CLARA environmental intelligence.

CLARA urban environmental digital twins.

Starting from a 3D model of a city, CLARA ingests live data from Earth observation, ground sensors and weather forecasting services into its urban physics simulator based on Computational Fluid Dynamics, accelerated with machine learning, to deliver fast and accurate digital twins of the urban environment: wind, air quality, temperature and, soon, noise.

Cities need this level of detail because conditions change from one street to the next. A building corner turns a breeze into dangerous gusts, a street canyon holds traffic pollution while the avenue nearby stays clean, and a paved square can run several degrees hotter than a shaded courtyard around the corner. Data averaged over a whole neighbourhood misses all of this, and with it the answers people and administrations actually need: which route, which square, which building.

Standard tools stop well before the street. Regional air quality models work at resolutions of a hundred meters to several kilometers¹, the weather models behind consumer wind and temperature forecasts at 1 kilometer or more², satellite observations at a hundred meters to several kilometers³, and official noise maps are static documents, redrawn every few years⁴. CLARA resolves the city at 1 meter, street by street, building by building, across all of its layers.

Accuracy is the other difference. Most alternative services spread sensor readings over the map with simplified statistical models, or rely on data alone. CLARA works the other way around: its engine solves the fundamental laws of physics (wind motion, pollutant dispersion, heat transfer) on hundreds of millions of computational cells, and uses sensor and satellite data to estimate what the physics alone cannot know. Being physics based makes CLARA accurate in places where no sensor exists, and predictive in conditions that have never been measured, including scenarios that have not happened yet.