Traditional risk assessment methods often rely on historical climate data, failing to account for the increasing frequency and intensity of extreme weather events caused by climate change. This makes it difficult for organizations to accurately assess and prepare for future climate-related risks, leading to potential economic losses and operational disruptions.

Celest.Science provides AI-powered climate risk insights, offering sub-seasonal to decadal predictions to help organizations proactively adapt to changing climate conditions. By blending deep learning, generative models, and physics-informed AI, their platform bridges the gap between weather forecasting and long-term climate simulation. This enables users to anticipate extreme climate events, accurately assess physical risks, and diagnose the vulnerability of assets to climate change. The platform delivers probabilistic forecasts that quantify prediction uncertainty, providing a comprehensive view of possible outcomes for enhanced decision-making.

Sub-seasonal to seasonal climate predictions, ranging from weeks to a decade ahead

AI-driven models that leverage deep learning, generative models, and physics-informed approaches

Integration of historical climate data, trends, and current conditions for dynamic risk assessment

Probabilistic forecasts that quantify prediction uncertainty and estimate extreme event occurrences

API access for easy integration into existing workflows

Weekly forecast updates

Celest.Science primarily serves organizations in the insurance, energy, and agribusiness industries, helping them manage climate-related risks, optimize resource allocation, and improve strategic decision-making.