Extreme weather has become a fact of life, from record-breaking storms to prolonged droughts. As our climate shifts, the need for faster, more accurate weather forecasts has never been greater. Traditional forecasting relies on physical simulations run on supercomputers – a resource-intensive process that can take hours to churn out a prediction. Today, artificial intelligence (AI) is changing the game. Prominent tech companies like Google’s DeepMind, NVIDIA, and Microsoft have developed AI-driven weather prediction models that learn from vast troves of data and predict the weather in a fraction of the time of conventional methods. These breakthroughs promise not only to tell us if we need an umbrella tomorrow, but also to help society better prepare for disasters, optimize farming and energy use, and adapt to climate change in the long run.

How AI Changes the Forecasting Game

To appreciate these innovations, it helps to know how AI weather forecasting differs from traditional approaches. Classic weather models, called Numerical Weather Prediction (NWP) models, solve complex physics equations for fluid dynamics and thermodynamics on a grid covering the globe. While very accurate, NWPs require enormous computing power and time – for example, one leading European model takes about six hours on a supercomputer to produce a 10-day forecast. In contrast, AI models learn the patterns directly from historical weather data. After training on decades of observations and simulation data, an AI model can “recognize” atmospheric patterns and extrapolate them forward in time – essentially, it develops an intuition for the weather. This approach dramatically speeds up forecasting. Google DeepMind’s latest AI can predict up to 10 days ahead in under a minute on a regular computer. The tradeoff is that the AI isn’t explicitly told about physics – it infers the rules from data – but as we’ll see, with enough training data and smart design, AI forecasts can rival those from traditional methods in accuracy.

Another advantage is that AI models can be updated very frequently (since they run so fast) and even tuned to specific needs. For instance, there are AI models focused on “nowcasting,” predicting very short-term changes (hour-by-hour weather) which is vital for things like aviation and storm tracking. AI models can also output probabilistic forecasts by running many slight variations (ensembles) to capture uncertainty. This is key because weather is chaotic – tiny differences can grow into big changes over days. AI ensemble systems can offer a range of likely scenarios instead of a single definitive forecast, giving users a fuller picture of what might happen.

Breakthrough Models from Tech Giants

Major technology companies have poured research into this field, producing breakthrough AI weather models. Let’s explore a few headline innovations from Google DeepMind, NVIDIA, and Microsoft, and what makes each special.

Google DeepMind: GraphCast and GenCast

One of the most talked-about advances came from Google’s DeepMind in late 2023 with an AI system called GraphCast. In a paper published in Science, the DeepMind team introduced GraphCast as a state-of-the-art AI model for global weather forecasting. GraphCast can predict weather conditions up to 10 days in advance more accurately – and much faster – than one of the world’s best traditional forecasting systems (the European Centre for Medium-Range Weather Forecasts high-resolution model). In practical terms, GraphCast’s forecasts beat 90% of the metrics that meteorologists use to judge forecast quality, according to an evaluation by DeepMind. Incredibly, once it’s trained, GraphCast generates a 10-day forecast in under one minute on a single desktop machine, whereas traditional models might need a large supercomputer running for hours. This speed means forecasts can be updated more frequently, and developing storms can be tracked in near-real-time with iterative runs.

How does GraphCast achieve this? As the name hints, it uses a form of AI called a graph neural network, which is well-suited to data that can be represented as nodes and connections – think of the nodes as points on the globe and connections as the physical relationships between them (like how weather in one region influences another). By training on almost 40 years of historical weather data, GraphCast learned to simulate atmospheric physics implicitly. The result is an AI “weather brain” that can spit out detailed maps of future temperature, pressure, winds, etc., very quickly. Notably, GraphCast excelled at predicting extreme weather events. The DeepMind team reported that it can forecast the tracks of tropical cyclones (hurricanes) with greater accuracy further into the future than traditional models, and better predict extreme temperatures and even phenomena like “atmospheric rivers” that can lead to heavy floods. This ability could translate into earlier warnings for communities in harm’s way, potentially saving lives through better preparedness.

