WUR-AI / WUR-AI/diffWOFOST

[Epic 2]: Data assimilation in Wofost72_WLP_CWB​ using pytorch

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Epic 2
Dominant language
Python
Stars
25
Forks
6
Avg merge
2d 23h
Merged PRs (30d)
1

Description

For use cases 1 and 2, we use the model Wofost72_WLP_CWB and explore the Variational DA. The config of Wofost72_WLP_CWB consists:

SOIL = WaterbalanceFD
CROP = Wofost72

this is related to use case 1.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Read pcse/models.py at Wofost72_WLP_CWB and pcse/conf/Wofost72_WLP_CWB.conf first, confirming the SOIL and CROP configuration. Then clarify the variational data-assimilation scope for use cases 1 and 2 and define validation criteria, since the issue does not specify a concrete code change or done condition.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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