Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

Thessa Tormann: Catalogue data in Autumn Semester 2016

Name Dr. Thessa Tormann
Schärbächlistrasse 3
8810 Horgen
DepartmentEnvironmental Systems Science

651-4271-00LData Analysis and Visualisation with Matlab in Earth Sciences3 credits2GS. Wiemer, G. De Souza, T. Tormann
AbstractThis lecture and the corresponding exercises provide the students with an introduction to the concepts and tools of scientific data analysis. Based on current questions in the Earth Sciences, the students solve problems of increasing complexity both in small groups and singly using the software package MATLAB. Students also learn how to effectively visualise different kinds of datasets.
ObjectiveThe following concepts are introduced in the course:
- Effective data analysis and visuatlisation in 2D and 3D
- Working with matrices and arrays
- Programming and development of algorithms
- Learning to effectively use animations
- Statistical description of a dataset
- Interactive data-mining
- Uncertainty, error propagation and bootstrapping
- Regression analysis
- Testing hypotheses
701-0035-00LIntegrated Practical Observation Networks Information 1.5 credits4PJ. Henneberger, T. Tormann
AbstractObservation networks - the combination of individual instruments - are the starting point of quantitative environmental studies. The structure and idiosyncrasies of existing observation networks are shown. When working in individual experiments on practical problems, various types of observation networks are dealt with; questions related to data quality and data availability are discussed.
ObjectiveGetting acquainted with existing networks. Insight into problems related to measuring and interpreting multi-dimensional fields of atmospheric physical, atmospheric chemical, and geophysical parameters.
ContentObservation networks for atmospheric physical, atmospheric chemical, geophysical, hydrological and climatological parameters on different scales (synoptic: 1000 km; mesoscale: 100 km, and microscale: 100 m). Combination of surface observation with remotely sensed data (satellite, radar). Solving interpolation problems in multi-dimensional fields of the observed variables. Assessing the representativity of local values, i.e., the directly observed variable in an observation network.
Lecture notesThe script is published anew every year. Apart from the description of the scientific problems to be worked on in individual experiments, it contains some theoretical chapters on observation networks, as well as guidelines for writing and publishing scientific papers. The script can be downloaded as pdf from the course webpage.
LiteratureLiterature is listed in the script.