WRF Diffusion Model

A deep learning model for predicting WRF (Weather Research and Forecasting) data using diffusion models.

Overview

This project implements a 3D U-Net based diffusion model for predicting future WRF-Chem model outputs based on previous inputs. The model is designed to handle the complex multi-variable nature of WRF data including meteorological, chemical, and radiation variables.

Features

• 3D U-Net Architecture: Custom 3D convolutional neural network for spatio-temporal data
• Diffusion Model: State-of-the-art generative modeling approach
• GPU Optimization: Optimized for NVIDIA A800 GPUs with mixed precision training
• Memory Efficient: Handles large 14TB datasets efficiently
• Multi-variable Support: Processes 17 key WRF variables simultaneously
• Time-aware Modeling: Sequential prediction with proper temporal dependencies

Data Structure

The model processes WRF NetCDF files with the following structure:

• Dimensions: 720 time steps × 64 × 64 spatial grid × 45 vertical levels
• Key Variables: 17 selected variables including meteorological and chemical species
• File Size: Individual files are several GB each, total dataset ~14TB

Installation

1. Install required packages:

pip install -r requirements.txt

2. Ensure CUDA 11.8 is installed and compatible with your system

Usage

Data Preprocessing

python scripts/preprocessing/preprocess_data.py \
    --data_dir /path/to/wrf/data \
    --output_dir /path/to/processed/data \
    --num_workers 8 \
    --validate_files

Training

python scripts/training/train.py \
    --data_dir /path/to/processed/data \
    --epochs 1000 \
    --batch_size 8 \
    --learning_rate 1e-4 \
    --device cuda

Evaluation

python scripts/evaluation/evaluate.py \
    --model_path /path/to/checkpoint.pth \
    --data_dir /path/to/test/data \
    --num_samples 50 \
    --save_visualizations

Configuration

The project uses a configuration system with the following key components:

• DataConfig: Data loading and preprocessing parameters
• ModelConfig: Neural network architecture settings
• TrainingConfig: Training optimization parameters
• SystemConfig: System and GPU settings

Model Architecture

3D U-Net

• Encoder: 4 levels with increasing channel dimensions
• Attention Blocks: Self-attention mechanisms at higher levels
• Decoder: Skip connections with upsampling
• Time Embedding: Sinusoidal time encoding for diffusion process

Diffusion Process

• Forward Process: Gradual noise addition over 1000 steps
• Reverse Process: Learned denoising with U-Net
• Sampling: Iterative generation from noise

Performance Optimization

GPU Utilization

• Mixed precision training (FP16)
• Gradient accumulation
• Memory-efficient data loading
• Multi-GPU support

Memory Management

• Chunked data processing
• Smart caching
• Periodic memory cleanup
• Out-of-core data handling

Key Variables

The model focuses on 17 key WRF variables:

• Meteorological: T, U, V, W, PH, P, QVAPOR
• Chemical: O3, H2O2, NO, NO2, SO2, CO, HCHO
• Optical: AOD_OUT, AOD2D_OUT

Monitoring

The system includes comprehensive monitoring:

• Training loss and metrics
• Memory usage tracking
• GPU utilization monitoring
• Automatic checkpointing
• Detailed logging

Error Handling

The implementation includes robust error handling:

• Graceful degradation on missing data
• Automatic retry mechanisms
• Comprehensive logging
• Checkpoint recovery

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Built with PyTorch and modern deep learning tools
• Optimized for NVIDIA A800 GPUs
• Designed for large-scale climate and weather modeling