Weather and climate modeling
State-of-the-art weather and climate models, dynamically downscaled to street scale and asset level on high-performance computing.
Technology
We run state-of-the-art weather and climate models, downscaled to street scale and asset level on high-performance computing, then apply machine learning and build the tools and workflows that act on the result.
The Tempra stack
Four layers, each one ours: physical modeling, machine learning, operational context, and the workflows that deliver it to the people who act.

Public forecasts are a starting point. Our own model runs resolve weather where it actually lands: along a street, across a site, at a single asset.
Each layer turns raw physics into something a team can act on.
State-of-the-art weather and climate models, dynamically downscaled to street scale and asset level on high-performance computing.
Models that correct forecast bias, learn from what actually happened, and convert a hazard into its expected impact on people and assets.
Sites, assets, work types, exposure and thresholds: what decides whether a given hazard matters to your organization.
Alerts, approvals, records, APIs and integrations that put model output into daily operations, as ready products or built around your process.
Research foundation
The useful layer is not only the forecast itself. It is the translation of changing conditions into where impacts may emerge and what decisions need attention.
GPU-driven flood modeling can turn complex hazard information into a place-based view of where consequences may emerge.
A simulation of flood hazard information moving toward site-level consequence.
Example DPI Climate Hub research/simulation visual used to illustrate spatial hazard intelligence. Read the DPI story.
Positioning
What technical buyers can expect from Tempra.
Forecasts, observations and models do the heavy lifting in the background. What reaches your team is a recommendation it can explain, question and defend.