Indus is being deployed first with Pravāh's partner utilities across the Indian subcontinent. The same weather model scales to agriculture, disaster response, and climate adaptation, sectors where a single percentage point of forecast accuracy compounds across millions of decisions, harvests, and lives.
Grid operators need to trust the next 24 hours of wind and solar generation.
Wind and solar capacity is scaling fastest across the Deccan plateau, the Chilean Atacama, and the Horn of Africa, the same regions where reanalysis-based forecasts systematically under-estimate variability. Every one-percent reduction in wind MAE translates to meaningful avoided curtailment and a measurable reduction in the spinning reserve that grid operators must hold.
Indus delivers hub-height wind and GHI/DNI at native resolution, calibrated against station observations, not reanalysis, so the numbers a trader sees are the numbers a physical meter confirms.
Rainfed agriculture dominates the tropics. Planting windows are decided on two weeks of expected rainfall, and those two weeks are exactly where current models blur.
The Indian Monsoon, the West African Sahel, and the South American Altiplano each behave as thousands of sub-regional onsets, break phases, and active spells, not a single event. A block-level forecast error of even 72 hours shifts sowing across millions of hectares, and with it the timing of fertilizer, pesticide, and reservoir draw.
Indus downscales rainfall probability to 2 km, tied directly to gauge observations from national meteorological services, so a district agronomist sees risk at the scale of the decision they need to make.
Evacuation plans hinge on probabilistic tracks, landfall cones, and rainfall bands at horizons that current regional models cannot resolve reliably.
Three cyclone basins dominate the population-weighted exposure map: the Bay of Bengal, the South-West Indian Ocean, and the Caribbean. Each places the tightest demands on forecast accuracy in the day before landfall. MAUSAM's ensemble assessment shows that probabilistic diffusion models already outperform the 50-member IFS ensemble on certain track metrics. The question now is operationalizing them.
Indus outputs 32-member calibrated tracks every 6 hours, benchmarked against IBTrACS best-track data, with explicit landfall-cone and accumulated-rainfall envelopes for emergency operations centers.
The regions most exposed to climate volatility are the ones whose observation networks, compute access, and model customization have been historically last in line.
Global forecast services are trained and validated on geographies with dense station networks. The Indian subcontinent, sub-Saharan Africa, and Latin America inherit models whose error budgets were calibrated somewhere else. Meanwhile, their populations face the sharpest edge of climate volatility: urban heat, coastal flood, rainfall failure.
Indus is built with that asymmetry as its founding premise. A regionally-calibrated, observation-grounded forecast, delivered as a public good where required, is the first piece of climate adaptation infrastructure most governments currently lack.
Weather forecasting is one of the rare planetary-scale public goods. We're rebuilding it for the places that need it most.
