Search result: Catalogue data in Autumn Semester 2022
Environmental Sciences Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Major in Atmosphere and Climate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Additional Elective Courses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-1241-00L | Atmospheric Remote Sensing | W | 3 credits | 2G | J. Gröbner, S. Kazantzis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course will provide advanced physical understanding on the fundamentals of passive and active remote sensing, measuring sensors and retrieval methods. A series of diverse remote sensing applications will be presented, including measurements/retrievals of various atmospheric composition parameters (ozone, aerosols, clouds, others) from surface based and satellite based instruments. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The students will learn how various components of the atmosphere are retrieved from radiation measurements, both from surface and satellite-based measurements. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Atmospheric passive and active remote sensing is connected with a large number of applications including: atmospheric composition, Earth-atmosphere radiative balance, atmospheric and weather prediction model assimilation, agriculture, energy and health related applications and many others. The proposed lesson is divided in three sections including exercises: • Fundamentals of remote sensing • Sensors (surface based and satellites) and retrieval methods • Applications The first aim of the lecture is to provide an in-depth understand of the physical aspects and basic laws on the fundamentals of remote sensing to the students. The lectures will provide a basic to intermediate understanding of radiative transfer of electromagnetic radiation through the atmosphere, covering the spectrum from UV to thermal. Examples of atmospheric components that will be addressed are: ozone, aerosols, greenhouse gases, clouds, water vapor. In addition, measuring sensors used from the surface or from satellites and the relevant retrieval methods based on passive and active remote sensing of atmospheric composition will be presented (e.g. Spectroradiometers, filter radiometers, Lidars and others). Finally, we aim to demonstrate a series of diverse remote sensing applications, including atmospheric composition measurements and retrievals from surface- and satellite-based instruments, including calibration and validation aspects. The exercises will be embedded in the overall course lectures to provide hands-on experience with the measurements and retrieval methods using datasets available from specific instruments (e.g. satellite sensors) and networks (e.g. EUBREWNET, AERONET, GAWPFR). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | none | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 701-1271-00L | Statistical Learning for Atmospheric and Climate Science  Number of participants limited to 30. Enrollment starts on19.09.2022 Priority is given to the target groups: Master Environmental Science and Master Atmospheric and Climate Science until 26.09.2022. Waiting list will be deleted 03.10.2022. | W | 3 credits | 2G | L. Gudmundsson, S. Sippel | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course will consist of overview lectures, hands-on practical exercises on (1) the basics of statistical learning and (2) with a focus on applications for atmospheric and climate science. Lectures will cover theoretical basics of statistical learning (advanced regression, nonlinear methods) and an overview of applications of statistical learning in the atmospheric and climate sciences. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | - Understanding elements and principals of statistical learning - Ability to select the appropriate statistical learning tools to tackle atmospheric and climate research problems - Ability to apply methods of statistical learning to atmospheric and climate research | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - Data in atmospheric and climate research (data types, observations, models) - Exploring properties of atmospheric and climate data (data in space and time, multivariate data) - Concepts of supervised learning (bias variance trade-off, overfitting, cross-validation) - Advanced linear regression (multiple linear regression, regularization) - Non-linear regression (tree based methods, neural networks) - Un-supervised learning (dimension reduction, clustering) - High-level applications of statistical learning for atmospheric and climate research (keynote speakers) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Hastie, T., Tibshirani, R. & Friedman, J. (2009). The elements of statistical learning (Ed. 2). New York: Springer series in statistics. (Link to book: https://web.stanford.edu/~hastie/Papers/ESLII.pdf, book homepage: http://web.stanford.edu/~hastie/ElemStatLearn/ ) James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York: springer. (Link to book: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf, book homepage (exercises, etc.): http://www-bcf.usc.edu/~gareth/ISL/) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | - Knowledge of introductory statistics - Overview on the climate system - Basic experience in a programming language Course should be limited to 30 participants. Exercises will be conducted in the R environment (https://www.r-project.org/), which is a specialized tool for statistical computing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-3001-00L | Environmental Systems Data Science: Data Processing **Students who have taken 701-3001-00L Environmental Systems Data Science in autumn semester 2020 or 21 are not allowed to take this lecture. The content is similar.