The three-layered WINDSTM platform elevates numerical weather prediction (NWP) to weather intelligence and commercial decision support.

The layers individually, and in unison, enable users across weather-sensitive industries to gauge the impact of daily forecasted weather, quantify the probability of extreme weather events, and estimate how the interaction between weather and operating context can manifest in unique strategic and tactical challenges to their business needs.

Layer 1: Numerical Weather Prediction (NWP) down to 1KM Resolution

The foundation of our weather intelligence platform is built upon the world-class weather forecasting and atmospheric science for which DRI is known. 

Our deep expertise in high performance computing synergizes with decades of experience in state-of-the-art  high resolution numerical weather prediction techniques. 

Layer 2: NWP and Ground-Truth Sensor Networks

DRI expertise in deploying precise sensor networks targeted to the application imparts accuracy and reliability in forecasting. Our statistical interface integrates distributed sensors on the ground with remote sensing data from satellites.

The WINDS TM platform with our expertise in forecast verification pulls weather forecasts to the ground.

Specialized insight is gained into day-of and multi-day horizon outlooks for energy demand, renewable generation, and evaporative water stress at the scales of neighborhoods and farms.

This is accomplished through a deeply intelligent and robust application of our forecast post-processing techniques. These techniques rely on fine-scale knowledge of the local environment and are built on decades of observational network deployments by DRI scientists across a vast array of science disciplines.

Layer 3: NWP + Sensor Networks = Decision Support

Pulling weather forecasts to the ground creates weather intelligence.  Weather affects landscapes differently, creating microclimates. Using proprietary built-environment modeling[e1]  by DRI scientists, interactions between weather and the surfaces of an urban landscape can be modeled.  Predictions quantifying the impacts of dynamic processes such as heat island effects, energy demand, and evapotranspiration are produced.

The result of this complex sensitivity analysis is an operationalization of weather forecasting, informing decisions on high-value, high-impact economic, resource, and health outcomes.