I’m always interested in learning about how drones are being used for the benefit of humanity, and was curious to learn more about how they are being used in weather forecasting. I did a little bit of research to find out what weather drones are and how they are used in predicting the weather. Here’s what I learned.
Weather drones are specially designed drones that are flown into the lowest layer of Earth’s atmosphere, the boundary layer. They are equipped with sensors to gather information about temperature, humidity and wind in the atmosphere, ultimately to help improve weather forecasting models.
Using drones to collect atmospheric samples is a huge advance on traditional methods of data collection, and has the potential to vastly improve the accuracy of weather forecasting models. Why does it matter? Having more accurate models affects both the day-to-day and the big picture. It allows meteorologists to give us better 10 day weather forecasts, but more than that, it means the ability to give more advanced warning for storms such as tornadoes, or whether and where hurricanes will land.
The lowest layer of the atmosphere, called the boundary layer, is where most of our weather is happening. The weather conditions that impact each and every one of us are dependent on a dizzying number of variables and factors. Trying to make accurate predictions about what the weather will do next relies on very complicated weather forecasting models, but the output of these models is only as good as the data fed into them. And getting good data is harder than you might think.
That’s where weather drones come in. Weather drones can be flown through the entire vertical layer of the boundary layer of the atmosphere, and collect crucial data on temperature, moisture, air pressure and wind speed and direction.
Weather drones can collect this data in several ways. One way is through temperature, humidity and air pressure sensors affixed directly onto the drone. Another way they collect data is by dropping sensors called dropsondes, fitted with a parachute, from a high altitude. The dropsondes descend through the vertical profile of the boundary layer, collecting data all the way down. One final important way weather drones collect data is through visual imaging, including photographs and video.
An Improvement Over Traditional Methods
Traditionally, scientists have used weather balloons and weather stations to collect weather data for modeling. The limitations of a weather station are its static location (it doesn’t move), and its proximity to the earth’s surface. Even if a weather station is located at the top of a skyscraper or a tower, it’s fairly earthbound, and can’t get data from the upper reaches of the boundary layer. Weather balloons can get up much higher, but they have the disadvantage of not being able to be controlled. They will go where the wind blows them. Weather balloons also do not return to land from where they launched, and are generally unretrievable.
Satellites are another source of data for weather forecasting models. They are able to obtain data on water vapor and cloud formation, but are not that useful in measuring temperature, humidity or wind, as they are obviously well above the boundary layer where these factors are occurring. High altitude, long-range, fixed wing weather drones have also historically been used, but have similar limitations to satellites, and are extremely expensive to operate. Airplanes are also used to collect weather data, and are frequently used in storm events. However, airplanes are also expensive to operate, and sending a pilot flying into a storm carries a much higher degree of risk to human life than sending a drone.
Drones do not have these limitations. They can easily be flown at high altitudes, and scientists were even recently given permission to fly as high as 3,500 feet above ground level for data collection studies. (As high as it sounds, even a standard consumer quadcopter would have no trouble operating at this elevation, aside from the 400 ft AGL maximum flight restriction). They are easily controlled to head straight into the wind, or even into the storm, to get data at precisely the elevation and location that the scientists would like. And the drones return to land, which means that much more expensive and sophisticated sensors and equipment can be used on a drone than would be practical to use on a weather balloon.
There are several important projects ongoing that are working to make use of and improve drones for weather data collection. One of these is directly connected to scientists working with the NOAA. They are working on short term field studies focused on specific regions of the US to study how terrain and land surface features affect weather patterns. The goal is to determine whether and how the land surface impacts weather, and how to incorporate that variable into weather and climate models.
Another ongoing project, directed by Professor Phillip Chilson at the University of Oklahoma, is in its planning and development stage, but has the potential to revolutionize meteorological science and weather forecasting. He envisions a nation-wide network of weather station towers, each equipped with a fleet of autonomous weather drones. The drones at each station would be deployed hourly to launch and collect data to be fed into weather models, providing huge amounts of reliable data – much more efficiently and reliably than any other method currently employed.
In addition, a swarm of communicating, interconnected drones at each station would be able to be automatically deployed when storm conditions are detected, and use AI to determine where to fly and collect relevant data. This data again, would be fed into weather prediction models.
More data from all over the nation (and the world), from more levels of the boundary layer, means more accurate weather forecasting models. Measurements from storm drones can help scientists evaluate current weather models, and identify and correct forecast models. Collaboration between scientists working on these data collection projects and local weather service forecast models is resulting in more accurate weather forecasts for local weather reporting. It’s a standing joke that meteorologists get to be wrong all the time with no one holding them accountable, but that could change with more accurate models.
Having an accurate and reliable weather forecast has much greater importance than simply planning which day to have a picnic, or whether to bring along your umbrella. When storms are developing, having the best possible weather model can help reduce impact on life and property.
In tornado prone regions, the current average early warning time is 16 minutes. This means residents are warned only 16 minutes before a likely or known tornado can be expected to reach them. This doesn’t leave sufficient time for evacuation or taking cover. The hopes are that with drone data driven weather predicting, the early warning response time could be increased to up to one hour, which would allow people much better opportunity to secure life and property.
Hurricanes are also notoriously difficult to predict. They can take unexpected paths, or intensify or disperse unpredictably. With improved ability to collect data from within an active storm with drones, meteorologists and weather scientists will be able to make better predictions of hurricane storm paths and intensity. This would allow the weather service to make more informed decisions in regards to evacuations, storm surge prevention measures and where to deploy emergency response teams.
The usefulness of drones in all kinds of uses and applications is still in the discovery stages. Even as they have already proved to be the best technology yet in gathering improved data for weather forecasting models, the technology has not yet fully developed. As the ability of drones to fly in poor weather conditions improves, as the sensitivity and accuracy of measurement sensors on drones increases, as the programming for swarm drone intelligence develops – all these things will add to the ability of drones to change the way we measure and are impacted by our weather.
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