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