen | |

Kurzbeschreibung | Comprehensive introduction to survey methods in transport planning and modeling of travel behavior, using advanced discrete choice models. | |||||

Lernziel | Enabling the student to understand and apply the various measurement approaches and models of modelling travel behaviour. | |||||

Inhalt | Behavioral model and measurement; travel diary, design process, hypothetical markets, discrete choice model, parameter estimation, pattern of travel behaviour, market segments, simulation, advanced discrete choice models | |||||

Skript | Various papers and notes are distributed during the course. | |||||

Voraussetzungen / Besonderes | Requirement: Transport I | |||||

103-0228-00L | Multimedia CartographyVoraussetzung: Erfolgreicher Abschluss der Lerneinheit Cartography III (103-0227-00L). | W | 4 KP | 3G | H.‑R. Bär, R. Sieber | |

Kurzbeschreibung | Focus of this course is on the realization of an atlas project in a small team. During the first part of the course, the necessary organizational, creative and technological basics will be provided. At the end of the course, the interactive atlas projects will be presented by the team members. | |||||

Lernziel | The goal of this course is to provide the students the theoretical background, knowledge and practical skills necessary to plan, design and create an interactive Web atlas based on modern Web technologies. | |||||

Inhalt | This course will cover the following topics: - Web map design - Project management - Graphical user interfaces in Web atlases - Interactions in map and atlas applications - Web standards - Programming interactive Web applications - Use of software libraries - Cartographic Web services - Code repository - Copyright and the Internet | |||||

Skript | Lecture notes and additional material are available on Moodle. | |||||

Literatur | - Cartwright, William; Peterson, Michael P. and Georg Gartner (2007); Multimedia Cartography, Springer, Heidelberg | |||||

Voraussetzungen / Besonderes | Prerequisites: Successful completion of Cartography III (103-0227-00L). Previous knowledge in Web programming. The students are expected to - present their work in progress on a regular basis - present their atlas project at the end of the course - keep records of all the work done - document all individual contributions to the project | |||||

103-0247-00L | Mobile GIS and Location-Based Services | W | 5 KP | 4G | P. Kiefer | |

Kurzbeschreibung | The course introduces students to the theoretical and technological background of mobile geographic information systems and location-based services. In lab sessions students acquire competences in mobile GIS design and implementation. | |||||

Lernziel | Students will - learn about the implications of mobility on GIS - get a detailed overview on research fields related to mobile GIS - get an overview on current mobile GIS and LBS technology, and learn how to assess new technologies in this fast-moving field - achieve an integrated view of Geospatial Web Services and mobile GIS - acquire competences in mobile GIS design and implementation | |||||

Inhalt | - LBS and mobile GIS: architectures, market, applications, and application development - Development for Android - Mobile decision-making, context, personalization, and privacy - Mobile human computer interaction and user interfaces - Mobile behavior interpretation | |||||

Voraussetzungen / Besonderes | Elementary programming skills (Java) | |||||

103-0255-01L | Geodatenanalyse | W | 2 KP | 2G | D. Jonietz | |

Kurzbeschreibung | Die Lehrveranstaltung behandelt weiterführende Methoden der Geodatenanalyse. | |||||

Lernziel | - Verstehen der theoretischen Grundlagen räumlicher Analyseverfahren. - Verstehen und Anwenden von Methoden zur raumbezogenen Datenanalyse. - Erkennen häufiger Fehlerquellen bei der Geodatenanalyse. - Vertiefende praktische Kenntnisse in der Anwendung entsprechender GIS-Tools. | |||||

Inhalt | In der Lehrveranstaltung werden weiterführende Methoden räumlicher Analyseverfahren theoretisch behandelt sowie anhand von Übungsaufgaben angewendet. | |||||

Skript | kein Skript. | |||||

Literatur | Eine Literaturliste wird in der Lehrveranstaltung zur Verfügung gestellt. | |||||

