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---
language: en
license: mit
library_name: pytorch
---
# Cloudcasting
## Model Description
These models are trained to predict future frames of satellite data from past frames. The model uses
3 hours of recent satellite imagery at 15 minute intervals and predicts 3 hours into the future also
at 15 minute intervals. The satellite inputs and predictions are multispectral with 11 channels.
See [1] and [2] for the repo used to train these model.
- **Developed by:** Open Climate Fix and the Alan Turing Institute
- **License:** mit
# Training Details
## Data
This was trained on EUMETSAT satellite imagery derived from the data stored in [this google public
dataset](https://console.cloud.google.com/marketplace/product/bigquery-public-data/eumetsat-seviri-rss?hl=en-GB&inv=1&invt=AbniZA&project=solar-pv-nowcasting&pli=1).
The data was processed using the protocol in [3]
## Results
See the READMEs in each model dir for links to the wandb training runs
## Usage
The models in this repo have slightly different requirements. The SimVP and eartherformer models
require [1] to be installed and the IAM4VP model requires [2].
The SimVP and earthfomer models can be loaded like:
```{python}
import hydra
import yaml
from huggingface_hub import snapshot_download
from safetensors.torch import load_model
REPO_ID = "openclimatefix/cloudcasting_example_models"
REVISION = None # None for latest or set <commit-id>
MODEL = "simvp_model" # simvp_model or earthformer_model
# Download the model checkpoints
hf_download_dir = snapshot_download(
repo_id=REPO_ID,
revision=REVISION,
)
# Create the model object
with open(f"{hf_download_dir}/{MODEL}/model_config.yaml", "r", encoding="utf-8") as f:
model = hydra.utils.instantiate(yaml.safe_load(f))
# Load the model weights
load_model(
model,
filename=f"{hf_download_dir}/{MODEL}/model.safetensors",
strict=True,
)
```
The IAM4VP model can be loaded like
```
from huggingface_hub import snapshot_download
from ocf_iam4vp import IAM4VPLightning
REPO_ID = "openclimatefix/cloudcasting_example_models"
REVISION = None # None for latest or set <commit-id>
# Download the model checkpoints
hf_download_dir = snapshot_download(
repo_id=REPO_ID,
revision=REVISION,
)
model = IAM4VPLightning.load_from_checkpoint(
f"{hf_download_dir}/iam4vp/iam4vp_checkpoint_0.4.3.ckpt",
num_forecast_steps=12,
).model
```
See the cloudcasting package [3]
### Packages
- [1] https://github.com/openclimatefix/sat_pred
- [2] https://github.com/alan-turing-institute/ocf-iam4vp
- [3] https://github.com/alan-turing-institute/cloudcasting
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