{"id":143679,"date":"2024-12-06T13:21:54","date_gmt":"2024-12-06T06:21:54","guid":{"rendered":"http:\/\/www.thestorythailand.com\/?p=143679"},"modified":"2025-02-11T11:42:47","modified_gmt":"2025-02-11T04:42:47","slug":"google-deepmind-gencast","status":"publish","type":"post","link":"https:\/\/www.thestorythailand.com\/en\/google-deepmind-gencast\/","title":{"rendered":"Google unveils GenCast Weather AI"},"content":{"rendered":"\n

Google DeepMind<\/a> introduced its newest AI model, GenCast, which can predict weather and the risks of extreme conditions with state-of-the-art accuracy, delivering faster and more accurate forecasts up to 15 days ahead.<\/p>\n\n\n\n

More accurate weather predictions can help officials prepare for natural disasters, which can save lives and protect homes and communities. To read more about GenCast and other weather prediction models from Google<\/p>\n\n\n\n

The evolution of AI weather models<\/h3>\n\n\n\n

GenCast marks a critical advance in AI-based weather prediction that builds on our previous weather model<\/a>, which was deterministic, and provided a single, best estimate of future weather. By contrast, a GenCast forecast comprises an ensemble of 50 or more predictions, each representing a possible weather trajectory.<\/p>\n\n\n\n

GenCast is a diffusion model, the type of generative AI model that underpins the recent, rapid advances in image<\/a>, video<\/a> and music generation<\/a>. However, GenCast differs from these, in that it\u2019s adapted to the spherical geometry of the Earth, and learns to accurately generate the complex probability distribution of future weather scenarios when given the most recent state of the weather as input.<\/p>\n\n\n\n

To train GenCast, we provided it with four decades of historical weather data from ECMWF\u2019s\u00a0ERA5 archive<\/a>. This data includes variables such as temperature, wind speed, and pressure at various altitudes. The model learned global weather patterns, at 0.25\u00b0 resolution, directly from this processed weather data.<\/p>\n\n\n\n

Setting a new standard for weather forecasting<\/h3>\n\n\n\n

To rigorously evaluate GenCast’s performance, we trained it on historical weather data up to 2018, and tested it on data from 2019. GenCast showed better forecasting skill than ECMWF\u2019s ENS, the top operational ensemble forecasting system that many national and local decisions depend upon every day.<\/p>\n\n\n\n

We comprehensively tested both systems, looking at forecasts of different variables at different lead times \u2014 1320 combinations in total. GenCast was more accurate than ENS on 97.2% of these targets, and on 99.8% at lead times greater than 36 hours.<\/p>\n\n\n\n

Today, in a paper published in Nature<\/a>, we present GenCast, our new high resolution (0.25\u00b0) AI ensemble model. GenCast provides better forecasts of both day-to-day weather and extreme events than the top operational system, the European Centre for Medium-Range Weather Forecasts\u2019 (ECMWF<\/a>) ENS, up to 15 days in advance. We\u2019ll be releasing our model\u2019s code, weights, and forecasts, to support the wider weather forecasting community.<\/p>\n\n\n\n

An ensemble forecast expresses uncertainty by making multiple predictions that represent different possible scenarios. If most predictions show a cyclone hitting the same area, uncertainty is low. But if they predict different locations, uncertainty is higher. GenCast strikes the right balance, avoiding both overstating or understating its confidence in its forecasts.<\/p>\n\n\n\n

It takes a single Google Cloud TPU v5 just 8 minutes to produce one 15-day forecast in GenCast\u2019s ensemble, and every forecast in the ensemble can be generated simultaneously, in parallel. Traditional physics-based ensemble forecasts such as those produced by ENS, at 0.2\u00b0 or 0.1\u00b0 resolution, take hours on a supercomputer with tens of thousands of processors.<\/p>\n\n\n\n

Advanced forecasts for extreme weather events<\/h3>\n\n\n\n

More accurate forecasts of risks of extreme weather can help officials safeguard more lives, avert damage, and save money. When we tested GenCast\u2019s ability to predict extreme heat and cold, and high wind speeds, GenCast consistently outperformed ENS.<\/p>\n\n\n\n

Now consider tropical cyclones, also known as hurricanes and typhoons. Getting better and more advanced warnings of where they\u2019ll strike land is invaluable. GenCast delivers superior predictions of the tracks of these deadly storms.<\/p>\n\n\n\n

\"GenCast<\/figure>\n\n\n\n

Better forecasts could also play a key role in other aspects of society, such as renewable energy planning. For example, improvements in wind-power forecasting directly increase the reliability of wind-power as a source of sustainable energy, and will potentially accelerate its adoption. In a proof-of-principle experiment that analyzed predictions of the total wind power generated by groupings of wind farms all over the world, GenCast was more accurate than ENS.<\/p>\n\n\n\n

Next generation forecasting and climate understanding at Google<\/h3>\n\n\n\n

GenCast is part of Google\u2019s growing suite of next-generation AI-based weather models, including Google DeepMind\u2019s AI-based deterministic medium-range forecasts<\/a>, and Google Research\u2019s NeuralGCM<\/a>, SEEDS<\/a>, and floods models<\/a>. These models are starting to power user experiences on Google Search and Maps, and improving the forecasting of precipitation<\/a>, wildfires<\/a>, flooding<\/a> and extreme heat<\/a>.<\/p>\n\n\n\n

We deeply value our partnerships with weather agencies, and will continue working with them to develop AI-based methods that enhance their forecasting. Meanwhile, traditional models remain essential for this work. For one thing, they supply the training data and initial weather conditions required by models such as GenCast. This cooperation between AI and traditional meteorology highlights the power of a combined approach to improve forecasts and better serve society.<\/p>\n\n\n\n

To foster wider collaboration and help accelerate research and development in the weather and climate community, we\u2019ve made GenCast an open model and released its code<\/a> and weights<\/a>, as we did for our deterministic medium-range global weather forecasting model.<\/p>\n\n\n\n

We’ll soon be releasing real-time and historical forecasts from GenCast, and previous models, which will enable anyone to integrate these weather inputs into their own models and research workflows.<\/p>\n\n\n\n

We are eager to engage with the wider weather community, including academic researchers, meteorologists, data scientists, renewable energy companies, and organizations focused on food security and disaster response. Such partnerships offer deep insights and constructive feedback, as well as invaluable opportunities for commercial and non-commercial impact, all of which are critical to our mission to apply our models to benefit humanity.<\/p>\n\n\n\n

KBTG unveils strategic plan: The Year of Agentic AI 2025<\/a><\/p>\n\n\n\n

Thailand to host the first Asia-Pacific \u201cUNESCO Global Forum on the Ethics of AI 2025<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Google DeepMind introduced its newest AI model, GenCast, which can predict weather and the risks of extreme conditions <\/p>\n","protected":false},"author":8,"featured_media":143680,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[122],"tags":[18397,30588,30589,30590],"class_list":["post-143679","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology-en","tag-ai-model","tag-google-deepmind","tag-gencast","tag-predict-weather"],"yoast_head":"\nGoogle unveils GenCast Weather AI - The Story Thailand<\/title>\n<meta name=\"description\" content=\"Google DeepMind introduced its newest AI model, GenCast, which can predict weather and the risks of extreme conditions with state-of-the-art accuracy, delivering faster and more accurate forecasts up to 15 days ahead.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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