Data assimilation (DA) involves combining information from observations and “models” of a particular physical system in order to best define and understand the evolving state of the system. It is currently applied across a wide range of Earth sciences, including weather forecasting, oceanography, atmospheric chemistry, hydrology, and climate studies. This course provides an introduction to the theory and applications of DA in atmospheric and related sciences. Topics include common DA methods like optimal interpolation, Kalman filtering and variational schemes within the context of estimation theory. The course is designed as a hands-on approach to key DA concepts that are currently used today
My intent as an instructor is to convey applied concepts of data assimilation such that students will:
- gain an understanding of basic principles of current DA algorithms
- think holistically of data and models in the context of associated errors
- grasp the significance of current DA algorithms in analysis and forecasting research and operations
- know how to use DA techniques to real world applications (your own research).
This Git repository hosts Python versions of the class exercises and homework as the original solutions are in Matlab.
- Go to https://colab.research.google.com/ (Gmail sign-in required)
- You should see a screen like this, if not, go to File -> Open notebook
- In the Open notebook window, click on Github and paste this link in the search bar: https://github.com/chayanroyc/ATMO545_2024S
- Click on the search button to find the notebooks from the repository.
- Click on the notebook you need to interact with.
- Once the notebook opens, wait for a few seconds for the kernel to connect. Then go to Runtime in the menu bar and click Run all
- At the end of the notebook, the interactive widget should appear.