A walk down the tarmac at the National Air and Space Museum of France is guaranteed to thrill aviation enthusiasts. Several civilian and military aircraft of the past and the present are showcased in this museum, located at the Paris-Le Bourget airport. One of them is the legendary Concorde F-WTSS 001 prototype, the sight of which should give goosebumps to science enthusiasts as well.

That’s because right behind its long nose is a painted map that traces the path of a June 1973 total solar eclipse across Africa. It’s there on the Concorde 001 prototype because a group of eight scientists set a new world record of observing a total solar eclipse for 74 minutes by flying this aircraft at more than twice the speed of sound.

To put this figure into context, the maximum duration of totality on the ground is around 7.5 minutes when an eclipse is observed in the equatorial regions. For the upcoming solar eclipse of August 12, which can be seen from parts of Europe, the maximum duration of totality from the ground will be slightly more than two minutes.

Repurposing a prototype

One of the scientists, and the initiator of the record-creating project, was Pierre Léna. A year before June 30, 1973, Léna, who had carried out astronomical observations from high-altitude aircraft, came up with this idea.

Since the 1973 eclipse was going to be near the equator, the Moon’s shadow would move at a relatively slower speed, although still more than 2.2 times the speed of sound. There was only one big aircraft that came close to matching this speed: The Concorde.

“In 1970, the Concorde 001 prototype flew supersonic for the first time,” Léna told Ars. “The newspapers were full of these results. By 1972, I thought it was worth trying to approach André Turcat, the chief of test flights at Aerospatiale in Toulouse, and ask him about the possibility of using the 001 prototype to follow the June 1973 eclipse. He was very interested.”

With his plan accepted, Léna began organizing experiments for the historic flight. Flying at nearly 17 km above the Earth’s surface had many benefits. “Flying very high in the stratosphere had several advantages, including no humidity, which meant an excellent infrared transmission of the solar light.”

“I had planned an experiment to study the particles of dust in the corona in the infrared wavelengths. However, it would have been presumptuous to use this big aircraft only for a single experiment. So the first thing we did, after we got a feeling that the project would go ahead, was to propose to other scientific groups in France, the UK, and the United States a seat on this flight to carry their own observations,” he said.

Building a team

While Léna was from the Paris Observatory, the other scientists came from the French National Centre for Scientific Research; Queen Mary College in London; Kitt Peak Observatory in Arizona; Los Alamos National Laboratory; and the University of Aberdeen.

The main goal of this project was to use the long totality to study the solar corona at different wavelengths, as the whole corona is only visible during a total eclipse. According to Léna, the experiments included studying the dust in the corona (which was emitting infrared radiation), studying the oscillating hot gas in the corona, and studying the chromosphere.

To accommodate these experiments, the Concorde 001 prototype underwent significant modifications, notably the creation of four portholes in the ceiling, with one dedicated window for each experiment. Those portholes are unmissable even today. They can be seen while walking down the aisle of the aircraft.

With the experiments and the team of scientists chosen, two rehearsal flights were conducted before the actual flight, which took off from Las Palmas in the Canary Islands for a rendezvous with the eclipse shadow over Mauritania.

“It had to be extremely precise. There were stringent conditions for the pilot and the crew. It [the rendezvous point] had to be within 1 nautical mile and within 15 seconds of time. They made it within one second. It was remarkable at that time, as it was done using only inertial navigation, as there was no GPS.”

In the eclipse

Léna distinctly remembers the experience of entering the shadow of the eclipse.

“During the eclipse, we were busy with our instruments, but a couple of minutes before the end, when we had done our measurements, I had the time to look through the window,” Léna said. “The sky was completely dark, and the curvature of the horizon noticeable. On the ground was a dark circle of 300 km radius in the Sahara Desert and on the horizon we could see the light because it was outside the eclipse. This was absolutely unique. Nobody had seen this before.”

With the shadow moving east at a slightly higher speed than the Concorde's Mach 2.2, the scientists had 80 minutes of observation time in the totality before the aircraft fell behind the eclipse’s shadow. However, due to headwinds, that was reduced to 74 minutes, which was and still continues to be an astonishing achievement. “No one has achieved even half of it during the past 53 years,” Léna said.

Following this remarkable eclipse chase, the Concorde 001 landed in the Chadian capital of N'Djamena (then Fort-Lamy). “After we landed, a partial solar eclipse went on for an hour and a half or so. There were people (celebrating) on the streets with music as they were very happy to see us. It was a very special moment in my life,” Léna said.

