Meteogliders

Challenges of Conventional Radiosondes

Radiosondes have long been vital tools for understanding the weather and Earth's atmosphere. These small instruments, attached to weather balloons, ascend into the atmosphere, gathering data on temperature, humidity, pressure, wind speed, and direction. The information they provide feeds into weather forecasts, climate models, and atmospheric studies, helping us better understand and predict atmospheric behavior and its effects on our daily lives.

Despite their importance, radiosondes come with significant challenges. One of the most significant drawbacks of traditional radiosondes is their fate post-launch.

These devices, carried by weather balloons, drift with the wind and are rarely recovered. Of the approximately 600,000 radiosondes launched globally each year, an estimated 80% are lost in nature. This loss makes them costly, single-use devices that contribute to environmental waste.

Pioneering Change: The Meteoglider as a Scalable Solution

While conventional radiosondes have been indispensable for decades, the increasing focus on sustainability and cost-efficiency is driving the search for innovative solutions to overcome their limitations. The Meteoglider is the first tool to succeed at scale.

Meteodrones, designed for long-term use, can be deployed globally by national weather services and private organizations. Capable of multiple daily flights anywhere in the world, they offer the high volume and frequency of data needed to improve weather predictions and address gaps in existing networks.

Meteogliders, on the other hand, provide a short-term, sustainable alternative for national weather services. They allow the continued use of radiosondes while reducing environmental and cost impacts with minimal changes to existing processes.

Together, these technologies complement each other by expanding the scope and accuracy of atmospheric data collection. While Meteogliders ensure critical stratospheric measurements are maintained sustainably, Meteodrones focus on increasing data density in the atmospheric boundary layer, addressing urgent forecasting needs. This combination supports both immediate goals and the long-term transformation of weather observation systems.