Using Environmental Predictive Analytics to Generate Actionable Information for Weather-Influenced Decisions

SLU ID 10-001 | Environmental Predictive Indicators

Background

More than US$3 billion is spent annually in the United States on weather-related forecasting services on everything from modeling patterns to taking measurements to distributing forecasts. Other predictive techniques for weather patterns are generally performed on a national or multi-state regional scale. This large scale weather prediction is generally insufficient for predicting the location of specific weather conditions with enough precision to ultimately assist decision makers. Largely because of their expense and complexity, traditional monitoring station configurations prevents them from monitoring ground conditions and limits their capabilities to providing predictions of general weather patterns. The environmental predictive indicators system and method, called Quantum Weather®, developed by researchers at Saint Louis University addresses many of the shortcomings of alternative systems.

Overview

Quantum Weather® technology is designed for highly accurate forecasts up to seven (7) days. This provides users with actionable weather-related intelligence with enough advance warning for them to take action. The system generally comprises a high spatial and temporal resolution model driven by mesonet data, networks of surface weather stations, radiosondes, and custom-tuned WRF forecast models.

Benefits

The potential benefits of this technology include:

• Increasing the accuracy of 6-12 hour forecasts

• Increasing accuracy of forecasts out to seven (7) days

• Increasing reporting frequency from weather station networks to near continuous

Applications

Potential applications of this technology include:

• Electric utility industry

• Emergency response and public safety

• Public health

• Agriculture

Opportunity

Saint Louis University is seeking partners to further develop and commercialize this technology.