aiice-bench 1.0.2

A standardized benchmark for spatio-temporal forecasting of Arctic sea ice concentration

Aiice

---

AIICE is an open-source Python framework designed as a standardized benchmark for spatio-temporal forecasting of Arctic sea ice concentration. It provides reproducible pipelines for loading, preprocessing, and evaluating satellite-derived OSI-SAF data, supporting both short- and long-term prediction horizons

Installation

The simplest way to install framework with pip:

pip install aiice-bench

Quickstart

The AIICE class provides a simple interface for loading Arctic ice data, preparing datasets, and benchmarking PyTorch models:

from aiice import AIICE

# Initialize AIICE with a sliding window 
# of past 30 days and forecast of 7 days
aiice = AIICE(
    pre_history_len=30,
    forecast_len=7,
    batch_size=32,
    start="2022-01-01",
    end="2022-12-31"
)

# Define your PyTorch model
model = MyModel()

# Run benchmarking to compute metrics on the dataset
report = aiice.bench(model)
print(report)

Check package doc and see more usage examples. You can also explore the raw dataset and work with it independently via Hugging Face

Anonymous versions for review:

Artifact	Link
📦 Repository	anonymous.4open.science/r/Aiice-0BF8
📖 Documentation	prismatic-baklava-6691d5.netlify.app
🗄️ Dataset	huggingface.co/datasets/anon-aiice/Aiice