Leveraging Private Real-Time Weather Data to Save Lives

Introduction

Extreme weather events are becoming more frequent and severe due to climate change, placing immense pressure on public safety systems across the United States. While traditional meteorological services such as the National Weather Service (NWS) provide essential forecasting, they often lack the hyper-local granularity and real-time responsiveness required for effective emergency decision-making. The integration of private sector real-time weather data solutions offers a compelling opportunity to enhance public safety, particularly in resource-constrained jurisdictions.

This article explores how private weather data providers can enhance storm detection, warning dissemination, and decision-making to protect lives. We examine case studies from recent flooding in Texas and Western North Carolina and analyze scholarly research to highlight practical, scalable solutions that enable public safety professionals to do more with less. While many operations are still ongoing in Texas, many lessons are able to be learned to be better prepared, as the weather is not going to wait for things to change.

The July 2025 Texas Flash Floods: A Case of Missed Opportunities

Between July 4 and July 7, 2025, Central Texas experienced catastrophic flash flooding. Torrential rainfall exceeding 20 inches in some areas caused the Guadalupe River to rise over 25 feet in less than 90 minutes, resulting in over 100 fatalities, including multiple children at a local summer camp. Despite the issuance of NWS warnings, Kerr County lacked sufficient alerting infrastructure, such as flood sirens and high-water sensors, delaying evacuations and contributing to the death toll.

Traditional forecasting systems were not the root issue; rather, it was the gap between forecast issuance and local response capabilities. Scholarly literature supports the value of integrating real-time, localized data to fill this critical gap. For instance, Sung, Devi, and Hsiao (2022) developed an AIoT-based flash flood early warning platform utilizing LoRa sensors and fuzzy logic to issue real-time alerts. Such systems can dramatically reduce lag time in flood detection and alert dissemination.

Hurricane Helene and Western North Carolina’s Evolving Resilience Strategy

In September 2024, Hurricane Helene brought over 30 inches of rain to Western North Carolina, leading to widespread landslides, infrastructure failure, and over 100 fatalities. The state’s preparedness efforts, bolstered by experiences from Hurricanes Matthew (2016) and Florence (2018), included pre-positioning National Guard units and deploying swift-water rescue teams. However, gaps remained in road inundation modeling and mountain-valley flood dynamics.

Research supports the integration of artificial intelligence (AI) and Internet of Things (IoT) devices for predictive flood modeling. Dong et al. (2020) introduced a hybrid deep learning model using sensor network data to forecast flood events with 97.8% accuracy. These models, when integrated with public safety decision-making frameworks, can enable faster, more targeted evacuations.

Real-Time Technologies: Enhancing Detection and Decision-Making

Private sector solutions offer modular, scalable systems for flood detection and alerting. AIoT frameworks combine sensor data, machine learning, and geospatial analytics to provide hyper-local, real-time intelligence. Systems like those proposed by Yuan et al. (2021) and Hadi et al. (2020) include the use of rainfall gauges, ultrasonic flow meters, and cloud-based dashboards to track changing conditions and trigger alerts.

Yuan et al. (2021) emphasized the value of integrating traffic, hydrological, and social media data for enhanced flood risk awareness and emergency response. Their system, "Smart Flood Resilience," provides real-time dashboards to emergency managers, allowing for proactive rather than reactive response strategies.

Operationalizing Private Data in Public Safety Frameworks

Integrating private real-time weather systems into public safety operations requires more than technology; it demands planning, training, and governance.

• Technical Integration: APIs must feed directly into emergency operations centers (EOCs), computer-aided dispatch (CAD) systems, and geographic information systems (GIS).

• Policy Adaptation: Emergency Operations Plans (EOPs) and Standard Operating Procedures (SOPs) should codify thresholds for private data to trigger public warnings.

• Training and Exercises: Regular drills involving private sector partners ensure seamless coordination during actual events.

Cost-Efficient Strategies for Resource-Constrained Jurisdictions

Many counties and municipalities lack the capital for large-scale infrastructure investments. However, private sector solutions offer subscription-based, modular deployments that are financially attainable. Leveraging federal and state grant funding, such as FEMA’s Building Resilient Infrastructure and Communities (BRIC) program, can facilitate initial setup, while shared service models reduce long-term costs.

Public safety officials can further stretch resources by:

• Utilizing existing infrastructure (e.g., telecom towers, utility poles) for sensor placement

• Partnering with local universities for data analysis

• Implementing geofenced alerting to reduce over-warning and alert fatigue

Lessons Learned and Best Practices From Texas and North Carolina’s experiences, several key insights emerge:

• Redundancy is essential: Overreliance on a single source (e.g., NWS alerts) increases risk.

• Localized alerts drive compliance: Hyper-targeted warnings are more actionable than county-wide messages.

• Data must translate into action: The existence of sensors is meaningless without protocols for response.

Conclusion

Real-time private weather data systems are not a luxury; they are a strategic imperative. By integrating these technologies into public safety frameworks, jurisdictions can detect threats earlier, alert more effectively, and ultimately save more lives. As climate-related disasters increase in frequency and severity, investing in real-time detection and response capabilities represents not only a best practice but a moral obligation.

Understanding that this just scratches the surface of the events and that there are ongoing operations still, we have to do what we can to make sure that we prevent further loss of life. A service that I have worked with that feels the same way is BAM Weather out of central Indiana. They have always been about protecting others from dangerous storms. These past events are no different. Everyone needs reliable weather information to be able to react promptly to better stay safe.

We must take the lessons of these horrible events and the loss of life, and do what we can to prevent tragedies like these from happening in the future. Summit Response Group works with BAM Weather for not only our operations but to help governments, public safety agencies, and businesses work to keep their greatest assets safe and operations moving forward with the least impact to operations.

References Dong, S., Yu, T., Farahmand, H., & Mostafavi, A. (2020). A hybrid deep learning model for predictive flood warning using channel sensor data. arXiv preprint arXiv:2006.09201.

Hadi, M. I., Yakub, F., Fakhrurradzi, A., Hui, C. X., & Azizan, A. (2020). Designing early warning flood detection and monitoring system via IoT. IOP Conference Series: Earth and Environmental Science, 479(1), 012016.

Sung, W.-T., Devi, I. V., & Hsiao, S.-J. (2022). Early warning of impending flash flood based on AIoT. EURASIP Journal on Wireless Communications and Networking, 2022(1), 1-17.

Yuan, F., Fan, C., Farahmand, H., Coleman, N., Esmalian, A., Lee, C.-C., & Mostafavi, A. (2021). Smart flood resilience: Harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness. arXiv preprint arXiv:2111.06461.