Voraussetzungen / Besonderes | Voraussetzungen: Basiswissen im Bereich der Geoinformationstechnologien und der Verwendung von Geoinformationssystemen entsprechend den Vorlesungen GIS I und GIS II im Bachelor-Studiengang Geomatik und Planung. | |||||

227-0945-10L | Cell and Molecular Biology for Engineers IIThis course is part II of a two-semester course. Knowledge of part I is required. | W | 3 KP | 2G | C. Frei | |

Kurzbeschreibung | The course gives an introduction into cellular and molecular biology, specifically for students with a background in engineering. The focus will be on the basic organization of eukaryotic cells, molecular mechanisms and cellular functions. Textbook knowledge will be combined with results from recent research and technological innovations in biology. | |||||

Lernziel | After completing this course, engineering students will be able to apply their previous training in the quantitative and physical sciences to modern biology. Students will also learn the principles how biological models are established, and how these models can be tested. | |||||

Inhalt | Lectures will include the following topics: DNA, chromosomes, RNA, protein, genetics, gene expression, membrane structure and function, vesicular traffic, cellular communication, energy conversion, cytoskeleton, cell cycle, cellular growth, apoptosis, autophagy, cancer, development and stem cells. In addition, three journal clubs will be held, where one/two publictions will be discussed. For each journal club, students (alone or in groups of up to three students) have to write a summary and discussion of the publication. These written documents will be graded, and count as 25% for the final grade. | |||||

Skript | Scripts of all lectures will be available. | |||||

Literatur | "Molecular Biology of the Cell" (6th edition) by Alberts, Johnson, Lewis, Morgan, Raff, Roberts, and Walter. | |||||

261-5111-00L | Asset Management: Advanced Investments (University of Zurich)Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: MFOEC207 Beachten Sie die Einschreibungstermine an der UZH: http://www.uzh.ch/studies/application/mobilitaet.html | W | 3 KP | 2V | Uni-Dozierende | |

Kurzbeschreibung | Comprehension and application of advanced portfolio theory | |||||

Lernziel | Comprehension and application of advanced portfolio theory | |||||

Inhalt | The theoretical part of the lecture consists of the topics listed below. - Standard Markowitz Model and Extensions MV Optimization, MV with Liabilities and CAPM. - The Crux with MV Resampling, regression, Black-Litterman, Bayesian, shrinkage, constrained and robust optimization. - Downside and Coherent Risk Measures Definition of risk measures, MV optimization under VaR and ES constraints. - Risk Budgeting Equal risk contribution, most diversified portfolio and other concentration indices - Regime Switching and Asset Allocation An introduction to regime switching models and its intuition. - Strategic Asset Allocation Introducing a continuous-time framework, solving the HJB equation and the classical Merton problem. | |||||

261-5120-00L | Computational Biomedicine II | W | 4 KP | 3P | G. Rätsch | |

Kurzbeschreibung | The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems. | |||||

Lernziel | During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions. | |||||

Inhalt | The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine: 1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc. 2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them. 3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them. 4) Genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges. | |||||

Voraussetzungen / Besonderes | Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line Relation to Course 261-5100-00 Computational Biomedicine: This course is a continuation of the previous course with new topics related to medical data and machine learning. The format of Computational Biomedicine II will also be different. It is helpful but not essential to attend Computational Biomedicine before attending Computational Biomedicine II. | |||||

263-3501-00L | Advanced Computer Networks | W | 5 KP | 2V + 2U | A. Singla, P. M. Stüdi | |

Kurzbeschreibung | This course covers a set of advanced topics in computer networks. The focus is on principles, architectures, and protocols used in modern networked systems, such as the Internet and data center networks. | |||||

Lernziel | The goals of the course are to build on basic undergraduate-level networking, and provide an understanding of the tradeoffs and existing technology in the design of large, complex networked systems, together with concrete experience of the challenges through a series of lab exercises. | |||||