Read full article

Comments

In October 2025, a storm brewed over the Caribbean Sea. Weather models differed on its trajectory. Would it remain weak and end up in Haiti, or would it intensify and head to Jamaica? Artificial intelligence model WeatherNext, developed by Google’s DeepMind and Google Research, went with the latter. Five days before landfall, it predicted with 80 percent confidence that the storm system would hit Jamaica as a Category 5 hurricane.

Hurricane Melissa was catastrophic, causing flooding and landslides across Jamaica. But the AI model helped forecasters give an earlier warning to communities in its path, so they could better prepare.

In a paper published on Thursday in Nature, researchers show that the WeatherNext AI model can predict cyclones with unprecedented accuracy. On average, it gives forecasters a day more lead time than existing models; this means its predictions three days out are as accurate as previous models’ predictions two days out. On the ground, that extra day can mean a lot.

“Even a few hours can make a difference,” says Mike Brennan, director of the US National Hurricane Center. Organizing evacuations, staging supplies, and moving resources to respond to a hurricane risk are all time-sensitive tasks—and making the wrong decision can have big consequences. “Time is really golden when it comes to those types of decisions, so the ability to push forecast accuracy out as much as a day beyond what we've previously been able to do is really valuable,” he says.

Historically, bringing forecasts forward by a day would take a decade of work, the researchers say.

Modeling extreme events can be challenging for AI. Machine learning requires ample training data in order to make future predictions, but extreme events are by nature rare occurrences. “We don’t have that much cyclone data, but we have a lot of weather data,” says Ferran Alet, a research scientist at Google DeepMind and one of the paper’s lead authors. “So what we did was train a model to be both good at weather as well as cyclones.”

Hurricanes are particularly difficult to predict because they operate at multiple spatial scales, says Kate Musgrave, tropical cyclone group lead at the Cooperative Institute for Research in the Atmosphere, and an author on the paper. Predicting a storm’s track—which direction it’s traveling—requires data about weather on a global scale, taking in information such as the location of cold fronts and prevailing winds. Predicting a storm’s intensity, however, requires much smaller-scale data focused specifically on the local atmospheric and ocean conditions.

“That’s something we just don’t get from these global models,” Musgrave says. While earlier AI models have done well at predicting a storm’s track, “intensity they could not do well at all.”

It’s critical to predict both: A change in intensity can mean the difference between a relatively weak storm and a major hurricane. Sometimes—as in the case of Hurricane Melissa—a storm system can intensify rapidly, developing into an emergency situation overnight. Melissa marked the first time the National Hurricane Centre was able to predict a Category 5 hurricane when the storm was only at a Category 1 stage.

Before the WeatherNext model was used in live forecasts, researchers tested it on retrospective data. “The results were so good that we were skeptical that we would actually see that in the real-time demonstration,” Musgrave says. But when forecasters started adopting the model into their operations, this performance held true. “I think everybody was surprised at just how well it did,” Musgrave says.

Even the DeepMind researchers working on the model don’t fully understand how the AI model produces such accurate predictions, given that it uses much lower-resolution atmospheric data than traditional models require to forecast storm intensity. “When we told the community that our model was only using relatively coarse resolution, they were shocked, because that means that the lower-resolution inputs capture more signal about what’s going to happen than previously believed,” Alet says.

The AI model must be picking up on something in the lower-resolution data that allows it to make predictions about storm intensity, but the researchers don’t know what. “It’s a black box at the end of the day, but that gives physicists a signal that something is happening that was not previously understood,” Alet says.

The model doesn’t just spit out one prediction; it produces a range of potential scenarios for a developing storm. This helps to capture any potential “butterfly effect,” says Alet, where a small deviation from a trend could lead to much bigger changes down the line. Forecasters can use these outputs, alongside those of other models, to inform their predictions about how a storm system will likely unfold. Last year, the AI model created 50 scenarios per storm; now, it generates 1,000.

“That’s something that, with our computing power, we simply can’t do with our existing numerical models,” Musgrave says.

Brennan says DeepMind’s model is a great new tool in forecasters’ toolbox but emphasizes that it’s one of many. “There’s no guarantee that one model, because it did well last year or really did well for this particular storm, is necessarily going to be the best model for the next season or the next storm,” he says. The human element, he adds, is still critical. “A hurricane is not just a track or an intensity forecast,” he says. “It requires experts to translate that into what the impacts are going to be—and it's the impacts that kill people.”

Google DeepMind also announced that it is open-sourcing the WeatherNext models used during hurricane season so that researchers can use and improve on them. Alet is hopeful that opening the models up to the research community could help uncover fresh insights into how cyclones work.

“I’m very excited about scientific discovery,” he says. “I think AI is giving us new tools to poke into the laws of the universe.”

This story originally appeared on wired.com.

Read full article

Comments