Google Launches WeatherNext 2: AI Model Delivers 8x Faster Weather Forecasts Across Consumer Apps

Google updates its weather forecasts with a new AI model

"We're taking it out of the lab and really putting it into the hands of users in more ways than we have before and sort of shedding off the experimental kind of designation because we have confidence that our forecasts are really quite effective and quite useful," Peter Battaglia, senior director of research and sustainability at Google DeepMind, said in a briefing with reporters. The new AI model, WeatherNext 2, can generate forecasts eight times faster than Google's previous model, and is also more accurate in predicting 99.9 percent of variables like temperature or wind. WeatherNext 2 can pump out hundreds of potential outcomes from a particular starting point. It takes less than a minute using one of Google's TPU chips to make a prediction, which the company says would typically take several hours to accomplish using physics-based models on a supercomputer.

DeepMind releases a new weather forecasting model for more accurate predictions

Google's DeepMind , a new version of its AI weather prediction model. The company promises that it "delivers more efficient, more accurate and higher-resolution global weather predictions." To that end, it should be able to provide accurate forecasts up to two weeks out, including information on temperature, pressure and wind. It should also be able to better predict tropical storm tracks, according to researchers. This means that predictions of a hurricane's path should be accurate up to three days out. The previous model only predicted things up to two days ahead of the storm. The model also brings hourly forecasts into the mix. All told, Google says this new model is eight times faster than the previous iteration. This should help businesses like energy traders make more precise decisions, . "It gives you a more granular forecast," DeepMind AI researcher Akib Uddin said. "Many other industries are quite interested in these one-hour steps. It helps them make more precise decisions. Their goal is, how can they make their business more resilient to weather?" The improvements here stem from a new approach to weather models, as explained in this . Older methods demanded the use of machine learning models that were built for image and video generation. This required repeated processing to ensure an accurate result. The new model only requires a single processing step, which also reduces reliance on costly AI computing systems. AI may not be , despite what proponents want you to believe, but it is . Newer models typically , even those that rely on supercomputers. These models aren't perfect, however, as even Google has acknowledged that WeatherNext 2 will likely struggle to predict outlier rain and snow events. This is due to gaps in the training data. "It's one limitation of our forecast, but one that we are working on improving," DeepMind research scientist Ferran Alet told Bloomberg.

Google apps are getting better weather forecasts

Google's WeatherNext 2 is here, and it's not just some advanced AI model to help with Gemini. It is designed to give faster and much more detailed global weather predictions. This should fundamentally change how Google generates forecasts, making them smarter, quicker, and more useful. The speed improvements alone are hard to believe. The biggest problem with old physics-based models was that they often took hours on a supercomputer to produce comparable data. Now, that massive time sink is almost entirely eliminated. WeatherNext 2 is capable of generating forecasts eight times faster than its predecessor. This lets you get updated predictions much more quickly than before, which didn't take very long to begin with. The real magic happens when you look at how well it operates. This AI model can generate hundreds of possible weather scenarios in under a minute, and it does this using just a single Tensor Processing Unit, which is also hard to believe. The numbers are just as impressive when talking about accuracy. Google reports that WeatherNext 2 surpasses the previous state-of-the-art WeatherNext model on a whopping 99.9% of variables and lead times. This covers everything you care about, like temperature, wind, and humidity, and forecasts spanning from zero up to 15 days out. This new model can also give higher-resolution predictions, drilling down to one-hour increments. This level of detail is exactly what you need when planning a trip or deciding when to run errands, not just a vague idea of the weather for the whole afternoon. One of the most interesting parts of WeatherNext 2 is its ability to predict hundreds of possible weather outcomes from a single starting point. This includes those low-probability, but potentially catastrophic, weather events that are the most important to plan for, so it is ready to move forward should the weather change for the worse. This is powered by a new AI modeling technique called a Functional Generative Network, or FGN. This technology is clever because it injects "noise" directly into the model's architecture. This makes sure that even though the model is creating many different possible outcomes, the forecasts it generates remain physically realistic and interconnected. It's like creating hundreds of slightly different, but still plausible, timelines for the weather. You're going to see more accurate and detailed forecasts across a huge range of apps. This includes updates to forecasts in Search, Gemini, and the dedicated Pixel Weather app. The improved weather information is also hitting the Google Maps Platform's Weather API. In the coming weeks, it will also help power the standard weather information you see directly in Google Maps. Beyond regular apps, Google is also making this powerful data accessible to the scientific and business communities. So if you are interested, the forecast data from WeatherNext 2 is now available in Earth Engine and BigQuery. This isn't the end of the upgrades coming to our weather apps. Google DeepMind and Google Research will continue to research more capabilities to improve the models even further. This includes integrating new data sources and expanding access even further down the road. However, now we can benefit by seeing our own apps become faster and more accurate.