Inhalt | The focus of the course is on principles, architectures, and protocols used in modern networked systems. Topics include data center network topologies, software defined networking, network function virtualization, flow control and congestion control in data centers, end-point optimizations, and server virtualization. | |||||

363-1000-00L | Financial Economics | W | 3 KP | 2V | A. Bommier | |

Kurzbeschreibung | This is a theoretical course on the economics of financial decision making, at the crossroads between Microeconomics and Finance. It discusses portfolio choice theory, risk sharing, market equilibrium and asset pricing. | |||||

Lernziel | The objective is to make students familiar with the economics of financial decision making and develop their intuition regarding the determination of asset prices, the notions of optimal risk sharing. However this is not a practical formation for traders. Moreover, the lecture doesn't cover topics such as market irrationality or systemic risk. | |||||

Inhalt | The following topics will be discussed: Introduction to finance and investment planning; Option valuation; Arbitrage; Choice under uncertainty; Portfolio Choice; Risk sharing and insurance; Market equilibrium under symmetric information. | |||||

Literatur | Suggesting readings: 1) "Investments", by Z. Bodie, A. Kane and A. Marcus, for the introductory part of the course (see chapters 20 and 21 in particular). 2) "Finance and the Economics of Uncertainty" by G. Demange and G. Laroque, Blackwell, 2006. 3) "The Economics of Risk and Time", by C. Gollier, and Other readings: - "Intermediate Financial Theory" by J.-P. Danthine and J.B. Donaldson. - Ingersoll, J., E., Theory of Financial Decision Making, Rowman and Littlefield Publishers. - Leroy S and J. Werner, Principles of Financial Economics, Cambridge University Press, 2001 | |||||

Voraussetzungen / Besonderes | Basic mathematical skills needed (calculus, linear algebra, convex analysis). Students must be able to solve simple optimization problems (e.g. Lagrangian methods). Some knowledge in microeconomics would help but is not compulsory. The bases will be covered in class. | |||||

363-1091-00L | Social Data Science | W | 3 KP | 2G | D. Garcia Becerra | |

Kurzbeschreibung | Social Data Science is introduced as a set of techniques to analyze human behavior and social interaction through digital traces. The course focuses both on the fundamentals and applications of Data Science in the Social Sciences, including technologies for data retrieval, processing, and analysis with the aim to derive insights that are interpretable from a wider theoretical perspective. | |||||

Lernziel | A successful participant of this course will be able to - understand a wide variety of techniques to retrieve digital trace data from online data sources - store, process, and summarize online data for quantitative analysis - perform statistical analyses to test hypotheses, derive insights, and formulate predictions - implement streamlined software that integrates data retrieval, processing, statistical analysis, and visualization - interpret the results of data analysis with respect to theoretical and testable principles of human behavior - understand the limitations of observational data analysis with respect to data volume, statistical power, and external validity | |||||

Inhalt | Social Data Science (SDS) provides a broad approach to the quantitative analysis of human behavior through digital trace data. SDS integrates the implementation of data retrieval and processing, the application of statistical analysis methods, and the interpretation of results to derive insights of human behavior at high resolutions and large scales. The motivation of SDS stems from theories in the Social Sciences, which are addressed with respect to societal phenomena and formulated as principles that can be tested against empirical data. Data retrieval in SDS is performed in an automated manner, accessing online databases and programming interfaces that capture the digital traces of human behavior. Data processing is computerized with calibrated methods that quantify human behavior, for example constructing social networks or measuring emotional expression. These quantities are used in statistical analyses to both test hypotheses and explore new aspects on human behavior. The course starts with an introduction to Social Data Science and the R statistical language, followed by three content blocks: collective behavior, sentiment analysis, and social network analysis. The course ends with a datathon that sets the starting point of final student projects. The course will cover various examples of the application of SDS: - Search trends to measure information seeking - Popularity and social impact - Evaluation of sentiment analysis techniques - Quantitative analysis of emotions and social media sharing - Twitter social network analysis The lectures include theoretical foundations of the application of digital trace data in the Social Sciences, as well as practical examples of data retrieval, processing, and analysis cases in the R statistical language from a literate programming perspective. The block course contains lectures and exercise sessions during the morning and afternoon of five days. Exercise classes provide practical skills and discuss the solutions to exercises that build on the concepts and methods presented in the previous lectures. | |||||

