Issue on page /example_era5.html
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1k
- Forks
- 174
- PR merge metrics
- No merged PRs in 30d
Description
To predict weather data we need to provide some historical data in the format which is mentioned in this link:
Example: Predictions for ERA5 — Aurora: A Foundation Model of the Atmosphere
But one item that I need to set in meta data apart from lat and long is time which I believe is forecast time.
metadata=Metadata(
lat=torch.from_numpy(surf_vars_ds.latitude.values),
lon=torch.from_numpy(surf_vars_ds.longitude.values),
time=(surf_vars_ds.valid_time.values.astype("datetime64[s]").tolist()[i],),
atmos_levels=tuple(int(level) for level in atmos_vars_ds.pressure_level.values),
),
but why would I want to forecast weather in the past, I already know what the weather was in the past and in fact I am providing that to model. I expect the model to be able to forecast weather data in future let say at lest say 1 hour from now. but when I pass any forecast date in future, or even in last few days it says it can forecast data 7 days ago or something. What am I missing?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with example_era5.html and the Metadata construction using valid_time and time. Reproduce the behavior with historical and future timestamps, then trace how the model interprets the supplied time. Done means the expected forecast-time behavior is established and the example or implementation clearly handles it.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100