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AI is changing sea ice melting climate projections

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AI is changing sea ice melting climate projections

The tremendous melting of sea ice at the poles is one of the most urgent problems facing planet as it warms up so quickly. These delicate ecosystems, whose survival depends so heavily on floating ice, have a difficult and uncertain future.

As a result, climate scientists are using AI more and more to transform our knowledge of this vital habitat and the actions that can be taken to preserve it.

Determining the precise date at which the Arctic will become ice-free is one of the most urgent problems that must be addressed in order to develop mitigation and preservation strategies. A step toward this, according to Princeton University research scientist William Gregory, is to lower the uncertainty in climate models to produce these kinds of forecasts.

“This study was inspired by the need to improve climate model predictions of sea ice at the polar regions, as well as increase our confidence in future sea ice projections,” said Gregory.

Arctic sea ice is a major factor in the acceleration of global climate change because it cools the planet overall by reflecting solar radiation back into space. But because of climate change brought on by our reliance on gas, oil, and coal, the polar regions are warming considerably faster than the rest of the world. When the sea is too warm for ice to form, more solar radiation is absorbed by the Earth’s surface, which warms the climate even more and reduces the amount of ice that forms.

Because of this, polar sea ice is extremely important even outside of the poles. The Arctic Ocean will probably eventually have no sea ice in the summer, which will intensify global warming’s effects on the rest of the world.

AI coming to the rescue

Predictions of the atmosphere, land, sea ice, and ocean are consistently biased as a result of errors in climate models, such as missing physics and numerical approximations. Gregory and his colleagues decided to use a kind of deep learning algorithm known as a convolutional neural network for the first time in order to get around these inherent problems with sea ice models.

“We often need to approximate certain physical laws in order to save on [computational] time,” wrote the team in their study. “Therefore, we often use a process called data assimilation to combine our climate model predictions together with observations, to produce our ‘best guess’ of the climate system. The difference between best-guess-models and original predictions provides clues as to how wrong our original climate model is.”

The team aims to show a computer algorithm  “lots of examples of sea ice, atmosphere and ocean climate model predictions, and see if it can learn its own inherent sea ice errors” according to their study published in JAMES.

Gregory explained that the neural network “can predict how wrong the climate model’s sea ice conditions are, without actually needing to see any sea ice observations,” which means that once it learns the features of the observed sea ice, it can correct the model on its own.

They achieved this by using climate model-simulated variables such as sea ice velocity, salinity, and ocean temperature. In the model, each of these factors adds to the overall representation of the Earth’s climate.

“Model state variables are simply physical fields which are represented by the climate model,” explained Gregory. “For example, sea-surface temperature is a model state variable and corresponds to the temperature in the top two meters of the ocean.

“We initially selected state variables based on those which we thought a-priori are likely to have an impact on sea ice conditions within the model. We then confirmed which state variables were important by evaluating their impact on the prediction skill of the [neural network],” explained Gregory.

In this instance, the most important input variables were found to be surface temperature and sea ice concentration—much fewer than what most climate models require to replicate sea ice. In order to fix the model prediction errors, the team then trained the neural network on decades’ worth of observed sea ice maps.

An “increment” is an additional value that indicates how much the neural network was able to enhance the model simulation. It is the difference between the initial prediction made by the model without AI and the corrected model state.

A revolution in progress

Though it is still in its early stages, artificial intelligence is becoming more and more used in climate science. According to Gregory, he and his colleagues are currently investigating whether their neural network can be applied to scenarios other than sea ice.

“The results show that it is possible to use deep learning models to predict the systematic [model biases] from data assimilation increments, and […] reduce sea ice bias and improve model simulations,” said Feiyu Lu, project scientist at UCAR and NOAA/GFDL, and involved in the same project that funded this study.

“Since this is a very new area of active research, there are definitely some limitations, which also makes it exciting,” Lu added. “It will be interesting and challenging to figure out how to apply such deep learning models in the full climate models for climate predictions.”  

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SpaceX Determines the Reason Behind the Falcon 9 Malfunction and Plans to Resume Flying as Soon as July 27

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On July 11, when a Falcon 9 was launching 20 of SpaceX’s Starlink broadband satellites into low Earth orbit, the failure took place. The rocket’s first stage ran smoothly that day, but an oxygen leak in its upper stage stopped it from performing the intended orbit-raising burn. As a result, the Starlink satellites were launched too low and quickly returned to Earth, where they burned up in the planet’s dense atmosphere.

The enigma surrounding the leak’s origin is now solved. In an update on Thursday afternoon (July 25), SpaceX stated that it was caused by “a crack in a sense line for a pressure sensor attached to the vehicle’s oxygen system.” “This line cracked due to fatigue caused by high loading from engine vibration and looseness in the clamp that normally constrains the line.”

On July 11, the upper stage’s lone Merlin engine executed its first burn according to plan, precisely as it entered a coast phase in an elliptical orbit. However, according to SpaceX’s anomaly study, which was supervised by the US Federal Aviation Administration (FAA), the leak stopped the engine from performing a second burn, which was intended to circularize its orbit prior to Starlink satellite placement.

In an update published on Thursday, the business stated that the leak “led to the excessive cooling of engine components, most importantly those associated with the delivery of ignition fluid to the engine.” “As a result, the engine experienced a hard start rather than a controlled burn, which damaged the engine hardware and caused the upper stage to subsequently lose attitude control.”

All 20 satellites were successfully launched by the upper stage, but as was already said, their orbital lifetime was short.

The update stated that “the failed sense line and sensor on the second-stage engine will be removed for near-term Falcon launches,” according to company reps.