Skript | The lecture slides will be available on the Moodle platform, for registered students only. | |||||

Literatur | See handouts. Specific literature is provided for download, for registered students only. | |||||

Voraussetzungen / Besonderes | Participants of the course should have some basic background in statistics and programming, and an interest to learn about human behavior from a quantitative perspective. Prior knowledge of advanced R, information retrieval, or information systems is not necessary. Exercise sessions build on technical and theoretical content explained in the lectures. Students need a working laptop with Internet access to perform the guided exercises. Course evaluation is based on the project developed in the last session datathon (50%) and on the final project report (50%). The course takes place between Feb 12th and Feb 16th (both inclusive), from 9:15 to 12:00 and from 13:15 to 16:00. | |||||

401-3629-00L | Quantitative Risk Management | W | 4 KP | 2V | P. Cheridito | |

Kurzbeschreibung | This course introduces methods from probability theory and statistics that can be used to model financial risks. Topics addressed include loss distributions, risk measures, extreme value theory, multivariate models, copulas and dependence structures as well as operational risk. | |||||

Lernziel | The goal is to learn the most important methods from probability theory and statistics used in financial risk modeling. | |||||

Inhalt | 1. Introduction 2. Basic Concepts in Risk Management 3. Empirical Properties of Financial Data 4. Financial Time Series 5. Extreme Value Theory 6. Multivariate Models 7. Copulas and Dependence 8. Operational Risk | |||||

Skript | Course material is available on https://people.math.ethz.ch/~patrickc/qrm | |||||

Literatur | Quantitative Risk Management: Concepts, Techniques and Tools AJ McNeil, R Frey and P Embrechts Princeton University Press, Princeton, 2015 (Revised Edition) http://press.princeton.edu/titles/10496.html | |||||

Voraussetzungen / Besonderes | The course corresponds to the Risk Management requirement for the SAA ("Aktuar SAV Ausbildung") as well as for the Master of Science UZH-ETH in Quantitative Finance. | |||||

401-3888-00L | Introduction to Mathematical Finance Ein verwandter Kurs ist 401-3913-01L Mathematical Foundations for Finance (3V+2U, 4 ECTS-KP). Obwohl beide Kurse unabhängig voneinander belegt werden können, darf nur einer ans gesamte Mathematik-Studium (Bachelor und Master) angerechnet werden. | W | 10 KP | 4V + 1U | M. Schweizer | |

Kurzbeschreibung | This is an introductory course on the mathematics for investment, hedging, portfolio management, asset pricing and financial derivatives in discrete-time financial markets. We discuss arbitrage, completeness, risk-neutral pricing and utility maximisation. We prove the fundamental theorem of asset pricing and the hedging duality theorems, and also study convex duality in utility maximization. | |||||

Lernziel | This is an introductory course on the mathematics for investment, hedging, portfolio management, asset pricing and financial derivatives in discrete-time financial markets. We discuss arbitrage, completeness, risk-neutral pricing and utility maximisation, and maybe other topics. We prove the fundamental theorem of asset pricing and the hedging duality theorems in discrete time, and also study convex duality in utility maximization. | |||||

Inhalt | This course focuses on discrete-time financial markets. It presumes a knowledge of measure-theoretic probability theory (as taught e.g. in the course "Probability Theory"). The course is offered every year in the Spring semester. This course is the first of a sequence of two courses on mathematical finance. The second course "Mathematical Finance" (MF II), 401-4889-00, focuses on continuous-time models. It is advisable that the present course, MF I, is taken prior to MF II. For an overview of courses offered in the area of mathematical finance, see Link. | |||||

Skript | The course is based on different parts from different textbooks as well as on original research literature. Lecture notes will not be available. | |||||