WeatherNext 2 is Google's most accurate forecasting model, now used by Pixel Weather & Search

Google DeepMind and Google Research today announced WeatherNext 2 as its "most advanced and efficient forecasting model." Notably, it's helping power forecasts in Google's consumer apps, including Pixel Weather. At a high-level, "WeatherNext 2 can generate forecasts 8x faster and with resolution up to 1-hour." It can predict wind speed and direction, precipitation, pressure, and other weather variables. This model's main advancement is predicting "hundreds of possible weather outcomes from a single starting point." This includes "low-probability, but catastrophic, weather events." Similar predictions would previously take "take hours on a supercomputer using physics-based models," but can now be done in under a minute using a single TPU and this AI approach. Under-the-hood, WeatherNext 2 generates four 6-hour forecasts per day. The model's input is the "most recent global weather state." A Functional Generative Network (FGN) is then used to produce slightly different forecasts, with those generated predictions then fed back into the model. This approach is particularly useful for predicting what meteorologists refer to as "marginals" and "joints." Marginals are individual, standalone weather elements: the precise temperature at a specific location, the wind speed at a certain altitude or the humidity. What's novel about our approach is that the model is only trained on these marginals. Yet, from that training, it learns to skillfully forecast 'joints' -- large, complex, interconnected systems that depend on how all those individual pieces fit together. This 'joint' forecasting is required for our most useful predictions, such as identifying entire regions affected by high heat, or expected power output across a wind farm. WeatherNext 2 surpasses Google's previous model "on 99.9% of variables (e.g. temperature, wind, humidity) and lead times (0-15 days)." The company has now incorporated WeatherNext technology and models "into the core forecasting system that powers all of Google's weather features." As such, you will see more accurate weather forecasts in Search, Gemini, Pixel Weather, and soon Google Maps. Meanwhile, businesses, scientists, and developers can access WeatherNext 2 via Google Cloud Vertex AI, Big Query, and Earth Engine. Looking ahead, Google will be "integrating new data sources, and expanding access even further."

Why Google's smarter weather might be the most useful AI in your life

WeatherNext 2 is now powering forecasts in Google Search, Gemini, Pixel, and Maps Google is overhauling your weather forecast with AI that thinks in probabilities. Rather than new radar towers or satellite launches, the new WeatherNext 2 AI-based forecasting model developed by Google DeepMind and Google Research delivers results up to eight times faster than traditional systems and can predict hundreds of possible weather outcomes from a single starting point at higher resolution and with better accuracy than its predecessors. WeatherNext 2 is being integrated into many of the most popular Google platforms, including Google Search, Gemini, Pixel Weather, and Maps, with a broader rollout coming soon via the Google Maps Platform Weather API. What makes this upgrade more than just a back-end refresh is the sheer scale of its ambition. WeatherNext 2 is built to account for uncertainty in unusual ways. Where older models might spit out a single most-likely outcome, WeatherNext 2 can generate hundreds of potential futures, allowing forecasters and you to see a full spread of possibilities. It also means your forecast might not just say "Rain, 40% chance," but instead show multiple coherent outcomes for your afternoon walk, with better insight into what could actually happen, and when. WeatherNext 2 uses what Google calls a Functional Generative Network (FGN). The model doesn't rely solely on finished forecasts or entire weather systems; instead, it's trained on individual, standalone variables such as temperature, wind speed, and humidity. The model then works out how those variables interact to create "joints," the complex, real-world patterns like storm fronts, heat waves, or regional wind shifts. Google claims this architecture enables WeatherNext 2 to outperform even its own previous best-in-class model, delivering more accurate forecasts for 99.9% of variables across for up to 15 days out. It's also much faster at making its predictions, completing a full forecast in under a minute. By comparison, traditional physics-based forecasting can take hours on a supercomputer. This efficiency allows for more frequent, more detailed forecast updates. After considerable testing, Google Gemini will start showing forecasts based on WeatherNext 2's outputs, as will Google Maps. The average person could theoretically gain many benefits from the upgrade. Weather forecasting is one of those invisible systems that underpins an extraordinary range of decisions. Make it more accurate, and you remove a thousand tiny sources of stress from people's days. There are bigger implications, too. For instance, with smarter weather predictions, renewable energy providers can better estimate wind and solar output, and emergency services can respond with more precision when forecasts capture uncertainty rather than masking it. That emphasis on uncertainty is key. Forecasting isn't about being perfectly right, but preparing wisely for what might happen. By providing a range of physically realistic, interconnected scenarios, WeatherNext 2 nudges forecasting toward something more strategic. This may not solve the chaos of climate change and related natural disasters, but it might be a boon for those planning ways to better address them. AI-powered forecasting starts to look like essential infrastructure. Better data means making better decisions. And for the weather, that means a lot more than just helping us decide what kind of coat we need in the morning.

