pages/conclusion/page.md

Conclusion

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The core claims of WIND reproduce at toy scale: a single pre-trained diffusion model solves multiple atmospheric inverse problems (forecasting, downscaling, sparse reconstruction, conservation enforcement, counterfactual generation) via posterior sampling without task-specific fine-tuning. No pretrained weights are available (GitHub issue #2 still open), so we trained from scratch on synthetic data. The inverse problem framework works correctly — the forward operator changes per task while the model stays frozen. Key limitation: a 1.4M-param toy model on synthetic data cannot match the paper's quantitative results on real ERA5 data, which requires ~4x H100 GPUs for ~3 days. HF GPU Jobs were unavailable (insufficient credits).

Scope & cost

	This reproduction	Full replication
Scope	Algorithmic framework: diffusion forcing + inverse problems on synthetic data	Full WIND model on real ERA5 data, 70 channels, 1.5° resolution
Hardware	CPU only (no GPU available)	4x H100 GPUs
Compute time	~9 min (3 runs)	~3 days training + evaluation
Cost	$0 (CPU only)	~$200+ (GPU compute)
Outcome	Framework verified, metrics directionally correct	Full benchmark numbers