1 km Real-Time Weather Downscaling for India's Capital
The Challenge
State-of-the-art AI weather models like GraphCast produce forecasts at ~25 km resolution — enough to track synoptic weather systems, but far too coarse for urban decision-making.
Delhi NCT is just 1,484 km². At 25 km resolution, the entire megacity fits inside 3 grid cells.
AirCast Delhi bridges this gap by downscaling GraphCast predictions to 1 km resolution using a 3D UNet with Latent Consistency Model (LCM) diffusion — revealing neighborhood-scale temperature gradients, urban heat islands, and local circulation patterns invisible to global models.
Method
From global AI forecast to street-level weather intelligence in seconds.
Google DeepMind's global AI weather model provides 0.25° forcing fields
Latent Consistency Model diffusion, conditioned on topography and sky-view factor
High-resolution grid at 0.01° spacing, 8 surface variables, hourly output
Model Architecture
Results
Watch the diurnal heating cycle unfold at 1 km resolution. Forecast initialized 14 June 2026, 00Z.
Resolution Comparison
Drag the slider to compare GraphCast input (~25 km) with AirCast Delhi output (1 km) at peak afternoon heating.
Validation
Hourly AirCast Delhi forecast (red line) compared against 3-hourly GHCN-H ground truth observations (green diamonds) at Safdarjung station during the May 2024 heatwave.
Technical Specifications