Unusually warm ocean temperatures fueled one of the worst hurricanes on record. New research finds climate change increased the storm's likelihood. Fueled by unusually warm waters, Hurricane Melissa this week turned into one of the strongest Atlantic storms ever recorded. Now a new rapid attribution study suggests human-induced climate change made the deadly tropical cyclone four times more likely. The storm, which reached Category 5, reserved for the hurricanes with the most powerful winds, has killed at least 40 people across the Caribbean so far.

The storm, which is set to make landfall in Jamaica Tuesday, has stunned meteorologists with its intensity and the speed at which it built. Meteorologists who have spent the past few days monitoring the rapid development of Hurricane Melissa in the Atlantic Ocean are sounding the alarm about the storm, which is set to make landfall in Jamaica today as a Category 5 hurricane. The sustained--and growing--intensity of the storm is remarkable, experts say, and has the makings of a historic hurricane. "When I look at the cloud pattern, I will tell you as a meteorologist and professional--and a person--it is beautiful, but it is terrifying," says Sean Sublette, a meteorologist based in Virginia. "I know what is underneath those clouds."

Billionaire Illinois Democrat governor caught in lie live on Fox News while trying to downplay Chicago's murder capital status Storm Melissa to explode into Category 5 hurricane as models reveal its'life-threatening' path to the US JAN MOIR: The Queen was blindly devoted to Prince Andrew... she raised a monster. The final hours of chess grandmaster Daniel Naroditsky - friends' desperate attempts to save him, warnings in final monologue and how he was haunted by sinister figure in hidden underworld. My wife won't get a job and I feel broken trying to provide for our family. Hold on, says DEAR CAROLINE... that's bad enough but your letter raises a MUCH bigger red flag Wild resurfaced Gilbert Arenas'snitching' claim goes viral in the wake of NBA mafia gambling scandal Inside the nondescript Virginia warehouse that wiped out the internet with one outage... and the neighbors who warn the next one is just a matter of time Fury as'insane' GM kills much-loved feature from upcoming cars as rival Ford doubles down I know all the secrets of the NBA legends' betting scandal. I think I've discovered Meghan's secret plan for if - or when - William strips away the Sussexes' royal titles: SHARON HUNT Disney fans left devastated after theme park dramatically'scales back' on its villains Doctor's $1M show of loyalty for murderer husband after he let adorable daughter, 2, die in roasting car as he watched adult videos Storm Melissa to explode into Category 5 hurricane as models reveal its'life-threatening' path to the US Tropical Storm Melissa is expected to strengthen into a life-threatening Category 5 hurricane that could swerve into the northeastern US in just days.

Peak is this summer's finest co-op game. The game, created in partnership with developers Aggro Crab and Landfall as part of a game jam, is currently in Steam's top five bestsellers. It sold over a million copies in its first week, and has now surpassed 8 million, according to Aggro Crab cofounder Nick Kamen. Now, as a result of its success, says Kamen, scammers are selling cheap, AI-made versions of it wherever they can. "We hate to see it," says Kamen.

A new method for estimating tropical cyclone track uncertainty is presented and tested. This method uses a neural network to predict a bivariate normal distribution, which serves as an estimate for track uncertainty. We train the network and make predictions on forecasts from the National Hurricane Center (NHC), which currently uses static error distributions based on forecasts from the past five years for most applications. The neural network-based method produces uncertainty estimates that are dynamic and probabilistic. Further, the neural network-based method allows for probabilistic statements about tropical cyclone trajectories, including landfall probability, which we highlight. We show that our predictions are well calibrated using multiple metrics, that our method produces better uncertainty estimates than current NHC approaches, and that our method achieves similar performance to the Global Ensemble Forecast System. Once trained, the computational cost of predictions using this method is negligible, making it a strong candidate to improve the NHC's operational estimations of tropical cyclone track uncertainty.

On Wednesday evening, Hurricane Milton will become the fifth hurricane in 2024 to make landfall in the mainland U.S. As storms like this one grow more frequent and intense, artificial intelligence is playing an increasingly central role in efforts by meteorologists and other scientists to track these storms and mitigate their harms. For years, meteorologists have built complex forecasting models of storms based on wind speeds, temperature, humidity and other factors, and recorded via readings from planes, buoys and satellites. But these models can take hours to produce updated forecasts. Machine learning models, on the other hand, draw upon vast knowledge of the earth's atmosphere and data from how previous storms have unfolded. They excel at pattern recognition, teasing out trends that most humans can't discern in a fraction of the time.

Accurate traffic prediction is vital for effective traffic management during hurricane evacuation. This paper proposes a predictive modeling system that integrates Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) models to capture both long-term congestion patterns and short-term speed patterns. Leveraging various input variables, including archived traffic data, spatial-temporal road network information, and hurricane forecast data, the framework is designed to address challenges posed by heterogeneous human behaviors, limited evacuation data, and hurricane event uncertainties. Deployed in a real-world traffic prediction system in Louisiana, the model achieved an 82% accuracy in predicting long-term congestion states over a 6-hour period during a 7-day hurricane-impacted duration. The short-term speed prediction model exhibited Mean Absolute Percentage Errors (MAPEs) ranging from 7% to 13% across evacuation horizons from 1 to 6 hours. Evaluation results underscore the model's potential to enhance traffic management during hurricane evacuations, and real-world deployment highlights its adaptability and scalability in diverse hurricane scenarios within extensive transportation networks.

In September, researchers at Google's DeepMind AI unit in London were paying unusual attention to the weather across the pond. Hurricane Lee was at least 10 days out from landfall--eons in forecasting terms--and official forecasts were still waffling between the storm landing on major Northeast cities or missing them entirely. DeepMind's own experimental software had made a very specific prognosis of landfall much farther north. "We were riveted to our seats," says research scientist Rémi Lam. A week and a half later, on September 16, Lee struck land right where DeepMind's software, called GraphCast, had predicted days earlier: Long Island, Nova Scotia--far from major population centers.

Combining satellite technology with machine learning may allow scientists to better track and prepare for climate-induced natural hazards, according to research presented last month at the annual meeting of the American Geophysical Union. Over the last few decades, rising global temperatures have caused many natural phenomena like hurricanes, snowstorms, floods and wildfires to grow in intensity and frequency. While humans can't prevent these disasters from occurring, the rapidly increasing number of satellites that orbit the Earth from space offers a greater opportunity to monitor their evolution, said C.K Shum, co-author of the study and a professor at the Byrd Polar Research Center and in earth sciences at The Ohio State University. He said that potentially allowing people in the area to make informed decisions could improve the effectiveness of local disaster response and management. "Predicting the future is a pretty difficult task, but by using remote sensing and machine learning, our research aims to help create a system that will be able to monitor these climate-induced hazards in a manner that enables a timely and informed disaster response," said Shum.

In one experiment, the team used these methods to determine if radar signals from Earth's Global Navigation Satellite System (GNSS), which were reflected over the ocean and received by GNSS receivers located at towns offshore in the Gulf of Mexico, could be used to track hurricane evolution by measuring rising sea levels after landfall. Between 2020 and 2021, the team studied how seven storms, such as Hurricane Hana and Hurricane Delta, affected coastal sea levels before they made landfall in the Gulf of Mexico. By monitoring these complex changes, they found a positive correlation between higher sea levels and how intense the storm surges were.