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openclimatefix/
cloudcasting_example_models

pytorch

safetensors

mit

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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.

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]

openclimatefix/cloudcasting_example_models