Phase
1
Architecture foundation established
We have engineered a CNN-U-Net hybrid engine capable of mapping complex, multi-modal atmospheric inputs into high-resolution spatial representations, effectively solving the signal heterogeneity challenge for 1km-grid forecasting.
Phase
2
Data consistency secured
We have architected a unified spatiotemporal assimilation pipeline that standardizes diverse environmental signals, from satellite observations and numerical models to static surface priors. This process neutralizes data noise and provides a normalized input stream for stable model ingestion.
Phase
3
Training and empirical validation
We will initiate neural engine training by integrating static environmental priors. This phase will validate physical consistency on a 1km grid and establish inference accuracy benchmarks against conventional numerical forecasting baselines.
Phase
4
Lightweight deployment and service integration
We will optimize inference speed via model compression to ensure efficient deployment across diverse hardware architectures. This phase will focus on delivering robust, domain-specific APIs that enable seamless integration into enterprise workflows.