Google Search and Gemini users to get more accurate, timely weather forecasts powered by AI

WeatherNext 2 is a breakthrough model that redefines everyday forecasting, bringing pro-level accuracy to your phone and enterprise-grade insights to industries worldwide. What's happened? Google has introduced WeatherNext 2, its latest AI-powered weather forecasting model, designed to deliver faster, more accurate forecasts. WeatherNext 2 is developed by Google DeepMind and Google Research. It uses a new Functional Generative Network (FGN) architecture that can simulate hundreds of possible weather scenarios from a single input. The new architecture allows for richer, more reliable predictions. It is also available in Google Search, Gemini, and Pixel Weather. Developers and enterprises can access WeatherNext 2's datasets in Earth Engine, BigQuery, and through early-access APIs in Vertex AI. Why is this important? Weather affects everything from planning commutes, travelling, agriculture, energy grids, and emergency response. And hence, improving forecast accuracy and speed can benefit all fields. WeatherNext 2 forecasts up to eight times faster and shows measurable gains across 99.9% of tested variables, such as temperature, wind, and humidity. As extreme weather becomes more frequent, reliable short- and long-range predictions are increasingly critical for safety and preparedness. Integrating such a model into ubiquitous products like Search, Gemini, and Maps means more people will have access to better forecasts. Recommended Videos Why should I care? When you open Google Search or check the weather via a Google-enabled service provider, you'll get more accurate, hour-by-hour forecasts, which can benefit your outdoor activities or travel plans. For travellers or people using apps with Google Maps integration, the weather layer will become more useful. The upcoming Maps integration means clearer weather-impacted routing, better storm tracking, and more useful travel and outdoor activity alerts. For power users or businesses, improved datasets allow better optimisation of deliveries, events, or outdoor operations. OK, what's next? Google has also confirmed that Google Maps will be the next major product to adopt WeatherNext 2, bringing more advanced weather layers, alerts, and real-time overlays to its popular navigation and travel tools. In the near future, the company may introduce personalized insights, like predictive rain windows for your commute or automated task suggestions based on forecast shifts. AI-first forecasting could reshape the global weather industry as Google and others move beyond traditional weather modelling.

Google Unveils New AI Weather Model With Faster, More Accurate Forecasts - Decrypt