** Number of participants is limited to 80. Course registration starts on 31.08.2022. Priority is given to the target groups until 23.09.2022, Target groups Agricultural Sciences MSc Environmental Sciences MSc Atmospheric and Climate Science MSc Environmental Sciences PhD Agricultural Sciences PhD Waiting list will be deleted on 30.09.2022 | W | 2 credits | 2G | L. Pellissier, E. J. Harris, J. Payne, M. Volpi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Students are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The students are able to ● frame a data science problem and build a hypothesis ● describe the steps of a typical data science project workflow ● conduct selected steps of a workflow on specifically prepared datasets, with a focus on choosing, fitting and evaluating appropriate algorithms and models ● critically think about the limits and implications of a method ● visualise data and results throughout the workflow ● access online resources to keep up with the latest data science methodology and deepen their understanding | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | ● The data science workflow ● Access and handle (large) datasets ● Prepare and clean data ● Analysis: data exploratory steps ● Analysis: machine learning and computational methods ● Evaluate results and analyse uncertainty ● Visualisation and communication | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | 252-0840-02L Anwendungsnahes Programmieren mit Python 401-0624-00L Mathematik IV: Statistik 401-6215-00L Using R for Data Analysis and Graphics (Part I) 401-6217-00L Using R for Data Analysis and Graphics (Part II) 701-0105-00L Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-3003-00L | Environmental Systems Data Science: Machine Learning Number of participants is limited to 80. Course registration starts on 31.08.2022. Priority is given to the target groups until 23.09.2022, Target groups Agricultural Sciences MSc Environmental Sciences MSc Atmospheric and Climate Science MSc Environmental Sciences PhD Agricultural Sciences PhD Waiting list will be deleted on 30.09.2022 | W | 3 credits | 2G | L. Pellissier, E. J. Harris, J. Payne, M. Volpi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Students are introduced to advanced data science where environmental data are analyzed using state of the art machine learning methods. Starting from known statistical approaches, they learn the principle of more advanced machine learning methods with practical application. The course enables students to plan their own data science project in their specialization and to apply machine learning mode | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The students are able to • select an appropriate model related to a research question and dataset • describe the steps from data preparation to running and evaluating models • prepare data for running machine learning with dependent and independent variable • build and validate regressions and neural network models • understand convolution and deep learning models • access online resources to keep up with the latest data science methodology and deepen their understanding | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | • The data science workflow • Data preparation for running and validating machine learning models • Get to know machine learning approaches including regression, random forest and neural network • Model complexity and hyperparameters • Model parameterization and loss • Model evaluations and uncertainty • Deep learning with convolutions | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Building on existing data science resources | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Math IV, VI (Statistics); R, Python; ESDS I | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 651-4273-00L | Numerical Modelling in Fortran | W | 3 credits | 2V | P. Tackley | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course gives an introduction to programming in Fortran, and is suitable for students who have only minimal programming experience. The focus will be on Fortran 95-2018, but differences to Fortran 77 will be mentioned for those working with already-existing codes. A hands-on approach will be emphasized rather than abstract concepts. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Fortran is a modern programming language that is updated every few years (most recently in 2018) and is specifically designed for scientific and engineering applications. This course gives an introduction to programming in this language, and is suitable for students who have only minimal programming experience, for example with MATLAB scripts. The focus will be on Fortran 95-2018, but differences to Fortran 77 will be mentioned for those working with already-existing codes. A hands-on approach will be emphasized rather than abstract concepts, using example scientific problems relevant to Earth science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | See http://jupiter.ethz.ch/~pjt/FORTRAN/FortranClass.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 651-4273-01L | Numerical Modelling in Fortran (Project) Prerequisite: 651-4273-00L Numerical Modelling in Fortran | W | 1 credit | 1U | P. Tackley | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course gives an introduction to programming in Fortran, and is suitable for students who have only minimal programming experience. The focus will be on Fortran 95-2018, but differences to Fortran 77 will be mentioned for those working with already-existing codes. A hands-on approach will be emphasized rather than abstract concepts. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Fortran is a modern programming language that is updated every few years (most recently in 2018) and is specifically designed for scientific and engineering applications. This course gives an introduction to programming in this language, and is suitable for students who have only minimal programming experience, for example with MATLAB scripts. The focus will be on Fortran 95-2018, but differences to Fortran 77 will be mentioned for those working with already-existing codes. A hands-on approach will be emphasized rather than abstract concepts, using example scientific problems relevant to Earth science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The project consists of writing a Fortran program to solve a problem agreed upon between the instructor and student; the topic is often related to (and helps to advance) the student's Masters or PhD research. The project is typically started towards the end of the end of the main Fortran class when the student has acquired sufficient programming skills, and is due by the end of Semesterprüfung week. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | See http://jupiter.ethz.ch/~pjt/FORTRAN/FortranProject.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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