Public Private Partnership of Mesonet and Radar Observations Utilized to Improve Decision Support

Mesonets can be combined with remotely sensed data, like radar and satellite, to get an accurate depiction of real-time weather conditions at the neighborhood-level. A combined hyper-local dataset utilizing surface and boundary layer measurements allows those on the ground to monitor, adapt, and make swift decisions in response to severe weather.

Understory is a weather hardware and analytics company that manufactures, deploys, and operates weather networks inside metropolitan areas to create unprecedented detail of how weather affects people and businesses at the ground level. Understory is partnering with CASA (Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere), which operates and maintains a network of seven X-band radars in the Dallas-Fort Worth Metroplex as part of a living lab to create a smarter severe weather warning system. Data from Understory’s network of over 140 sensors in the Dallas-Fort Worth area is ingested into the CASA data stream in real-time, which can then be viewed in a web browser as well as a mobile app. Understory data is used to alert users in emergency management to dangerous wind and hail conditions in their area in real-time. Understory’s ground observations can also be used to influence the radar scan strategy, enhancing CASA’s ability to identify gust features in mesoscale convective systems.

CASA and Understory have been participating in a public-private partnership throughout 2017. Initial results from the 2017 spring storm season in Dallas-Fort Worth highlight the power of this partnership in action, the CASA-Understory dataset has been used to directly impact decisions on the ground by emergency managers and field operations supervisors. Preliminary analysis of the combined dataset shows promise for further research in data assimilation, modeling, and algorithm enhancement.

The partnership shows a path forward for future collaboration around forecasting and the benefits to creating a combined dataset for warnings and decision support.