A new modeling approach, Functional Generative Networks, boosted accuracy on key measures, including extreme wind and cyclone tracking. Google DeepMind introduced a new AI-powered weather-forecasting system on Monday, capable of generating global weather predictions eight times faster than traditional tools, it said. Dubbed WeatherNext 2, the system is being positioned as a tool to help agencies prepare for severe conditions more quickly, as the world continues to grapple with frequent natural disasters spurred by an increasingly warming climate. To do this, it generates hundreds of possible scenarios from a single starting point, each computed in under a minute on a single Tensor Processing Unit, a specialized chip developed by Google to accelerate machine learning and AI workloads. "We rely on accurate weather predictions for critical decisions-from supply chains to energy grids to crop planning," Google DeepMind research scientist Peter Battaglia wrote on X. "AI is transforming how we forecast weather." WeatherNext 2 forecast is already running in Search, Gemini, Pixel Weather, and the Google Maps Weather API, with broader support coming at a later date. "We're working with the Google teams to integrate WeatherNext into our forecasting system," WeatherNext 2 product manager Akib Uddin said in a statement. "Whether you're on search, Android, or Google Maps, weather affects everyone, and so by making better weather predictions, we're able to help everyone." Conventional models can take hours, limiting how often scenarios can be refreshed, DeepMind said. By using advanced AI, WeatherNext 2 outperformed its earlier operational model, WeatherNext Gen, the company claims. "It's about eight times faster than the previous probabilistic model that we released last year, and in terms of resolution, it is six times greater," Battaglia said in a statement. "So instead of making six-hour steps, it takes one-hour steps. It outperforms the previous weather next gen on 99.9% of the variables that we tested." In practical terms, that means the new system produced more accurate forecasts of temperature, wind, humidity, and pressure almost everywhere and at nearly every point in the 15-day window. DeepMind attributed the gains to a new modeling approach described in a June research paper on Functional Generative Networks, or FGN, which changes how the system represents uncertainty and generates forecast variations. FGN is trained only on single-variable forecasts, or "marginals," such as temperature, wind, or humidity at a specific location, according to Google. Despite this, the model learns how those variables interact, allowing it to predict broader, interconnected patterns, such as regional heat events and cyclone behavior. Google said FGN matched GenCast on extreme two-meter temperature forecasts and exceeded it on extreme ten-meter wind forecasts, depending on the variable. The model also showed stronger calibration across lead times and better performance when forecasts were evaluated over larger regions rather than individual points. Using the Continuous Ranked Probability Score -- a standard accuracy metric that checks how closely a model's full range of predicted outcomes matches what actually occurred -- the paper reports average improvements of 8.7% for average-pooled CRPS and 7.5% for max-pooled CRPS compared with GenCast. FGN also improved tropical cyclone forecasts. Compared with historical tracks from the International Best Track Archive for Climate Stewardship, the ensemble-mean predictions reduced position errors by about 24 hours of lead time between three- and five-day forecasts. A version of FGN run at 12-hour timesteps showed higher error than the six-hour version but still outperformed GenCast at lead times beyond two days. Track-probability forecasts showed higher Relative Economic Value across most cost-loss ratios and lead times. DeepMind said experimental cyclone-prediction tools built with this technology have been shared with weather agencies. "You get more accurate forecasts, and you get them faster, and that helps everyone make the right decisions, especially as we start seeing more and more extreme weather," Uddin said. "I think there's a whole spectrum of applications for better weather forecasting."

Google launches WeatherNext 2 with FGN architecture

Google has introduced WeatherNext 2, an AI-powered weather forecasting model developed by Google DeepMind and Google Research, while announcing Gemini 3 Pro and Antigravity. The model employs a new Functional Generative Network architecture to simulate hundreds of possible weather scenarios from a single input, delivering faster and more accurate forecasts now available in Google Search, Gemini, and Pixel Weather. WeatherNext 2 generates richer predictions by processing inputs through its Functional Generative Network (FGN) architecture. This design enables the simulation of multiple weather scenarios simultaneously, which enhances reliability in forecasting outcomes. Users accessing Google Search encounter these improved forecasts directly in search results. Gemini integrates the model for conversational weather queries, while Pixel Weather on compatible devices provides dedicated, precise updates. Developers and enterprises gain access to WeatherNext 2 datasets via specific Google platforms. Earth Engine offers geospatial data integration for environmental analysis. BigQuery supports large-scale data querying and analytics. Early-access APIs in Vertex AI allow programmatic integration into custom applications, facilitating advanced weather-dependent operations. WeatherNext 2 produces forecasts up to eight times faster than previous models. It demonstrates measurable gains across 99.9% of tested variables, including temperature, wind speed, and humidity levels. These variables cover essential meteorological elements tracked in daily and extended predictions. Weather influences commutes, travel itineraries, agricultural schedules, energy grid management, and emergency response protocols. Enhanced forecast accuracy and speed support planning in these areas. As extreme weather events increase in frequency, short-range and long-range predictions aid safety measures and preparedness efforts. Integration into widely used products expands access to superior forecasts. Google Search delivers hour-by-hour details for immediate planning. Services with Google Maps integration benefit from upgraded weather layers. Users receive clearer routing adjusted for weather conditions, precise storm tracking, and alerts for travel or outdoor activities. Google confirmed Google Maps as the next major product to adopt WeatherNext 2. This update introduces advanced weather layers, real-time overlays, and targeted alerts within navigation and travel tools. Power users and businesses leverage improved datasets to optimize deliveries, event scheduling, and outdoor operations. In the near future, Google may introduce personalized insights through the model. Examples include predictive rain windows tailored to individual commutes and automated task suggestions responding to forecast changes.

