Overview
IPSL-AID enables:
Global-to-regional climate downscaling
Multi-variable spatio-temporal generation
Diffusion-based generative modeling
Scalable training and inference on multi-GPU systems
HPC-friendly workflows using SLURM and batch scripts
The codebase is modular, extensively configurable, and designed to be explored incrementally, starting from unit tests before full model training.
Key Features
Multiple Diffusion Formulations: VE, VP, EDM, iDDPM
UNet-based Architectures: ADM-style and conditional variants
Global Training: Random block sampling across the globe
Flexible Inference: Global, regional, or domain-specific
HPC Integration: SLURM-ready with full reproducibility
Comprehensive Testing: Module-level tests for validation
Use Cases
High-resolution climate projections
Regional climate impact assessments
Ensemble generation for uncertainty quantification
Data augmentation for climate studies
Downscaling of global climate model outputs
Background
Anthropogenic climate change poses substantial risks to critical socio-economic sectors, such as agriculture, forestry, energy, and water supply. The development of effective adaptation and mitigation strategies requires accessible, high-resolution climate projections to anticipate impacts at both local and regional scales.
General circulation models (GCMs), which are widely used in climate research, typically operate at spatial resolutions of approximately 150–200 km. This coarse resolution is insufficient to capture essential fine-scale processes, especially those shaped by topography, land-sea contrast, and surface heterogeneity, all of which are vital to regional climate dynamics and extreme weather events. Therefore, downscaling climate model outputs to finer resolutions is necessary to provide relevant climate information at the local level.
IPSL-AID addresses this need by providing a diffusion-based generative model for efficient climate downscaling with uncertainty estimation.