Weather forecasters may not have the best reputation for accuracy, but with today’s computational modeling, it’s possible to make pretty reliable weather predictions up to 48 hours in advance. Researchers at MIT, however, believe that autonomous aircraft running smart storm-chasing algorithms could get that figure up to four days. Better weather forecasting could help farmers and transportation authorities with planning and even save lives by providing earlier warnings about storms and severe weather, says Jonathan How, principal investigator at MIT’s Department of Aeronautics and Astronautics.
Long-term predictions don’t necessarily go wrong because of forecasting models, but rather because initial conditions were inaccurately measured, says Martin Ralph, a research meteorologist at the National Oceanic and Atmospheric Administration’s earth systems laboratory, in Boulder, CO. Such inaccuracies come from gaps in the data, he says.
Ground-based sensors are already used to record temperature, wind speed, humidity, air density, and rainfall, but they gauge conditions only at ground level, says How. At sea, where many severe weather fronts originate, the coverage is much sparser. Satellite observations help build up a picture, but satellites are blind to a number of useful types of data, such as low-altitude wind speed and atmospheric boundary conditions, says Ralph.
To get the most accurate readings, you really want to get your sensors into the weather itself, says How. In theory, weather balloons can do this, but only if they happen to be in the right place at the right time. So weather services currently attempt to track down weather systems using piloted planes that fly prescribed routes, taking measurements along the way. The logistics of deploying such planes is so complicated, however, that it’s difficult to change their routes in response to changing weather conditions.
Consequently, says How, there has been a lot of interest in using unmanned aerial vehicles, or UAVs, instead. The idea is that there would be a constant number of UAVs in the air, continuously working together to position themselves in what would collectively be the most useful locations.
The problem, says How, is that calculating the most useful locations is an enormously complex task. It involves analyzing more than a million data states from hundreds of thousands of sensor locations, and using this data to predict the weather conditions six to eight hours from now. But that’s exactly the challenge that the MIT researchers tackled.
So far, the algorithms they developed have been used only in a simulation, as part of a National Science Foundation project. MIT’s Han-Lim Choi, who has been working on the algorithms as part of his PhD research, presented the latest results of the project last week at the IEEE Conference on Decision Control in Cancun, Mexico. The work has attracted the interest of the U.S. Navy, and the MIT group is applying for funding to put the algorithms into practice, says How.
One of the challenges presented by the project is fuel management, says Dario Floreano, an expert in flying robotics and head of the Laboratory of Intelligent Systems at the École Polytechnique Fédérale de Lausanne, in Switzerland. The algorithms will need to be able to quickly and efficiently reroute the UAVs so that they maintain optimal coverage, he says. “This will have to take into account many variables, including energy requirements for different reallocation strategies.”
Another challenge is size, says Floreano. The UAVs need to be small and safe enough to not harm humans and objects if they are deployed in large numbers. He points out, however, that subkilogram UAVs are now becoming available.
In fact, How and his colleagues are more interested in testing their algorithms on the relatively large ScanEagle UAVs from Boeing, which weigh about 18 kilograms apiece. These would be capable of flying distances in excess of 1,000 miles, even laden with sensors and communications equipment. With this sort of range, a fleet of just four could reasonably cover a good-sized area, reducing the risk of collisions with manmade objects.
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