Google's Latest Weather Forecasting AI Model Is Available to Users

* Google DeepMind says it is eight times faster than GenCast * The model is also powering Google Search and Gemini * WeatherNext 2 supports a resolution of up to one hour Google DeepMind and Google Research introduced a new weather forecasting artificial intelligence (AI) model on Monday. Dubbed WeatherNext 2, it is the successor of last year's GenCast model and comes with several improvements. The Mountain View-based tech giant states that the model is now eight times faster and features an increased resolution (enabling detailed weather monitoring) for one hour. This is also the first time the company is bringing the AI model outside of its research labs and letting users access its capabilities. DeepMind's WeatherNext 2 Released to Users In a blog post, the tech giant detailed the new weather forecasting model and highlighted its availability. WeatherNext 2's data can now be accessed from Earth Engine and BigQuery. It is also being made available to Google Cloud's Vertex AI platform as part of an early access programme. Notably, the company already uses the technology in Search, Gemini, Pixel Weather, and Google Maps Platform's Weather application programming interface (API). Put simply, WeatherNext 2 can generate hundreds of possible weather scenarios from a single input in less than a minute, using just one Tensor Processing Unit (TPU), Google's custom chip for AI work. This is a big deal because traditional weather models, which rely on physics-based simulations, often take hours to run on supercomputers. The WeatherNext 2 shows measurable improvements, Google claims. It is said to outperform GenCast on 99.9 percent of key variables (like temperature, humidity, wind) and across lead times up to 15 days. It also delivers higher temporal resolution, meaning it can provide more detailed, hour-by-hour forecasts. The technology behind the AI model is based on a new architecture called a Functional Generative Network (FGN). Rather than just producing a single line-of-best-guess forecast, FGN injects structured "noise" into the model's parameters so it can generate a variety of realistic and coherent weather futures. The model learns what meteorologists call "marginals" and "joints". Marginals are simple, separate variables, such as temperature or wind speed in a place. Joints are combinations, such as how wind and humidity interact over a whole region. Even though WeatherNext 2 is trained only on marginal data, DeepMind says it excels at forecasting joints too, which is important for complex weather patterns like heatwaves or storms. Notably, WeatherNext 2 runs four times a day, producing six-hour forecasts each time.

Google's WeatherNext 2 pushes global forecasting to one hour resolution

Google DeepMind and Google Research introduced WeatherNext 2, their most advanced weather forecasting AI model, on a specified date. The model delivers 8x faster global forecasts at up to 1-hour resolution by generating hundreds of scenarios from a single input through noise injection in function space. This supports weather agencies with experimental cyclone predictions and integrates into Google products. Weather influences daily decisions across global supply chains, flight paths, and personal commutes. Artificial intelligence has expanded capabilities in weather forecasting over recent years. WeatherNext 2 represents the latest advancement from the WeatherNext team, focusing on efficiency, accuracy, and resolution in predictions worldwide. The model generates forecasts eight times faster than predecessors while achieving hourly resolution. This speed stems from processing on a single Tensor Processing Unit (TPU), where each scenario prediction completes in less than one minute. In contrast, physics-based models on supercomputers require hours for equivalent computations. WeatherNext 2 has already aided weather agencies by providing experimental cyclone predictions based on scenario ranges, enabling decisions informed by multiple outcomes. Forecast data from WeatherNext 2 now resides in Earth Engine and BigQuery, allowing users direct access to these datasets. Google launched an early access program on Google Cloud's Vertex AI platform, permitting custom model inference for participants. These steps move the technology from research labs into practical applications for broader utilization. WeatherNext technology now enhances forecasts within Google Search, Gemini, Pixel Weather, and Google Maps Platform's Weather API. In the coming weeks, WeatherNext 2 will power weather information displayed in Google Maps, extending its reach to mobile navigation and planning tools used by millions daily. From one initial input, WeatherNext 2 employs independently trained neural networks and injects noise directly into function space. This method produces coherent variability across hundreds of possible weather outcomes, capturing the full spectrum of possibilities. Such coverage proves essential for planning around worst-case scenarios, which demand precise preparation in meteorology and beyond. WeatherNext 2 outperforms the prior WeatherNext model across 99.9% of variables, including temperature, wind, and humidity, and all lead times from zero to 15 days. These metrics reflect higher skill levels and finer hourly resolution. The Continuous Ranked Probability Score (CRPS) comparisons confirm these gains against WeatherNext Gen, quantifying superior probabilistic accuracy in ensemble predictions. Central to these improvements is the Functional Generative Network (FGN), a new AI modeling approach. The FGN injects noise into the model architecture itself, ensuring generated forecasts maintain physical realism and interconnections between variables. This architectural innovation preserves spatial and temporal consistency, distinguishing it from traditional noise addition techniques. Video: Google The model excels in forecasting both marginals and joints. Marginals encompass individual weather elements, such as precise temperature at a specific location, wind speed at a designated altitude, or humidity levels at a given point. Training occurs exclusively on these marginals. Despite this limited scope, the model acquires the ability to predict joints accurately -- complex, interconnected systems reliant on interactions among multiple elements. Joints include predictions for entire regions impacted by high heat or aggregated power output from wind farms spanning large areas. These forecasts depend on how individual marginals combine dynamically. WeatherNext 2's capacity to derive joint distributions from marginal training data marks a key technical achievement, enabling applications in energy production, agriculture, and disaster management that require holistic system views. Development of WeatherNext 2 translates research into operational tools. Google DeepMind and Google Research commit to advancing model capabilities through integration of new data sources. Plans encompass further expansion of access to these tools. Provision of open data and platforms aims to support researchers, developers, and businesses. Users can explore related geospatial and AI initiatives via Google Earth, Earth Engine, AlphaEarth Foundations, and Earth AI for deeper context on these efforts.