“The sensor is not used by the flight safety system and can be covered by alternate sensors already present on the engine,” they stated.”The design change has been tested at SpaceX’s rocket development facility in McGregor, Texas, with enhanced qualification analysis and oversight by the FAA and involvement from the SpaceX investigation team. An additional qualification review, inspection, and scrub of all sense lines and clamps on the active booster fleet led to a proactive replacement in select locations.”

The FAA has received SpaceX’s accident report. Thursday afternoon, the firm announced on X that it is “poised to rapidly return to flight as soon as Saturday, July 27.”

Since a Falcon 9 rocket disintegrated in June 2015 while delivering a robotic Dragon cargo capsule toward the International Space Station, SpaceX has not experienced an in-flight malfunction until the anomaly of July 11. The Dragon was lost as a result of the mishap.

Nevertheless, in September 2016, during preflight testing, a Falcon 9 exploded on the pad. The AMOS-6 communications satellite, which was part of the rocket’s payload, was also lost due to that incident.

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NASA Chandra View Reveals Cosmic Pillars of Creation Shining

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The Chandra X-ray Observatory of NASA had a magnificent 25th anniversary celebration. On Tuesday, the space agency unveiled 25 never-before-seen photos taken with the space telescope. The images include the planet Jupiter as well as galaxies and nebulae throughout the cosmos. It’s difficult to choose a favorite, but a new perspective on the M16 Eagle Nebula is noteworthy. The picture includes the magnificent Pillars of Creation, a well-known celestial structure that has fascinated people on Earth for many years.

The Pillars of Creation, a star-forming zone with towering fingers of gas and dust, gained notoriety in the 1990s thanks to the Hubble Space Telescope. In 2022, the James Webb Space Telescope participated with its own version.

Chandra’s X-ray vision gives it a unique ability to reveal aspects of the universe. As per NASA, “X-rays are an especially penetrating type of light that reveals extremely hot objects and very energetic physical processes.” “Many fascinating regions in space glow strongly in X-rays, such as the debris from exploded stars and material swirling around black holes.” Data from other observatories that “see” in different ways is frequently integrated with data from Chandra. Data from Chandra combined with an infrared picture from Webb is used to create the Pillars of Creation image.

In the updated picture, Chandra amplifies the stellar power. Search for young stars that resemble confetti. These represent the pinnacle of Chandra’s input. The Chandra team used beautiful words to describe the image: “The misty glow, colorful stars, and lifelike gray dust formations combine to create an image of yearning cloud creatures at dusk, reaching for something just out of frame.”

The latest Chandra photos are all quite beautiful. Known for being the remnant of a supernova explosion, the Crab Nebula is another prominent structure. It looked like a neon purple mushroom with a web of veins and filaments surrounding it, according to the Chandra team.

Another area where stars originate is called Cat’s Paw Nebula, which was also photographed by Chandra. A group of young stars with white spots at their centers and a vivid purple appearance were observed by the observatory.

The combined Chandra and Hubble infrared image of Jupiter appears like a striped marble hovering in space. The neon-purple bands at the planet’s top and bottom were identified by the Chandra team. The scientists explained, “Capping the planet’s magnetic poles, these purple strips represent X-ray auroras, created when high-energy particles collide with gas in the planet’s atmosphere.”

On July 23, 1999, Chandra was launched with assistance from the space shuttle Columbia. Since then, the telescope has recorded about 25,000 observations. According to Pat Slane, director of the Chandra X-ray Center, “Astronomers have used Chandra to investigate mysteries that we didn’t even know about when we were building the telescope—including exoplanets and dark energy,” A NASA count indicates that more than 10,000 peer-reviewed articles have been authored by scientists using Chandra data.

The stunning new photos have a bittersweet quality. NASA revealed intentions to reduce the observatory’s funding, perhaps leading to the closure of its operations. That implies that there may not be any more anniversary parties. Even though Chandra may not be as well-known as the Hubble Space Telescope or the Webb telescope, the observatory has made significant contributions to the study of exoplanets, black holes, and distant galaxies. “Help understand the structure and evolution of the universe.” is its stated objective. It would be hard for the astronomers who utilize Chandra’s data to say goodbye.

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NASA Releases a New Study by Sunita Williams While the Boeing Starliner Remains in Orbit

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The two astronauts who were sent as part of Boeing’s Crew Flight Test were Sunita “Suni” Williams and Butch Wilmore.

For more than a month now, Boeing’s Starliner has been trapped in orbit. The spacecraft was originally scheduled for a 10-day mission when it launched on June 5. But when it docked at the International Space Station the following day, it ran into unanticipated problems with its thrusters. The astronauts are conducting research while in orbit as they continue to work on a solution.

NASA releases research on space plants by Sunita Williams

As part of Boeing’s Crew Flight Test, two astronauts were deployed: Sunita “Suni” Williams and Butch Wilmore. According to a NASA statement, the two, with plenty of free time on their hands, conducted research on space plants and free-flying robots on Wednesday. According to the US space agency, Wilmore and Williams are “exploring ways to effectively water plants in the weightless environment.”

The announcement also stated that “The duo took turns throughout the day in the Harmony module, testing how root models and plants of various sizes would absorb water in microgravity . The Plant Water Management study looks at techniques such as hydroponics and air circulation to nourish plants growing aboard spacecraft and space habitats.”

NASA noted in an earlier statement that the two astronauts’ primary focus was testing various techniques for watering plants grown in the weightless microgravity environment without soil. According to the statement, “Williams first set up the Plant Water Management hardware in the Harmony module then tested a variety of liquid flow methods while video recording the results,”

It continues, “Following her work, Wilmore ran more tests using hydroponics and air circulation techniques to learn how to effectively nourish a variety of plants on spacecraft and space habitats.” In the meantime, the agency stated that the two “started their day servicing a variety of research hardware” in the release on Thursday.

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