OEKB301202 Meteorological data analysis and visualization (in Eng.)

Type
Lecture and exercise
Semester hours
2
Lecturer (assistant)
Kuchar, Ales
Organisation
Meteorology and Climatology
Offered in
Wintersemester 2025/26
Languages of instruction
Englisch
Content

Over the last decades, the global network of ground-based meteorological stations has significantly expanded. At the same time, tremendous progress has been made in observations from remote-sensing platforms, and the skill and resolution of global and regional weather-forecast and climate models have steadily increased. Observational and model output exists in a variety of data formats (csv, grib, NetCDF, tiff, zarr), and analysis frequently requires experience with numerical programming environments. In this class, an overview is given about selected data sets available from in-situ or remote-sensing platforms as well as model archives. Different methodological frameworks and numerical recipes for the analysis of meteorological and climatological data are presented. Theoretical content is supplemented with hands-on experience, with special emphasis on the application of toolboxes available within open-source software. Besides numerical analysis, emphasis is given on the effective visualisation of meteorological and climatological data to ease interpretation.
Previous knowledge expected

None. This course is recommended as a prerequisite to Climate lab (OEKB301211) and Atmospheric and climate modelling (OEKB301208).
Objective (expected results of study and acquired competences)

On successful completion of this module in terms of knowledge, students will be able to:
- Describe the main sources and characteristics of meteorological and climatological data (station data, climate-model outputs, reanalyses, remote sensing products).
- Explain standard formats, structures, and conventions for climate data (e.g. grib, netCDF) and the principles behind tools such as CDO and NCO.
- Summarise basic statistical approaches relevant for climate analysis, including data selection methods and hypothesis testing.

On successful completion of this module in terms of skills, students will be able to:
- Acquire, manage, and process diverse climate datasets using open-source scripting languages (e.g. Bash, Python).
- Apply computational tools to transform and standardize meteorological data for further analysis.
- Perform quantitative analyses to detect variability, trends, and extremes in meteorological and climate datasets.
- Design and generate 2D and 3D visualizations that effectively communicate climate data and analysis results.
- Develop and implement reproducible workflows for climate data analysis and visualization.

On successful completion of this module in terms of attitudes, students will be able to:
- Critically evaluate the strengths and limitations of alternative data-processing approaches for climate research.
- Interpret and contextualize the results of data analysis in terms of physical climate processes and societal relevance.
- Integrate computational, statistical, and visualization methods into independent and collaborative climate research.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.