Version 12 (modified by dgoll, 3 years ago) (diff) |
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Spin up with a Machine Learning approach
What is it about?
Aim: develop a spinup acceleration procedure which is model version independent. The idea is to develop a python tool set which can applied to the ORCHIDEE family of models.
How can I contribute to this effort?
Please contact the D.Goll if you want to join. Some example we would benefit from are:
- data from conventional spinup simulations
- expertise how to link it to other tools, like libIGCM, ORCHIDAS etc.
- expertise how to host/distribute/maintain the software
- machine learning, python
Task force members
Daniel Goll, Yan Sun, Jinfeng Chang, Yilong Wang, Yuanyuan Huang, Vladislav Bastrikov, Nicolas Viovy Matt McGrath?
Status reports
26/01/2021
- DONE: Proof of concept for ORCHIDEE-CNP v1.2
- ONGOING: Finding a common setup for pixel selection applicable to all ORCHIDEE versions
- ONGOING: Collecting data from other ORCHIDEE versions for testing
- ONGOING: Translating matlab into python code
- ONGOING: Cleaning the code
- ONGOING: Recruiting task force members
16/02/2021
Yan gave a presentation on progress with python coding, results on CNP and trunk, and timeline for next 2 months.
- Input files: restart + climate forcing (not hist file as might ORCHIDEE might introduce noise)
- K-means clustering: add plot which shows the total distance vs k to monitor if the chosen number of cluster paranmeter is well chosen (part of the monitoring info for user)
- Add checks and quality statistics to monitor if each steps performs well & stop the procedure is results fail minimum quality criteria (e.g. stop if machine learning fails to predict training pixels)
- Externalize all parameters of the routines in one file.
Work distribution:
- Matt: Provide trunk v4.0 data (EQ files, + results from 200yr after scratch w/o anal spinup)
- Yilong refines & extend coding of tool 1&2
- Run tests with the refined tools for other forcings (everyone)
- Yan will focus next month on PhD defens (20.March)
03/03/2021
- First version of python tools are available for testing
- Yilong gave an overview
Next steps:
- put code and documentation on github (Daniel, Vlad, Yilong)
- add documentation on how to run the tools; adapt them to other models (Yan,Yilong)
- all attempt to run the tools with their model data (keep a log on github about what model data used)
information/suggestions on run the tools:
- user specification files: need more information, e.g. what file name corresponds to Equilibirum information what to info from transient run (Yan)
- things to improve: figure labelling, user spec file (simplify)
- try to use qsub to avoid blocking nodes on obelix
16/03/2021
- github has been setup and some initial test and exchanges were done
- next: everyone try and test the tool on the two available datasets (CNP, trunk); report bugs, improvmenets, etc on github
- ongoing: acquire data from other model (versions): CABLE, ORCHIDEE-MICT, ORCHIDEE-<any>
- next meeting will be scheduled after discussion with Yan after her defence