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Experiment 5 v1 — Interpolation DiT (Flow Matching)
Model
3D Diffusion Transformer for atmospheric spatial interpolation using Flow Matching. Takes coarse atmospheric state and generates fine-grained local atmospheric fields.
Station
- Target: RKSI (Incheon International Airport, South Korea)
- Coordinates: 37.4692°N, 126.4505°E
Training Results (Best Epoch 7 / 12)
- Val T2 MAE: 0.9937 K
- Val T2 Bias: 0.9791 K
- Val FM Loss: 0.2880
- LR at best: 8.652e-05
Training Curve
| Epoch | Val FM Loss | T2 MAE (K) | T2 Bias (K) | LR |
|---|---|---|---|---|
| 1 | 1.1642 | 1.1759 | 0.5837 | 1.998e-04 |
| 2 | 0.9870 | 1.2539 | 0.9651 | 1.960e-04 |
| 3 | 0.7686 | 1.4457 | 1.3613 | 1.843e-04 |
| 4 | 0.5216 | 1.4016 | 1.3781 | 1.657e-04 |
| 5 | 0.3710 | 1.5597 | 1.5510 | 1.420e-04 |
| 6 | 0.3259 | 1.6226 | 1.6055 | 1.148e-04 |
| 7 | 0.2880 | 0.9937 | 0.9791 | 8.652e-05 |
Hyperparameters
- Architecture: 3D DiT (Diffusion Transformer)
- Batch size: 16
- Optimizer: Muon (LR=2e-4, WD=5e-3) + AdamW (LR=2e-5)
- Warmup: 1 epoch (cosine decay to 1e-6)
- Total epochs: 12 (early stopped at 7 due to NCCL crash, best=epoch 7)
- Flow steps: 1000
- Loss: Charbonnier + 0.05 * Gradient + 0.05 * LPIPS
- EMA decay: 0.9995
- Early stopping patience: 6
Data
- Training data: ERA5 atmospheric reanalysis (2005-2014)
- Grid: 10°x10° around RKSI at 0.25° resolution
- Variables: 7 atmospheric variables (T2m, U10, V10, SP, T850, U500, V500)
Hardware
- 8x NVIDIA B200 GPUs (DDP via Accelerate)
- Training time: ~7.5 hours
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