Building on GraphCast, in 2024 DeepMind announced GenCast, a new model that pushes into the uncertain realm of long-range forecasting. GenCast is an ensemble AI model that produces not just one forecast but many possible outcomes, up to 15 days ahead. Why is this important? Because beyond a week, the atmosphere’s chaos makes single forecasts very unreliable. GenCast addresses this by providing a range of scenarios along with the probability of each. In tests, GenCast outperformed the European Centre’s own ensemble system for forecasts two weeks out, both for everyday weather and for extreme events. In simple terms, GenCast might tell us “There’s a 70% chance of a heatwave in two weeks, but a smaller 20% chance it stays mild,” which is incredibly useful for decision-makers. It gives farmers, governments, or disaster agencies time to prepare for the most likely scenarios while being aware of less likely but possible alternatives. This probabilistic approach is a more complete way to communicate forecast uncertainty than a single-line prediction.

DeepMind has open-sourced GraphCast’s code, allowing weather agencies and researchers around the world to experiment with and use it. In fact, the European Centre for Medium-Range Weather Forecasts (ECMWF) ran GraphCast in real-time on their website as an experiment, showcasing confidence in this new method. The impact of GraphCast was recognized with the UK’s top engineering award in 2024 (the MacRobert Award), with judges noting that such highly accurate and timely predictions could help mitigate the impact of severe weather and ultimately save lives. It’s a powerful example of AI for social good.

NVIDIA: FourCastNet and the Earth-2 Initiative

Another major player, NVIDIA – known for its graphics and AI computing hardware – has been advancing AI weather models as part of its Earth-2 climate modeling initiative. NVIDIA’s flagship AI weather model is called FourCastNet (short for Fourier Forecasting Neural Network). In 2022, FourCastNet made waves by being the first AI-based weather forecasting system at 0.25° global resolution to be open-sourced, enabling widespread collaboration​. FourCastNet uses a deep learning approach that leverages the mathematics of Fourier transforms (hence the name) to capture patterns in global weather data. Essentially, it learns how weather fields (like wind or humidity) change in frequency space, which helps it represent large-scale patterns like jet streams and small-scale details simultaneously.

FourCastNet demonstrated that AI can achieve accuracy comparable to leading numerical models for short to medium-range forecasts (several days ahead). An exciting advantage of FourCastNet is its stability and efficiency. Because it’s data-driven, it can generate many forecast scenarios extremely fast. For example, scientists can run an ensemble of thousands of forecasts with FourCastNet in the time a traditional model might run just one – this huge ensemble can improve risk assessments for events like hurricanes by providing a spread of possible tracks and intensities. NVIDIA’s team also improved FourCastNet by ensuring the model “respects” the spherical geometry of the Earth (so it doesn’t treat the North Pole like an edge of a flat image, for instance), which boosted its skill on longer-range predictions​.

The speedups reported are astonishing. In one case study in the United Arab Emirates, NVIDIA’s tools were used to predict local weather at high resolution (down to 200 meters) using a two-step AI pipeline. First FourCastNet produced a global forecast, then a second AI model refined the details for the local region. The result: a full day’s high-resolution forecast generated in about 170 seconds of GPU processing. To put that in perspective, achieving a similar resolution with a traditional model (WRF) took roughly 960 CPU hours – that could equate to a supercomputer running on 960 cores for an hour, or one CPU core running non-stop for 40 days! In other words, what used to take many hours of computing can now be done in a couple of minutes with AI. This speed advantage means forecasters could update predictions hourly or on demand before and during extreme events, getting information out faster to the public and emergency managers.

NVIDIA’s broader Earth-2 initiative envisions a “digital twin” of Earth powered by AI – essentially using models like FourCastNet as building blocks to simulate not just weather but also climate processes with high fidelity. While still in development, the goal is to help forecast extreme events and climate trends with unprecedented detail and lead time. Early results from FourCastNet and its successors show that AI can capture complex phenomena (like the formation of extreme storms) quickly and even perform downscaling (zooming into regional details) as part of the learned process​. This is promising for hyper-local forecasts, such as predicting rainfall over a city neighborhood or wind gusts on a single wind farm – information that traditional global models struggle to provide in real-time.

Microsoft: Project Aurora – A Foundation Model for Weather

Not to be outdone, Microsoft Research introduced Project Aurora in 2024, describing it as the first large-scale foundation model for the atmosphere. In AI lingo, a foundation model is one trained on a broad dataset to create a general-purpose model that can be fine-tuned for various tasks (somewhat akin to how a large language model like GPT can be adapted to many language jobs). Aurora is a 1.3 billion-parameter AI model that was trained on over a million hours of weather and climate simulation data. By exposing it to such diverse and extensive data – including different variables, altitudes, and regions – Aurora effectively learned the climate system in a flexible way. The result is a model that can be specialized for multiple forecasting tasks: Microsoft provides versions of Aurora fine-tuned for medium-range weather, high-resolution short-range weather, air pollution forecasting, and even ocean wave predictions.

