README.md

---
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