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# MSc Intro Computational Meteorology Exercises W2024
+Git repo for the exercises.
+The directory `organisational` contains information on the content of the exercises and to get started with the data analysis.
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.phaidra.org/voigta80/msc-intro-computational-meteorology-exercises-w2024.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.phaidra.org/voigta80/msc-intro-computational-meteorology-exercises-w2024/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
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-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
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+---
+title: "262004 UE Introduction to Computational Meteorology - Exercises (2024W)"
+format:
+ pdf:
+ documentclass: scrartcl
+ papersize: letter
+ number-sections: true
+ colorlinks: true
+author:
+ - name: "Aiko Voigt"
+ email: aiko.voigt@univie.ac.at
+ affiliation:
+ - name: Department of Meteorology and Geophysics, University of Vienna
+ url: "https://klimadynamik.univie.ac.at"
+date: today
+date-format: long
+
+bibliography: "C:/Users/Aiko/Dropbox/BibTEX/my_entire_bibliography.bib"
+---
+
+## Logistics
+
+We meet each Tuesday at 9:30 in UZA2 2F513. Note, however, that we do not meet on Oct 15, so the next meeting is on Oct 22. Please use the week of Oct 15 to work through the material on netCDF and xarray given below, and to read @jerez_climatechange_photovoltaicenergy_natcomm2015 and @buster_machinelearngin_renewableenergy_natenergy2024.
+
+## Content of the exercises
+
+We will use ERA5 reanalysis data to compute potential solar energy production and its evolution from about 1950 to today. To this end, we will
+use hourly data from ERA5 for surface downward solar radiation, 2m temperature and near-surface wind speed. To compute the potential solar energy production, we will apply Eqs. 1, 2 and 3 of @jerez_climatechange_photovoltaicenergy_natcomm2015.
+
+ERA5 has a horizontal resolution of 31km. We might also experiment with using ERA5-Land, which has a finer resolution of 9km.
+Moreover, we will study how coarse-graining the input fields to a coarser resolution of 100 km and to daily values affects the calculated
+potential solar energy production. The coarse graining is interesting because it will allow us to link to the type of data that is typically
+provided by global climate models. Some background on why this is interesting is provided in @buster_machinelearngin_renewableenergy_natenergy2024.
+
+## TeachingHub
+
+We will work with the JuypterHub of the Department of Meteorology and Geophysics. You can access it via the Moodle page of the exercise course.
+
+I will make sure that the ERA5 data is available on the JupyterHub.
+
+## Getting started with data analysis
+
+Below are links to tutorials and videos to help you get started with geoscientific
+data analysis in Python. The two most important concepts to make yourself familiar with
+are the netCDF file format and the xarray package. We will make extensive use of both.
+
+For netCDF, I suggest you watch [https://www.youtube.com/watch?v=UvNBnjiTXa0](https://www.youtube.com/watch?v=UvNBnjiTXa0) (@fig-yt-netcdf-climate-unboxed) and[https://www.youtube.com/watch?v=699jkjLJGyM](https://www.youtube.com/watch?v=699jkjLJGyM) (@fig-yt-netcdf-lukedatamanager.png). As you will see in the second video, netCDF files can easily be opened with xarray.
+
+.](yt-netcdf-climate-unboxed.png){#fig-yt-netcdf-climate-unboxed}
+
+.](yt-netcdf-lukedatamanager.png){#fig-yt-netcdf-lukedatamanager.png}
+
+For xarray, I suggest you work through [http://gallery.pangeo.io/repos/pangeo-data/pangeo-tutorial-gallery/xarray.html](http://gallery.pangeo.io/repos/pangeo-data/pangeo-tutorial-gallery/xarray.html). You can also work interactively through the tutorial by clicking on the **launch mybinder** button
+in the left-top corner (@fig-pangeo-xarray-tutorial). This will start a JupyterLab session for the xarray.ipynb notebook. No registration or data transfer is needed, you can work with the notebook right away. See @fig-mybinder-xarray-ipynb.
+
+{#fig-pangeo-xarray-tutorial}
+
+{#fig-mybinder-xarray-ipynb}
+
+## References
+
+
+
+
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