Aurora’s performance has been impressive. It delivers 10-day high-resolution weather forecasts and 5-day global air-quality forecasts that outperform both traditional physics models and other specialized deep learning models. In other words, this single AI model, after appropriate fine-tuning, can beat the dedicated systems in each of those domains. For example, it can predict temperature, wind, and other variables with greater accuracy than the classical weather models it was benchmarked against, and likewise it can predict particulate matter levels (air pollution) better than established chemical transport models – all within one unified system. This speaks to the power of the foundation model approach: by learning a general representation of atmospheric dynamics, Aurora can tackle different but related problems without starting from scratch each time.

Microsoft’s researchers highlight that Aurora could help with extreme event prediction, citing cases like Storm Ciarán, a powerful storm that hit Europe in 2023. Even advanced models had trouble forecasting the storm’s sudden intensification. A model like Aurora, with its broad training, might capture such unusual events better, improving early warnings. Technically, Aurora uses a cutting-edge architecture (a 3D Swin Transformer with Perceiver IO – terms a lay reader need not worry about) that allows it to handle three-dimensional atmospheric data efficiently. What matters is that these design choices let the model learn from varied data (satellite images, weather balloons, simulations) and operate at a high spatial resolution of about 0.1° (around 10 km grids), which is finer detail than many global models use.

Project Aurora reflects a trend in AI: using one general AI model to tackle multiple related challenges in climate and weather. This could streamline forecasting systems (one AI could eventually replace several specialized models) and make advanced forecasts more accessible. Microsoft has been testing Aurora and sharing their findings with the community, and like others, they have made the model available (Aurora’s code and data are on GitHub) to encourage collaboration. The hope is that such foundation models could serve as universal engines for environmental prediction, driving everything from daily weather apps to climate risk tools.

Real-World Applications: From Storms to Farms to Cities

What do these AI breakthroughs mean in practical terms? In this section, we explore how AI-driven weather prediction is being applied in real-world scenarios – helping with disaster preparedness, smarter agriculture, and urban planning – and how these advances tie into our broader fight for sustainability.

Early Warnings for Disasters

One of the most compelling benefits of improved forecasts is the ability to issue earlier and more reliable warnings for extreme weather events. AI models like GraphCast can identify the seeds of a severe storm or hurricane days in advance, often detecting subtle signals that might be missed or computed too slowly by traditional methods. For instance, GraphCast has shown skill in predicting the tracks of tropical cyclones further ahead, giving extra lead time compared to conventional forecasts. Even an extra 12–24 hours of warning can be critical for evacuations or securing infrastructure in the path of a hurricane.

Flooding is another area where AI forecasts are making a difference. Google has developed an AI-based flood forecasting system that can provide accurate river flood warnings up to 7 days in advance, even in regions with sparse data. As of early 2024, this technology was rolled out in over 80 countries, helping to protect some 460 million people in flood-prone areas with free real-time forecasts on the Flood Hub platform. These AI models take in weather forecasts (for rainfall), data about rivers and terrain, and then rapidly compute how rivers will swell and which areas might be inundated. The speed is crucial: in the past, predicting detailed flood inundation maps could take all day, by which time a flash flood might have already struck. Now, machine learning models can generate those maps in just a minute or two​. Researchers in New Zealand, for example, demonstrated an AI system that produces street-level flood risk maps 1–2 minutes after receiving a forecast, versus the 24 hours such calculations used to require​. With that kind of turnaround, emergency managers can get a much clearer picture of who and what is at risk well before flood waters arrive, enabling faster alerts and more targeted evacuations (even down to neighborhoods or streets that need it most).

Extreme heat and wildfires are another growing threat. Better prediction of heatwaves – in terms of both timing and intensity – allows cities to implement heat action plans (like opening cooling centers, issuing public health warnings, or adjusting the power grid load) to reduce heat-related illnesses. AI models that excel at identifying the precursors to heatwaves or spotting the risk of sudden temperature spikes can give health officials and utilities a valuable head start. Similarly, in wildfire-prone areas, AI-enhanced weather forecasts improve the prediction of high wind events and dry conditions, which are key factors in fire risk. This can aid in pre-positioning firefighting resources and issuing early red-flag warnings to communities.

