# Weather Published articles for Weather. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Generating scenarios for extreme events, without extreme data DevFeed: [Generating scenarios for extreme events, without extreme data]() Original publisher: [Read original article]() Author: Jennifer Chu | MIT News Published: 2026-08-24T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Machine learning](), [Algorithms](), [Critical Infrastructure](), [data]() Tags: [algorithm](), [algorithms](), [artificial-intelligence](), [center-for-computational-science-and-engineering](), [climate](), [climate-risk-assessment](), [computer-modeling](), [critical-infrastructure](), [data](), [extreme-event-aware](), [extreme-weather](), [fire](), [heat](), [idss](), [kai-chang](), [learning-fefb62e9fa83](), [machine-learning](), [mechanical-engineering](), [mit-meche](), [mit-schwarzman-college-of-computing](), [natural-disasters](), [research](), [risk](), [school-of-engineering](), [storm](), [sustainability](), [themis-sapsis](), [weather](), [weather-prediction]() ### AI overview MIT engineers developed a machine-learning algorithm that generates plausible future extreme-event scenarios without requiring past extreme events in the training data. It learns from available records, filters out implausible weather scenarios, and estimates events' frequency, size, intensity, duration, and area of impact to help planners prepare. ### Source excerpt A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for. ## Generative AI to quantify uncertainty in weather forecasting DevFeed: [Generative AI to quantify uncertainty in weather forecasting]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-29T18:03:00Z Content type: release Language: en Sources: [Google Research]() Topics: [Generative AI](), [Artificial Intelligence](), [Google](), [Simulation]() Tags: [ai](), [climate](), [diffusion](), [generative-ai](), [machine-learning](), [research](), [science](), [weather]() ### AI overview Google Research introduces SEEDS, a generative AI model designed to efficiently generate large ensembles of weather forecasts for quantifying uncertainty. The article explains that SEEDS aims to reduce the computational cost of traditional physics-based ensemble forecasting and describes its application to weather and climate science. ### Source excerpt Posted by Lizao (Larry) Li, Software Engineer, and Rob Carver, Research Scientist, Google Research Accurate weather forecasts can have a direct impact on people's lives, from helping make routine decisions, like what to pack for a day's activities, to informing urgent actions, for example, protecting people in the face of hazardous weather conditions. The importance of accurate and timely weather forecasts will only increase as the climate changes. Recognizing this, we at Google have been investing in weather and climate research to help ensure that the forecasting technology of tomorrow can meet the demand for reliable weather information. Some of our recent innovations include MetNet-3, Google's high-resolution forecasts up to 24-hours into the future, and GraphCast, a weather model that can predict weather up to 10 days ahead. Weather is inherently stochastic. To quantify the uncertainty, traditional methods rely on physics-based simulation to generate an ensemble of forecasts. However, it is computationally costly to generate a large ensemble so that rare and extreme weather events can be discerned and characterized accurately. With that in mind, we are excited to announce our latest innovation designed to accelerate progress in weather forecasting, Scalable Ensemble Envelope Diffusion Sampler (SEEDS), recently published in Science Advances. SEEDS is a generative AI model that can efficiently generate ensembles of weather forecasts at scale at a small fraction of the cost of traditional physics-based forecasting models. This technology opens up novel opportunities for weather and climate science, and it represents one of the first applications to weather and climate forecasting of probabilistic diffusion models, a generative AI technology behind recent advances in media generation. The need for probabilistic forecasts: the butterfly effect In December 1972, at the American Association for the Advancement of Science meeting in Washington, D.C., MIT meteorology pr ## Using AI to expand global access to reliable flood forecasts DevFeed: [Using AI to expand global access to reliable flood forecasts]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-20T16:06:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Artificial Intelligence](), [Google](), [real-time](), [data](), [notifications]() Tags: [ai](), [data](), [environment](), [google](), [machine-learning](), [notifications](), [real-time](), [research](), [weather]() ### AI overview Google Research describes how AI and machine learning improved global flood forecasting in regions with scarce flood-related data. The work extended the average reliability of global nowcasts from zero to five days and supports real-time river forecasts up to seven days ahead across more than 80 countries. ### Source excerpt Posted by Yossi Matias, VP Engineering & Research, and Grey Nearing, Research Scientist, Google Research Floods are the most common natural disaster, and are responsible for roughly $50 billion in annual financial damages worldwide. The rate of flood-related disasters has more than doubled since the year 2000 partly due to climate change. Nearly 1.5 billion people, making up 19% of the world's population, are exposed to substantial risks from severe flood events. Upgrading early warning systems to make accurate and timely information accessible to these populations can save thousands of lives per year. Driven by the potential impact of reliable flood forecasting on people's lives globally, we started our flood forecasting effort in 2017. Through this multi-year journey, we advanced research over the years hand-in-hand with building a real-time operational flood forecasting system that provides alerts on Google Search, Maps, Android notifications and through the Flood Hub. However, in order to scale globally, especially in places where accurate local data is not available, more research advances were required. In "Global prediction of extreme floods in ungauged watersheds", published in Nature, we demonstrate how machine learning (ML) technologies can significantly improve global-scale flood forecasting relative to the current state-of-the-art for countries where flood-related data is scarce. With these AI-based technologies we extended the reliability of currently-available global nowcasts, on average, from zero to five days, and improved forecasts across regions in Africa and Asia to be similar to what are currently available in Europe. The evaluation of the models was conducted in collaboration with the European Center for Medium Range Weather Forecasting (ECMWF). These technologies also enable Flood Hub to provide real-time river forecasts up to seven days in advance, covering river reaches across over 80 countries. This information can be used by people, communi ## Recommended Reads on IT Security, Cybersecurity, and Organizational Resilience DevFeed: [Recommended Reads on IT Security, Cybersecurity, and Organizational Resilience]() Original publisher: [Read original article]() Author: Adam Published: 2017-08-14T00:00:00Z Content type: article Language: en Sources: [Shostack & Friends Blog]() Topics: [IT\_Security](), [Cybersecurity](), [Business Security](), [risk-management]() Tags: [culture](), [it-security](), [management](), [risk](), [risk-management](), [security](), [weather]() ### AI overview A curated selection of long, thought-provoking reads covering IT security, cybersecurity risk management for the U.S. government, and company culture for handling failure. ### Source excerpt Each of these is long and thought-provoking and worth savoring. ## Why I prefer Android to iOS DevFeed: [Why I prefer Android to iOS]() Original publisher: [Read original article]() Author: Graham King Published: 2012-12-31T17:53:58Z Content type: opinion Language: en Sources: [Graham King]() Topics: [Android](), [iOS](), [account](), [React Native]() Tags: [account](), [android](), [devices](), [email](), [ios](), [ipad](), [samsung](), [screen](), [software](), [tablet](), [terminal](), [weather]() ### AI overview The author explains a personal preference for Android over iOS, citing multi-user accounts, home-screen widgets, ad blocking, and greater access to internal device information. The comparison is based on the author's household devices, with Android devices described as newer. ### Source excerpt Our house has two iOS devices (an iPad and and iPod Touch), and two Android devices (Nexus 7 tablet, Samsung Galaxy phone). The Androids are newer than the iOS. So far, I prefer the Android devices for these reasons: - Android is multi-user. Just swipe down, select a different account. iOS seems designed for individuals living alone. - Widgets on the home screen. Just by glancing at my Nexus 7's screen I can check my email, my calendar, and the weather.