Timely and accurate forecasts of severe weather events are essential for early warning and for constraining downstream analysis and decision-making. Since severe weather events prediction still depends on subjective, time-consuming expert interpretation, end-to-end "AI weather station" systems are emerging but face three major challenges: (1) scarcity of severe weather event samples; (2) imperfect alignment between high-dimensional meteorological data and textual warnings; (3) current multimodal language models cannot effectively process high-dimensional meteorological inputs or capture their complex spatiotemporal dependencies. T o address these challenges, we introduce MP-Bench, the first large-scale multimodal dataset for severe weather events prediction, comprising 421,363 pairs of raw multi-year meteorological data and corresponding text caption, covering a wide range of severe weather scenarios. On top of this dataset, we develop a Meteorology Multimodal Large Model (MMLM) that directly ingests 4D meteorological inputs. In addition, it is designed to accommodate the unique characteristics of 4D meteorological data flow, incorporating three plug-and-play adaptive fusion modules that enable dynamic feature extraction and integration across temporal sequences, vertical pressure layers, and spatial dimensions. Extensive experiments on MP-Bench show that MMLM achieves strong performance across multiple tasks, demonstrating effective severe weather understanding and representing a key step toward automated, AI-driven severe weather events forecasting systems. Our source code and dataset will be made publicly available.

A machine learning-based weather prediction program developed by DeepMind researchers called "GraphCast" can predict weather variables over the span of 10 days, in under one minute. In a report, scientists highlight that GraphCast has outperformed traditional weather pattern prediction technologies at a 90% verification rate. The AI-powered weather prediction program works by taking in "the two most recent states of Earth's weather," which includes the variables from the time of the test and six hours prior. Using that data, GraphCast can predict what the state of the weather will be in six hours. In practice, AI has already showcased its applicability in the real world.

Weather prediction has come a long way since the days of relying on folklore and superstition. However, there is still plenty of room for improvement in weather forecasting in terms of accuracy, timeliness, and reliability. One of the main ways that weather forecasting is becoming better is through the use of application programming interfaces (APIs). Today, we have a plethora of APIs that allow weather data to be shared quickly and easily between different technologies, making it possible to create more accurate and advanced weather models. One question that's fairly obvious to emerge here- why do APIs matter for weather forecasting?

Artificial intelligence holds vast potential to help solve a number of challenging problems in Earth system science, from improving prediction of severe weather events to increasing the efficiency of climate models. But as in all AI applications, the use of machine learning and other techniques in environmental science has the potential to introduce biases that could deepen inequities. The authors of a new paper published in the journal Environmental Data Science argue that researchers must develop ethical, responsible, and trustworthy approaches to applying AI in Earth system science to ensure that unintentional consequences do not worsen environmental and climate injustice. "It's really exciting to see all the ways researchers are finding to creatively apply artificial intelligence in weather, climate, and other environmental science research," said David John Gagne, a scientist at the National Center for Atmospheric Research (NCAR) and a paper co-author. "But we have a responsibility to ensure that we don't cause more harm than good."

We all know severe weather impacts demand for quick service retail chains. Those working in the industry know it impacts for longer than simply the day or two of the event itself. But building a model to accurately identify these demand impact patterns was impossible for QSR chains given the limited frequency and distribution of severe weather event data. Here's how my team tackled it and successfully pinpointed the multi-day impact of 73 different kinds of severe weather events. You need information -- you need numbers. So my team needed to find a way to turn the costly chaos of severe weather impact into accurate impact figures as soon as a watch or warning was issued.

It's the time of year when the MIT Technology Review releases its biggest breakthrough technologies for the year. These are technologies that are expected to have widespread consequences for human life in the coming year. The impetus behind satellite mega-constellations is the goal to provide every corner of the planet with high-speed internet. Satellite mega-constellations are the solution to banish spotty Wi-Fi signals and cellular connections. While enabling global connectivity for nearly anyone on the planet, these satellite mega-constellations will also litter space and dramatically increase the number of satellites in orbit very quickly.

It's the time of year when the MIT Technology Review releases its biggest breakthrough technologies for the year. These are technologies that are expected to have widespread consequences for human life in the coming year. The impetus behind satellite mega-constellations is the goal to provide every corner of the planet with high-speed internet. Satellite mega-constellations are the solution to banish spotty Wi-Fi signals and cellular connections. While enabling global connectivity for nearly anyone on the planet, these satellite mega-constellations will also litter space and dramatically increase the number of satellites in orbit very quickly.

However, with increasingly expanding weather data sets and looming deadlines, it is nearly impossible for them to monitor all storm formations -- especially smaller-scale ones -- in real time. Now, there is a computer model that can help forecasters recognize potential severe storms more quickly and accurately, thanks to a team of researchers at Penn State, AccuWeather, Inc., and the University of Almería in Spain. They have developed a framework based on machine learning linear classifiers -- a kind of artificial intelligence -- that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed. This AI solution ran on the Bridges supercomputer at the Pittsburgh Supercomputing Center. In their study, the researchers analyzed more than 50,000 historical U.S. weather satellite images.

Now, there is a computer model that can help forecasters recognize potential severe storms more quickly and accurately, thanks to a team of researchers at Penn State, AccuWeather, Inc., and the University of Almería in Spain. They have developed a framework based on machine learning linear classifiers -- a kind of artificial intelligence -- that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed. This AI solution ran on the Bridges supercomputer at the Pittsburgh Supercomputing Center. Steve Wistar, senior forensic meteorologist at AccuWeather, said that having this tool to point his eye toward potentially threatening formations could help him to make a better forecast. "The very best forecasting incorporates as much data as possible," he said.