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The Position of the Moon Slightly Affects the Rainfall on Earth

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We like to give the moon credit for all kinds of weird things, from werewolves and hockey fights to the ocean’s shifting tides (though only the latter is true). And now the Moon can add a new accomplishment to its resume: messing with our rainfall. A paper published last week in Geophysical Research Letters shows that the position of the Moon slightly affects the amount of rain we get here on Earth.

The Sun, the Moon, and the Earth are all constantly in motion, both orbiting and revolving. Each of these bodies has its own gravitational field, and we are affected by all of them. The Sun’s gravity is what keeps us on track in our path around the solar system. But the Moon, being so very tiny by comparison, can’t do anything quite that big. Instead, when the Moon is high in the sky, its gravitational field just kind of tugs on our planet. This tug is not enough to move Earth out of its path, but it is enough to give us what amounts to a little squeeze. The part of the planet directly under the Moon bulges a little bit, swelling upward. When this happens over the ocean, we call it high tide.

The same gravitational pull is responsible for changes in Earth’s rainfall, says study co-author Tsubasa Kohyama. While researching changes in atmospheric pressure, Kohyama noticed a strange, consistent pattern.

Since the 1800s, scientists have been suggesting that the Moon’s place in the sky can impact air pressure on Earth. After analyzing 15 years of rainfall data collected by NASA and the Japan Aerospace Exploration Agency's Tropical Rainfall Measuring Mission satellite, Kohyama and his co-author John Wallace were able to confirm that those air pressure changes translate to changes in rainfall.

“When the Moon is overhead or underfoot, the air pressure is higher," Kohyama said in a press statement. It goes back to that bulge. The wedge of Earth underneath the Moon at any given moment also includes the atmosphere above it, and under high gravitational pressure, that atmosphere swells, too. High pressure raises the temperature of the swelling pocket of air, which then retains more moisture. But the same air parcels are now farther from their moisture capacity, the researchers said.

"It's like the container becomes larger at higher pressure," Kohyama said. The relative humidity affects rain, he said, because "lower humidity is less favorable for precipitation."

As a result, when the Moon is high, the rain is slightly lighter. When the moon is on the horizon, or rising, there's a little more rain. But the change is so small that you'll never notice it; the researchers estimated the lunar influence at about 1 percent. "No one should carry an umbrella just because the Moon is rising," Kohyama said. While these findings may not be of much use for the daily forecast, the authors hope their findings can help scientists fine-tune their climate models.

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Google's AI Can Make Its Own AI Now
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Artificial intelligence is advanced enough to do some pretty complicated things: read lips, mimic sounds, analyze photographs of food, and even design beer. Unfortunately, even people who have plenty of coding knowledge might not know how to create the kind of algorithm that can perform these tasks. Google wants to bring the ability to harness artificial intelligence to more people, though, and according to WIRED, it's doing that by teaching machine-learning software to make more machine-learning software.

The project is called AutoML, and it's designed to come up with better machine-learning software than humans can. As algorithms become more important in scientific research, healthcare, and other fields outside the direct scope of robotics and math, the number of people who could benefit from using AI has outstripped the number of people who actually know how to set up a useful machine-learning program. Though computers can do a lot, according to Google, human experts are still needed to do things like preprocess the data, set parameters, and analyze the results. These are tasks that even developers may not have experience in.

The idea behind AutoML is that people who aren't hyper-specialists in the machine-learning field will be able to use AutoML to create their own machine-learning algorithms, without having to do as much legwork. It can also limit the amount of menial labor developers have to do, since the software can do the work of training the resulting neural networks, which often involves a lot of trial and error, as WIRED writes.

Aside from giving robots the ability to turn around and make new robots—somewhere, a novelist is plotting out a dystopian sci-fi story around that idea—it could make machine learning more accessible for people who don't work at Google, too. Companies and academic researchers are already trying to deploy AI to calculate calories based on food photos, find the best way to teach kids, and identify health risks in medical patients. Making it easier to create sophisticated machine-learning programs could lead to even more uses.

[h/t WIRED]

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European Space Agency Releases First High-Res Land Cover Map of Africa
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Land Cover CCI, ESA

This isn’t just any image of Africa. It represents the first of its kind: a high-resolution map of the different types of land cover that are found on the continent, released by The European Space Agency, as Travel + Leisure reports.

Land cover maps depict the different physical materials that cover the Earth, whether that material is vegetation, wetlands, concrete, or sand. They can be used to track the growth of cities, assess flooding, keep tabs on environmental issues like deforestation or desertification, and more.

The newly released land cover map of Africa shows the continent at an extremely detailed resolution. Each pixel represents just 65.6 feet (20 meters) on the ground. It’s designed to help researchers model the extent of climate change across Africa, study biodiversity and natural resources, and see how land use is changing, among other applications.

Developed as part of the Climate Change Initiative (CCI) Land Cover project, the space agency gathered a full year’s worth of data from its Sentinel-2A satellite to create the map. In total, the image is made from 90 terabytes of data—180,000 images—taken between December 2015 and December 2016.

The map is so large and detailed that the space agency created its own online viewer for it. You can dive further into the image here.

And keep watch: A better map might be close at hand. In March, the ESA launched the Sentinal-2B satellite, which it says will make a global map at a 32.8 feet-per-pixel (10 meters) resolution possible.

[h/t Travel + Leisure]

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