Is turbulence really like Jello-O? Science backs up the goofy analogy. The viral TikTok video may actually hold up under scrutiny. Breakthroughs, discoveries, and DIY tips sent six days a week. A young woman pushes a balled-up piece of napkin into a cup of Jell-O, asking the viewer to imagine that it is an airplane, high in the air.

How pilots avoid thunderstorms--and what happens when they can't Most commercial planes get struck by lightning a couple times a year. Despite the fears of nervous fliers, radar, routing, and teamwork keep planes safe during storms. Breakthroughs, discoveries, and DIY tips sent every weekday. In the 2023 movie starring Gerard Butler, a commercial aircraft is caught in a terrible storm. The plane shakes and the lights go out.

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Large language models (LLMs) are typically pretrained with next-word prediction (NWP), which yields strong surface fluency but places limited pressure on models to form explicit reasoning before emitting tokens. We study whether shifting the supervision signal can better elicit explicit reasoning and, more broadly, strengthen models' general reasoning capability. We present BOttlenecked next-Word prediction (BOW), a RL formulation of NWP that inserts an intermediate reasoning bottleneck. Instead of predicting the next word directly from context, the policy model must first generate a next-word reasoning trajectory. A frozen scorer then assigns this trajectory a soft, distributional reward equal to the probability of the gold next token conditioned solely on the trajectory to guide the RL optimization. We also propose an optional L1-style regularizer on the reward to discourage "name-the-answer" shortcuts. Across ten benchmarks, a brief BOW adaptation phase on Qwen2.5-7B-Instruct and Llama3.1-8B-Instruct improves zero-shot reasoning and outperforms strong continual-pretraining baselines, including an RL variant with a hard, binary reward and a supervised finetuning approach with augmented data, by nearly 5% on average, while achieving the top result in 7 of 10 intrinsic NWP evaluations. These results indicate that BOW is a viable alternative to vanilla NWP, inducing explicit next-word reasoning and strengthening general reasoning ability.

Breakthroughs, discoveries, and DIY tips sent every weekday. In October 2017, a single flash of lightning during a thunderstorm streaked across the Great Plains for 515 miles. The flash traveled from eastern Texas all the way to Kansas City--and now into the record books. The World Meteorological Organization (WMO) certified that this megaflash is now the longest single lightning flash in the United States. The massive lightning bolt is detailed in a study published July 31 in the Bulletin of the American Meteorological Society.

Breakthroughs, discoveries, and DIY tips sent every weekday. A US astronaut aboard the International Space Station (ISS) recently caught a glimpse of one of Earth's least understood atmospheric phenomena. While orbiting in the early hours of July 3, Nichole "Vapor" Ayers snapped a photo of a transient luminous event, as she passed over North America. Better known as a sprite, these atmospheric events are common after a lightning strike. Wow," Ayers posted to social media later that day along with the stunning picture.

But severe turbulence is set to get even worse - with climate change to blame. That's according to Professor Lance M Leslie and Milton Speer from the University of Technology Sydney, who have discovered a link between'freak wind gusts' and global warming. Using machine learning techniques, the pair found that heat and moisture are'key ingredients' for dangerous wind gusts known as'downbursts.' Downbursts can wreak havoc during takeoff and landing, causing planes to dangerously gain or lose altitude. Based on their findings, the scientists are calling for air safety authorities and airlines to be'more vigilant during takeoff and landing in a warming world.' 'Our research is among the first to detail the heightened climate risk to airlines from thunderstorm microbursts, especially during takeoff and landing,' they explained in an article for The Conversation.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Commuters wait for a train against dark skies at the MTA's Expo/Bundy station in Culver City on Thursday. An unseasonable shift in weather is bringing the chance of showers and thunderstorms across Southern California, prompting some concerns about flooding as temperatures also drop well below average for mid-September. In much of the Los Angeles area, the system is expected to bring only light rain or drizzling Thursday and Friday, but there is a possibility for pockets of thunderstorms that could bring heavier rain. The greatest chance for thunderstorms is in the mountains, including along the Interstate 5 corridor and across the San Gabriels, according to Bryan Lewis, a National Weather Service meteorologist in Oxnard. "We're looking at mostly less than a tenth of an inch, maybe up to a quarter of an inch in the mountains," Lewis said.

Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires are becoming increasingly frequent. Although they occur less frequently globally than those sparked by human activities, lightning-ignited wildfires play a substantial role in carbon emissions and account for the majority of burned areas in certain regions. While existing computational models, especially those based on machine learning, aim to predict lightning-ignited wildfires, they are typically tailored to specific regions with unique characteristics, limiting their global applicability. In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. Our approach involves classifying lightning-ignited versus anthropogenic wildfires, and estimating with high accuracy the probability of lightning to ignite a fire based on a wide spectrum of factors such as meteorological conditions and vegetation. Utilizing these models, we analyze seasonal and spatial trends in lightning-ignited wildfires shedding light on the impact of climate change on this phenomenon. We analyze the influence of various features on the models using eXplainable Artificial Intelligence (XAI) frameworks. Our findings highlight significant global differences between anthropogenic and lightning-ignited wildfires. Moreover, we demonstrate that, even over a short time span of less than a decade, climate changes have steadily increased the global risk of lightning-ignited wildfires. This distinction underscores the imperative need for dedicated predictive models and fire weather indices tailored specifically to each type of wildfire.

With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. In order to meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.