Literatur | Literature: Michael U. Dothan, "Prices in Financial Markets", Oxford University Press Hans Föllmer and Alexander Schied, "Stochastic Finance: An Introduction in Discrete Time", de Gruyter Marek Capinski and Ekkehard Kopp, "Discrete Models of Financial Markets", Cambridge University Press Robert J. Elliott and P. Ekkehard Kopp, "Mathematics of Financial Markets", Springer | |||||

Voraussetzungen / Besonderes | NOTE: Due to personal (health) reasons, this course is offered in concentrated form during the second half of the semester. The course will start on *Monday, April 09, 2018*. Some extra information about possible preparation as well as extra references will be posted here later. A related course is "Mathematical Foundations for Finance" (MFF), 401-3913-01. Although both courses can be taken independently of each other, only one will be given credit points for the Bachelor and the Master degree. In other words, it is also not possible to earn credit points with one for the Bachelor and with the other for the Master degree. This course is the first of a sequence of two courses on mathematical finance. The second course "Mathematical Finance" (MF II), 401-4889-00, focuses on continuous-time models. It is advisable that the present course, MF I, is taken prior to MF II. For an overview of courses offered in the area of mathematical finance, see Link. | |||||

401-3936-00L | Data Analytics for Non-Life Insurance Pricing | W | 4 KP | 2V | C. M. Buser, M. V. Wüthrich | |

Kurzbeschreibung | We study statistical methods in supervised learning for non-life insurance pricing such as generalized linear models, generalized additive models, Bayesian models, neural networks, classification and regression trees, random forests, gradient boosting machines and support vector machines. Moreover, we present unsupervised learning methods applied to telematics car driving data. | |||||

Lernziel | The student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction. | |||||

Inhalt | We present the following chapters: - generalized linear models (GLMs) - generalized additive models (GAMs) - credibility theory - classification and regression trees (CARTs) - bagging, random forests and boosting - support vector machines (SVMs) - unsupervised learning methods - telematics car driving data | |||||

Skript | The lecture notes are available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2870308 | |||||

Voraussetzungen / Besonderes | This course will be held in English and counts towards the diploma of "Aktuar SAV". For the latter, see details under www.actuaries.ch Good knowledge in probability theory, stochastic processes and statistics is assumed. | |||||

401-4658-00L | Computational Methods for Quantitative Finance: PDE Methods | W | 6 KP | 3V + 1U | C. Schwab | |

Kurzbeschreibung | Introduction to principal methods of option pricing. Emphasis on PDE-based methods. Prerequisite MATLAB programming and knowledge of numerical mathematics at ETH BSc level. | |||||

Lernziel | Introduce the main methods for efficient numerical valuation of derivative contracts in a Black Scholes as well as in incomplete markets due Levy processes or due to stochastic volatility models. Develop implementation of pricing methods in MATLAB. Finite-Difference/ Finite Element based methods for the solution of the pricing integrodifferential equation. | |||||

Inhalt | 1. Review of option pricing. Wiener and Levy price process models. Deterministic, local and stochastic volatility models. 2. Finite Difference Methods for option pricing. Relation to bi- and multinomial trees. European contracts. 3. Finite Difference methods for Asian, American and Barrier type contracts. 4. Finite element methods for European and American style contracts. 5. Pricing under local and stochastic volatility in Black-Scholes Markets. 6. Finite Element Methods for option pricing under Levy processes. Treatment of integrodifferential operators. 7. Stochastic volatility models for Levy processes. 8. Techniques for multidimensional problems. Baskets in a Black-Scholes setting and stochastic volatility models in Black Scholes and Levy markets. 9. Introduction to sparse grid option pricing techniques. | |||||

Skript | There will be english, typed lecture notes as well as MATLAB software for registered participants in the course. | |||||

