Innovation: Cloud to ground lightning strike prediction
- Dominant language
- Python
- Stars
- 65
- Forks
- 11
- Avg merge
- 21h 25m
- Merged PRs (30d)
- 70
Description
**Describe the task**
Predict ground lightning strikes using model weather data.
The idea is to use machine learning to predict lightning strikes. Lightning strikes is an input into fire start prediction.
Use Global Model + Lightning Observations + Linear Regression to see if it's any good at predicting lightning occurrences in the future. Algorithms to use would be show-alter, sweat, the cape index.
**Acceptance Criteria**
- [ ] Generate raster containing predicted lightning occurrence
- [ ] Generate raster containing observed lightning occurrence
Contributor guide
Research direction
No files, tests, or entry points are named. Start by clarifying the available global model, lightning observations, and proposed algorithms, then trace how weather data is currently ingested. Done means producing both predicted-lightning and observed-lightning rasters as specified by the acceptance criteria.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning, python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100