How meteorological forecasting technologies can improve public safety

How meteorological forecasting technologies can improve public safety

With extreme weather events on the rise as a result of global heating, researchers are working hard to improve the accuracy of meteorological warning systems. From lightning detection systems to pressure and wind detectors, the quality of technologies used in early warning systems is rising at an impressive rate. With this in mind, how can scientists improve public safety via forecasting technologies? And how can they leverage predictive intelligence to mitigate the impact of storms and heatwaves on public health?


Why do accurate local weather forecasts matter?


Weather forecasting helps authorities and individuals make important decisions such as whether to postpone certain events, close roads, or evacuate people from their homes. While regional forecasts are often helpful for making these decisions, lack of detailed and location-specific forecasting can lead to the following problems:


  • Unforeseen repair costs: Property owners need to know when to protect their homes from extreme weather events.
  • Safety risks: Storms, hurricanes, and heatwaves represent a threat to the health and wellbeing of local residents. An accurate weather forecast helps people prepare for the worst.
  • Supply chain disruptions: Weather events can slow down or completely halt vital supply chains, leaving local residents in need of vital supplies.


How are scientists improving weather forecasting and simulation technologies?


High-performance computing (HPC) represents a promising technology in the field of climate research and could help to boost the speed and quality of weather research. Here’s how:


  • It helps scientists deploy data-heavy weather models.
  • It can be used to record highly accurate temporal and spatial weather simulations.
  • It can be used for managing very high and proliferating volumes of data.
  • It can reduce the time it takes to alert authorities to dangers.


Data analysis is very important for meteorologists and climate scientists, providing access to actionable intelligence such as geospatial, climate, and atmospheric data. To collect this data, scientists gather facts and figures from sources such as meteorological sensors, satellites, and weather stations. Once they have collected enough information, they can use visualization technologies to create complex weather models based on the present, past, and future conditions.


Once models have been drawn, researchers can use predictive analytics technologies to come up with accurate weather warnings. Thanks to the proliferation of machine learning in recent years, these predictive capacities are more sophisticated than ever before and help scientists to predict hazardous weather events long before they occur.


How to leverage predictive analytics


Predictive analytics can be applied in the following ways:


  • Boost safety and accountability across a range of contexts: Accurate weather forecasts can help leaders and authority figures make decisions about, for example, setting employees’ work hours, closing schools, evacuating vulnerable areas, and much more.
  • Enhance logistics operations: Location-specific forecasts can help logistics companies and local authorities protect against supply chain issues and organise backup options with alternative vendors.
  • Boost infrastructural resilience: Predictive analytics can help authorities avoid severe weather damage through improved machinery and materials. What’s more, maintenance teams with advance warnings about extreme weather events have a better chance of surviving storms, floods, and other occurrences.