Literatur | R. Cont and P. Tankov : Financial Modelling with Jump Processes, Chapman and Hall Publ. 2004. Y. Achdou and O. Pironneau : Computational Methods for Option Pricing, SIAM Frontiers in Applied Mathematics, SIAM Publishers, Philadelphia 2005. D. Lamberton and B. Lapeyre : Introduction to stochastic calculus Applied to Finance (second edition), Chapman & Hall/CRC Financial Mathematics Series, Taylor & Francis Publ. Boca Raton, London, New York 2008. J.-P. Fouque, G. Papanicolaou and K.-R. Sircar : Derivatives in financial markets with stochastic volatility, Cambridge Univeristy Press, Cambridge, 2000. N. Hilber, O. Reichmann, Ch. Schwab and Ch. Winter: Computational Methods for Quantitative Finance, Springer Finance, Springer, 2013. | |||||

Voraussetzungen / Besonderes | Start of the lecture: WED, 28 Feb. 2018 (second week of the semester). | |||||

401-8915-00L | Advanced Financial Economics (University of Zurich)Der Kurs muss direkt an der UZH belegt werden. UZH Modulkürzel: MFOEC206 Beachten Sie die Einschreibungstermine an der UZH: https://www.uzh.ch/cmsssl/de/studies/application/mobilitaet.html | W | 6 KP | 4G | Uni-Dozierende | |

Kurzbeschreibung | Portfolio Theory, CAPM, Financial Derivatives, Incomplete Markets, Corporate Finance, Behavioural Finance, Evolutionary Finance | |||||

Lernziel | Students should get familiar with the cornerstones of modern financial economics. | |||||

Voraussetzungen / Besonderes | This course replaces "Advanced Financial Economics" (MFOEC105), which will be discontinued. Students who have taken "Advanced Financial Economics" (MFOEC105) in the past, are not allowed to book this course "Advanced Financial Economics" (MFOEC206). There will be a podcast for this lecture. | |||||

636-0702-00L | Statistical Models in Computational Biology | W | 6 KP | 2V + 1U + 2A | N. Beerenwinkel | |

Kurzbeschreibung | The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods. | |||||

Lernziel | The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets. | |||||

Inhalt | Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises. | |||||

Skript | no | |||||

Literatur | - Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004 | |||||

701-0412-00L | Klimasysteme | W | 3 KP | 2G | R. Knutti, I. Medhaug | |

Kurzbeschreibung | Die wichtigsten physikalischen Komponenten des Klimasystems und deren Wechselwirkungen werden eingeführt. Vor dem Hintergrund der Klimageschichte - und variabilität werden die Mechanismen des anthropogenen Klimawandels analysiert. Absolvierende des Kurses sind in der Lage, einfache Problemstellungen aus dem Bereich der Klimasysteme zu identifizieren und erläutern. | |||||

Lernziel | Studierende können: - die wichtigsten physikalischen Komponenten des goblaben Klimasystems beschreiben und ihre Wechselwirkungen skizzieren. - die Mechanismen des anthropogenen Klimawandels erklären. einfache Problemstellungen aus dem Bereich der Klimasysteme identifizieren und erläutern. | |||||

Skript | Kopien der Folien werden elektronisch zur Verfuegung gestellt. | |||||

Literatur | Eine vollständige Literaturliste wird abgegeben. Insbesondere empfohlen sind: - Hartmann, D., 2016: Global Physical Climatology. Academic Press, London, 485 pp. - Peixoto, J.P. and A.H. Oort, 1992: Physics of Climate. American Institute of Physics, New York, 520 pp. | |||||

Voraussetzungen / Besonderes | Dozierende: Reto Knutti, mehrere Vorträge zu Spezialthemen von anderen Dozenten Unterrichtssprache: deutsch Sprache der Folien: englisch | |||||

701-1216-00L | Numerical Modelling of Weather and Climate | W | 4 KP | 3G | C. Schär, U. Lohmann | |

Kurzbeschreibung | The guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes. | |||||

Lernziel | The guiding principle of this lecture is that students can understand how weather and climate models are formulated from the governing physical principles and how they are used for climate and weather prediction purposes. | |||||