In summary, when it comes to disasters, every minute counts. By delivering forecasts faster and with greater detail, AI weather models ensure that warnings for hurricanes, floods, and heatwaves can be disseminated with more lead time and confidence. The long-term impact is measured in lives saved and damages avoided. As climate change continues to amplify weather extremes, these AI tools become critical components of global disaster resilience strategies.

Smarter Agriculture and Food Security

Farmers have always danced with the weather, planning their activities around seasonal rains, temperatures, and the ever-fickle whims of nature. With AI weather forecasting, that dance becomes a lot more informed. Agriculture stands to gain immensely from more accurate and granular weather predictions, as they enable better decision-making to protect crops and optimize resources.

One immediate application is smart irrigation. Water is a precious resource, and farmers need to know when to water crops – too much or too little at the wrong time can harm yields. AI-driven weather models can predict not just if it will rain, but how much and when, which can be used to schedule irrigation optimally. Modern smart irrigation systems are being paired with AI forecasts to automatically adjust watering schedules. The result is significant water savings – integrating AI into irrigation has been shown to cut water usage by up to 25% while maintaining healthy crops​. This is achieved by watering only when necessary and in the right amounts, based on predicted weather and soil moisture, rather than on fixed routines or reactive guesses.

Precise forecasts also help farmers with planting and harvesting schedules. AI/ML models can analyze long-range weather outlooks to suggest the best windows for planting a particular crop or the ideal time to harvest before a major storm. For example, if a farmer knows that in two weeks there’s a high likelihood of a dry spell, they might plant seeds now so that they germinate during anticipated rain and establish roots before the dry period. Or if an unusually wet harvest season is predicted, they might invest in faster harvesting equipment or drying facilities to prevent rot. By aligning farm activities with weather forecasts, growers can increase yields and reduce the risk of crop losses due to unexpected frosts, heatwaves, or floods.

Furthermore, AI forecasts contribute to effective pest and disease management. Many crop pests and diseases are influenced by weather conditions (for instance, certain fungi spread in prolonged humidity, while some locust swarms proliferate after heavy rains). With better predictive data, farmers and agricultural agencies can anticipate outbreaks and take preventative measures, like deploying pesticides or protective measures at just the right time. This means less blanket use of chemicals (saving money and the environment) and more targeted actions.

In a larger sense, by stabilizing crop production and improving efficiency, these technologies bolster food security. As climate change makes weather patterns more erratic, having AI that can forecast these swings becomes a key adaptation tool for agriculture. It allows farmers big and small to adapt to climate variability, ensuring communities continue to have food on the table. Also, when farming uses water and fertilizers more efficiently, it reduces runoff pollution and lowers the energy footprint (for example, pumping irrigation water or manufacturing fertilizers), indirectly cutting greenhouse gas emissions. It’s a virtuous cycle: better forecasts lead to smarter farming, which leads to more sustainable use of land and water – crucial as we strive to feed a growing population sustainably.

Urban Planning and Infrastructure Resilience

Cities and towns are complex systems that are highly sensitive to weather. Urban planners and city managers are increasingly leveraging AI-driven forecasts to make smarter decisions for infrastructure and public services, both in day-to-day operations and long-term planning.

Consider flood management in urban areas. A sudden cloudburst can overwhelm city drainage, leading to flash floods that disrupt traffic and damage property. Traditional flood models might warn that a city will get, say, 100 mm of rain, but not precisely which neighborhoods will flood. AI models, especially when coupled with detailed local data, can provide street-by-street flood forecasts in near real-time​. This can guide emergency responders to the most affected zones faster and help utilities manage drainage systems (e.g., by lowering levels in retention basins ahead of time). Over the longer term, urban planners can use these high-resolution predictions to identify vulnerabilities in infrastructure – perhaps a particular low-lying road or an underserved drainage district that consistently shows up in simulation as high-risk – and prioritize those for upgrades. In essence, AI forecasts are becoming a tool for urban resilience planning, highlighting where flood defenses need strengthening or where zoning laws might need to prevent construction on floodplains.

