# Climate Published articles for Climate. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## 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. ## An Opinionated Critique of AI's Environmental, Social, and Infrastructure Impacts DevFeed: [An Opinionated Critique of AI's Environmental, Social, and Infrastructure Impacts]() Original publisher: [Read original article]() Published: 2026-05-05T15:09:12Z Content type: opinion Language: en Sources: [Bert Hubert's writings]() Topics: [Artificial Intelligence](), [Code](), [large-language-models](), [data centers](), [data]() Tags: [ai](), [ai-data-centers](), [climate](), [co2](), [code](), [data-centers](), [education](), [energy](), [hiring](), [language-models](), [large-language-models](), [water]() ### AI overview This opinion article argues that society is being asked to accept damaging conditions as normal, focusing on climate change, AI data centers' resource use, geopolitical crises, and dependence on US cloud infrastructure. It is especially critical of AI's effects on hiring and education, and of deploying unproven large language model output in production services. ### Source excerpt "Alice laughed. 'There's no use trying,' she said. 'One can't believe impossible things.' I daresay you haven't had much practice,' said the Queen. 'When I was your age, I always did it for half-an-hour a day. Why, sometimes I've believed as many as six impossible things before breakfast." - Through the looking-glass, Lewis Carrol by John Tenniel To stay sane, we have to accept that our climate is going completely haywire, but that it is ok to mostly ignore that since saving ourselves is apparently not cost-effective. ## 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 ## What are we going to do: CO2 edition DevFeed: [What are we going to do: CO2 edition]() Original publisher: [Read original article]() Author: Adam Published: 2021-10-05T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [risk-management](), [Cybersecurity](), [Microsoft]() Tags: [analysis](), [bypass](), [carbon](), [climate](), [cost](), [cybersecurity](), [ease](), [emissions](), [microsoft](), [risk-management](), [usability]() ### AI overview The article uses Microsoft's evaluation of carbon-removal proposals to discuss how explicit criteria can improve mitigation and risk-management decisions. It applies this idea to cybersecurity threat modeling, including cost, bypass resistance, usability, and unusual circumstances. ### Source excerpt What happened when Microsoft tried to buy climate abatements