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7 Advanced Facts About the GOES-R Weather Satellite Launching Today

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At Cape Canaveral, a crane lifts the GOES-R satellite to join it with the Atlas V Centaur rocket that will take it up into orbit. Image Credit: NASA/Ben Smegelsky via Flickr

The future of weather forecasting weighs more than 6000 pounds and is patiently spending its final days on Earth overlooking the glistening Florida coast. NASA will soon launch the latest addition to its arsenal of tools designed to help meteorologists track and predict the future movements of our fluid atmosphere. The GOES-R weather satellite will provide scientists around the world with a trove of data to monitor the latest movements of storms both near and far.


Barring any last-minute issues, GOES-R is scheduled to begin its journey on November 19 just after sunset from Cape Canaveral. GOES-R should have already been in space by now, but like many space projects before it, the new satellite’s launch has suffered several minor delays in the months leading up to launch.

The original launch date was November 4, but in a fitting sendoff for the country’s most advanced weather satellite to date, Hurricane Matthew’s terrifying brush with Florida pushed the launch back by a couple of weeks to November 16 due to safety checks. The launch was further delayed by a couple of days while crews worked out some issues with the booster rockets that will help GOES-R reach orbit.


The name “GOES” stands for Geostationary Operational Environmental Satellite, a mouthful that alludes to the very orbit that makes these satellites so useful. Unlike many spacecraft that actively circle the Earth every hour or two, weather monitoring satellites like the GOES series are parked in a geosynchronous, or geostationary, orbit. Satellites that follow a geosynchronous orbit exactly match the speed at which the planet rotates, allowing the satellite to remain over one fixed point on the Earth’s surface. Scientists achieve this feat by sending satellites into orbit exactly 42,164 kilometers (26,199 miles) away from the center of the Earth—or about 36,000 kilometers (22,369 miles) above the surface at the equator—giving the satellite a consistent view of half the planet for its entire service life, which in this case is anticipated to be about 10 years.


A map showing the locations and coverage area of the three GOES satellites in active service. Image credit: NOAA/NASA

We currently have three different GOES satellites that help us monitor the Western Hemisphere. The two satellites that are in active service are GOES-13 and GOES-15. The former satellite is commonly called GOES-East, while the latter is aptly known as GOES-West. Each satellite covers about half of the Western Hemisphere. GOES-East watches over most of North America, all of South America, and the Atlantic Ocean, while GOES-West primarily keeps tabs on the eastern Pacific Ocean and parts of western North America. GOES-14 serves as a backup satellite, filling in for the other two satellites if they encounter any issues.


A low-pressure system in the western Atlantic Ocean as seen by GOES-East on November 10, 2016. Image credit: NASA/NOAA

The most important feature of GOES-R will be its Advanced Baseline Imager (ABI), the device that will give us a more detailed view of the atmosphere much faster than its predecessors. The current generation of GOES satellites generate "full disk" images (meaning of the entire Earth face) every three hours and higher-resolution views every 15 minutes. In contrast, GOES-R and its successors will take full-disk images every 15 minutes and a higher-resolution image of the United States every five minutes. If there's an active storm, it'll take two images of it every 60 seconds. See it in action below.

The new satellite also has the ability to give us rapid scans of smaller areas—think on the level of a couple of states—to track events like tornado outbreaks or the eye of a hurricane. The satellite will be able to give us rapid updates for two small areas every 60 seconds or one small area every 30 seconds, which will be a tremendous help in tracking important changes in rapidly-developing weather systems.


GOES-R's primary capabilities. Image Credit: NOAA/NASA

GOES-R will also host a nifty device known as the Geostationary Lightning Mapper (GLM), making it the first satellite to track lightning flashes from geosynchronous orbit. The sensor will monitor the atmosphere for sudden flashes of light that indicate the presence of lightning, mapping this data to give us a near-real-time look at just about every thunderstorm within the satellite’s range of sight.

Among other uses, data collected by the GLM could help forecasters extend warning lead times ahead of intensifying severe thunderstorms, adding crucial minutes for people to act before dangerous wind, hail, or tornadoes strike their area. It’s also useful in helping us monitor rapid intensification of hurricanes, as increased lightning activity in the eyewall of a tropical cyclone often precedes strengthening.


The satellite will also have several sensors dedicated to monitoring activity around the Sun, some of which can have serious implications here on Earth. The Extreme ultraviolet and X-ray Irradiance Sensors (EXIS) will help us track solar flares that could disrupt communications and potentially damage satellites. Several of the sensors will also measure different types of radiation approaching the planet, which can also damage satellites and pose harm to astronauts and even passengers on airline routes that travel over the poles.


The GOES-R satellite in the payload processing facility two months before launch. Image Credit: NOAA Satellites via Flickr

It’s customary for GOES satellites to be named sequentially by letter before launch and by number after launch. Once it reaches a successful orbit and begins operation, GOES-R will become GOES-16. NOAA hasn’t decided which current satellite the new one will replace, though GOES-East is the odds-on favorite for replacement as it’s passed the end of its expected 10-year lifespan.


GOES-R represents the fifth generation of GOES satellites, a series that began with the launch of GOES-1 back in 1975. Each new group of satellites improved by leaps and bounds over the previous generation. The first three satellites had limited abilities and provided limited data compared to what we can gather today; they took little more than a picture of the Earth. Each generation after that grew more advanced with improved image resolution, improved speed, more data points, and better data quality.


The next two satellites in GOES-R’s class are scheduled to launch before the end of the decade, finally phasing out the fourth generation of satellites in use today. Barring any major issues with GOES-R, the next satellite, GOES-S, is tentatively scheduled to launch in the winter of 2018, and GOES-T will follow behind it in the fall of 2019. After that, we have to wait until the middle of the 2020s to enjoy the technological advances of the series of satellites that will replace the one launching this Saturday.  

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]