Inhalt | The course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Hands-on experience with simple models will be acquired in the tutorials. | |||||

Skript | Slides and lecture notes will be made available at Link | |||||

Literatur | List of literature will be provided. | |||||

Voraussetzungen / Besonderes | Prerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming | |||||

701-1226-00L | Inter-Annual Phenomena and Their Prediction | W | 2 KP | 2G | C. Appenzeller | |

Kurzbeschreibung | This course provides an overview of the current ability to understand and predict intra-seasonal and inter-annual climate variability in the tropical and extra-tropical region and provides insights on how operational weather and climate services are organized. | |||||

Lernziel | Students will acquire an understanding of the key atmosphere and ocean processes involved, will gain experience in analyzing and predicting short-term climate variability and learn how operational weather and climate services are organised and how scientific developments can improve these services. | |||||

Inhalt | The course covers the following topics: Part 1: - a brief introduction into short-term climate variability and some basic concepts - a brief review of climate data and the statistical concepts used for analysing climate variability (e.g. correlation analysis, teleconnection maps, EOF analysis) Part 2: - inter-annual variability in the tropical region (e.g. ENSO, MJO) - inter-annual variability in the extra-tropical region (e.g. Blocking, NAO, PNA, regimes) Part 3: - prediction of short-term climate variability (statistical methods, ensemble prediction systems. weekly to seasonal forecasts) - verification methods for probabilistic forecast systems Part 4: - challenges for operational weather and climate services - weather and climate extremes - early warning systems - a visit to the forecasting centre of MeteoSwiss | |||||

Skript | A pdf version of the slides will be available at http://www.iac.ethz.ch/edu/courses/master/modules/interannual-phenomena.html | |||||

Literatur | References are given during the lecture. | |||||

701-1252-00L | Climate Change Uncertainty and Risk: From Probabilistic Forecasts to Economics of Climate Adaptation | W | 3 KP | 2V + 1U | D. N. Bresch, R. Knutti | |

Kurzbeschreibung | The course introduces the concepts of predictability, probability, uncertainty and probabilistic risk modelling and their application to climate modeling and the economics of climate adaptation. | |||||

Lernziel | Students will acquire knowledge in uncertainty and risk quantification (probabilistic modelling) and an understanding of the economics of climate adaptation. They will become able to construct their own uncertainty and risk assessment models (MATLAB), hence basic understanding of scientific programming forms a prerequisite of the course. | |||||

Inhalt | The first part of the course covers methods to quantify uncertainty in detecting and attributing human influence on climate change and to generate probabilistic climate change projections on global to regional scales. Model evaluation, calibration and structural error are discussed. In the second part, quantification of risks associated with local climate impacts and the economics of different baskets of climate adaptation options are assessed – leading to informed decisions to optimally allocate resources. Such pre-emptive risk management allows evaluating a mix of prevention, preparation, response, recovery, and (financial) risk transfer actions, resulting in an optimal balance of public and private contributions to risk management, aiming at a more resilient society. The course provides an introduction to the following themes: 1) basics of probabilistic modelling and quantification of uncertainty from global climate change to local impacts of extreme events 2) methods to optimize and constrain model parameters using observations 3) risk management from identification (perception) and understanding (assessment, modelling) to actions (prevention, preparation, response, recovery, risk transfer) 4) basics of economic evaluation, economic decision making in the presence of climate risks and pre-emptive risk management to optimally allocate resources | |||||

Skript | Powerpoint slides will be made available | |||||

Literatur | - | |||||

Voraussetzungen / Besonderes | Hands-on experience with probabilistic climate models and risk models will be acquired in the tutorials; hence basic understanding of scientific programming forms a prerequisite of the course. Basic understanding of the climate system, e.g. as covered in the course 'Klimasysteme' is required. Examination: graded tutorials during the semester (benotete Semesterleistung) |

- Seite 1 von 2 Alle