Implementing open-source AI models for weather prediction involves leveraging pre-trained models, open datasets, and cloud-based tools to generate accurate forecasts with reduced computational costs. The process is now accessible to researchers, developers, and even hobbyists thanks to initiatives by organizations like ECMWF, NVIDIA, and Google, which provide frameworks, pre-trained weights, and step-by-step guides. Key models such as PanguWeather (Huawei), FourCastNet (NVIDIA), GraphCast (Google DeepMind), and FuXi are leading the field, offering performance comparable to traditional numerical weather prediction (NWP) systems but with significantly lower resource requirements.

• Core models available: PanguWeather, FourCastNet, GraphCast, and FuXi are the most widely used open-source AI models, each with public repositories and pre-trained weights ^[2][4].
• Data sources: Open datasets like ECMWF’s MARS, Copernicus Climate Data Store, and NOAA’s reanalysis data are essential for initializing models and training custom versions ^[1][2].
• Implementation platforms: Google Colab, local GPU/CPU setups, and cloud-based HPC (High-Performance Computing) environments are common deployment options, with Colab offering free tier access ^[5][3].
• Key prerequisites: Python 3.10+, CUDA (for GPU acceleration), and basic familiarity with AI frameworks like PyTorch or TensorFlow are required for most implementations ^[1][2].

Implementing Open-Source AI Weather Models

Selecting and Setting Up AI Models

The first step in implementation is choosing an appropriate AI model based on your use case, computational resources, and desired forecast resolution. Models like GraphCast and FourCastNet are optimized for global forecasts, while others like PanguWeather excel in regional predictions. The ECMWF’s ai-models repository serves as a central hub for accessing these models, providing installation scripts and documentation for seamless integration.

• Model selection criteria:
• GraphCast (Google DeepMind): Best for high-resolution global forecasts with a 0.25° grid; requires GPU for optimal performance ^[2][6].
• FourCastNet (NVIDIA): Optimized for NVIDIA GPUs; supports adaptive computation for variable resolutions ^[3][2].
• PanguWeather (Huawei): Focuses on tropical cyclone and extreme weather prediction; trained on 39 years of ERA5 reanalysis data ^[2].
• FuXi: Developed by the Chinese Academy of Sciences; emphasizes probabilistic forecasting for uncertainty quantification ^[4].
• Installation process:
• Clone the ai-models repository: git clone https://github.com/ecmwf-lab/ai-models ^[1].
• Install dependencies via pip install ai-models or environment-specific commands (e.g., conda create -n ai-weather python=3.10) ^[1][4].
• Download pre-trained weights for the selected model (e.g., ai-models install pangu-weather) ^[1].
• Verify GPU compatibility with CUDA 11.8+ for accelerated inference ^[3].
• Data requirements:
• Initial conditions must be sourced from reanalysis datasets like ERA5 (available via Copernicus Climate Data Store) or ECMWF’s MARS archive ^[1][2].
• Input data should be in GRIB or NetCDF format, with spatial resolutions matching the model’s training parameters (e.g., 0.25° for GraphCast) ^[1].

Running Forecasts and Visualizing Results

Once the model and data are prepared, generating forecasts involves executing inference scripts and post-processing the outputs. Most models support command-line interfaces (CLI) or Python APIs for flexibility. Visualization tools like Matplotlib, Cartopy, or ECMWF’s Metview can then render forecast maps for temperature, precipitation, and wind patterns.

• Forecast generation workflow:
• Initialize the model with current weather data: ai-models run --model graphcast --input era5_20240926.grib --output forecast.grib ^[1].
• Specify forecast lead time (e.g., 24–72 hours) and spatial domain (global or regional) in the configuration file ^[2].
• Run on GPU for faster inference (e.g., FourCastNet processes a 10-day forecast in ~2 minutes on an A100 GPU vs. ~30 minutes on CPU) ^[2].
• Output formats and visualization:
• Default output is in GRIB format, convertible to NetCDF or GeoTIFF using tools like wgrib2 or xarray ^[1].
• Use Google Colab notebooks for interactive visualization, such as the ‘Running_AIWP.ipynb’ template, which includes pre-built plots for temperature anomalies and precipitation ^[5].
• Compare AI forecasts against ground truth (e.g., ECMWF’s operational NWP) using metrics like Root Mean Square Error (RMSE) or Anomaly Correlation Coefficient (ACC) ^[2].
• Performance considerations:
• GPU acceleration reduces inference time by 90%+ compared to CPU-only setups ^[2][3].
• Memory requirements scale with resolution: a 0.25° global forecast may need 16–32GB VRAM ^[2].
• Cloud platforms (e.g., Google Colab Pro, AWS EC2) offer cost-effective access to high-end GPUs for occasional users ^[5].

For advanced use cases, such as probabilistic forecasting or ensemble generation, models like GenCast (discussed in Nature) can be integrated to quantify uncertainty in predictions ^[7]. However, these require additional computational resources and expertise in ensemble post-processing techniques.