How WeatherNext 2 works: Google DeepMind's AI model for faster, more accurate forecasts

Google's advanced WeatherNext 2 model improves reliability of extreme weather monitoring Weather forecasting influences decisions across every part of modern life - flight operations, agriculture, retail planning, energy management and public safety. Yet the tools behind these forecasts haven't always been fast or flexible enough to keep up with the world's increasingly unpredictable climate patterns. Traditional numerical weather prediction models simulate the laws of physics across the atmosphere, but doing so requires supercomputers and hours of processing. In moments where minutes matter, that delay can be crucial. Google DeepMind's WeatherNext 2 proposes a new solution. It uses artificial intelligence to deliver global forecasts in less than a minute, while offering more detail and scenario diversity than many conventional systems. This explainer breaks down how it works and why it's considered one of the most significant steps forward in AI-driven meteorology. Also read: Grok 4.1 explained: What's new, better, and why it matters for you Physics-based forecasting remains the gold standard because it mimics real atmospheric behaviour. But this precision comes with a cost. These models are computationally intensive, generate results slowly and make it difficult to run large ensembles, multiple versions of the same forecast that represent uncertainty. Ensembles are vital for predicting extreme weather, but many regions simply don't have the computing resources to generate them frequently. AI-based models flip this approach. Instead of simulating the atmosphere from scratch, they learn patterns from decades of global weather data. Once trained, they can produce forecasts quickly while still capturing complex interactions within the climate system. WeatherNext 2 significantly upgrades DeepMind's earlier models in three crucial ways: Faster predictions: The system can generate global forecasts in under a minute, making rapid updates and high-frequency insights possible. Higher accuracy: Google reports that WeatherNext 2 outperforms the previous version across almost all evaluated variables - temperature, wind, humidity and rainfall - up to 15 days out. Also read: Inside ChatGPT: OpenAI's new LLM reveals secret of AI's inner working Multiple scenarios, not just one: WeatherNext 2 excels at creating hundreds of plausible outcomes from a single atmospheric snapshot. This ensemble-style forecasting is extremely valuable for disaster planning and risk assessment. The system is built on a large neural network trained with historical weather patterns, satellite data and outputs from traditional forecasting systems. WeatherNext 2 takes the latest global data grids, representing variables like pressure, wind and moisture, and sets them as the initial state. A major innovation is its "Functional Generative Network." By adding controlled randomness during prediction, the model can output many different but physically coherent future states. This allows forecasters to explore uncertainty, which is a natural part of weather prediction. Inspired by the same architecture used in large language models, WeatherNext 2 uses transformer components to understand long-range patterns and interactions across the globe. This helps capture complex systems such as monsoons, jet streams and atmospheric waves. The model can provide predictions at hourly resolution for up to 15 days, offering a fine-grained view of weather evolution. AI forecasts depend on the data they are trained on. Gaps, errors or biases can influence predictions, and very localised phenomena still require specialised regional models. For now, AI works best alongside physics-based systems, not as a replacement. WeatherNext 2 represents a major step toward more democratic, faster and more reliable forecasting. As extreme weather becomes more frequent, tools like this could help communities prepare earlier and respond more effectively, marking a significant evolution in how the world understands and anticipates the atmosphere.