Heatwaves in cities pose another challenge. Urban areas often experience the “heat island” effect, where concrete and asphalt make cities hotter than surrounding rural areas. Advanced weather prediction can pinpoint when a heatwave will hit and how severe it will be, allowing city officials to prepare cooling centers, adjust work hours, and issue health advisories. Some cities are experimenting with AI that combines weather forecasts with data on power usage and health records to anticipate heat-related stress on the electric grid and hospital systems, so they can adapt capacity as needed. Likewise, accurate forecasts of cold snaps ensure road crews and public transit are ready for snow and ice, improving safety and reducing disruptions.

On the infrastructure front, energy and water utilities benefit enormously from better weather data. For example, a city’s power grid operators need to balance supply and demand. If you can predict a cold front or a heatwave a few days ahead, you can manage power generation (or purchase) to meet the upcoming high demand for heating or cooling, avoiding blackouts or costly last-minute energy buys. Water utilities managing reservoirs and dams can use rainfall and snowmelt forecasts to decide when to release water or store it, maintaining supply while preventing overflow. AI models improve these forecasts, giving a more accurate sense of timing and magnitude of such events, thus optimizing resource management.

In terms of long-term urban planning, climate change means cities must prepare for weather extremes outside historical norms. AI models can be used in scenario planning – for instance, to simulate how often a city might expect extreme flooding or hurricanes by 2050 under various climate scenarios. Planners and engineers can use this information to design infrastructure that will withstand future conditions, not just past ones. In fact, international bodies are exploring AI as a key tool to link climate modeling with adaptation planning, helping governments develop tailored, efficient climate resilience strategies. This might mean updating building codes for wind resistance in hurricane zones or redesigning urban layouts to include more green spaces that naturally cool and absorb rainwater.

From daily operations like traffic management during a storm, to multi-decade city development plans, AI-enhanced weather forecasting is becoming an indispensable compass for urban decision-makers. It helps ensure that our cities are not just smart, but also safe and sustainable in the face of Mother Nature’s challenges.

Long-Term Implications for Global Sustainability

Beyond immediate applications, the rise of AI-driven weather prediction has profound long-term implications for environmental sustainability. Accurate and faster forecasts are powerful tools in the fight against climate change – both in mitigating its causes and adapting to its effects.

Reducing Greenhouse Gas Emissions: At first glance, weather forecasts might not seem directly related to carbon emissions, but they absolutely are. A prime example is the integration of renewable energy into our power grids. Solar and wind energy are weather-dependent – operators need to know when the sun will shine or the wind will blow, and how strongly. Improved forecasting helps grid managers plan for these fluctuations. AI models that predict sunshine duration or wind speeds days in advance allow utilities to maximize the use of renewable sources when they’re available and to efficiently schedule backup power (like gas turbines) when they’re not. By accurately predicting periods of high renewable output, utilities can reduce reliance on fossil-fuel backups, which means lower emissions and often lower costs too​. They can also avoid wasting renewable energy: for instance, if you know a windy spell is coming, you might maintain lower output from other sources to make room for the wind power, rather than curtailing it. In essence, better forecasts lead to a greener grid, smoothing out the unpredictability of clean energy. This directly contributes to cutting greenhouse gases by burning fewer fossil fuels.

Transportation is another sector where weather forecasts can trim emissions. Airlines, for instance, use sophisticated weather forecasts to plan flight routes that avoid headwinds or storms, saving fuel. If AI can improve the accuracy of jet stream predictions or turbulence areas, planes can be routed more efficiently, reducing fuel burn and emissions. Similarly, shipping companies time departures to ride favorable winds and currents, and trucking fleets plan around weather to prevent idling in traffic jams caused by storms. Even consumers adjust heating and cooling usage based on weather; better forecasts help energy companies manage demand response programs (like pre-heating homes when a cold surge is forecast, to avoid peak grid strain later), which in turn optimizes energy use.

Optimizing Resource Use: Sustainability is also about doing more with less. With AI forecasts, we’ve touched on how agriculture can save water and cities can manage resources better. Zooming out, these efficiencies add up to significant resource conservation. Agriculture using 25% less water for the same yield means less strain on rivers and aquifers, preserving ecosystems. Precision farming guided by weather predictions also means fertilizers and pesticides are used more sparingly and effectively, which reduces runoff into waterways and the energy required to produce those chemicals. Over time, this can help restore soils and reduce the agriculture sector’s environmental footprint.

Consider also disaster response and recovery: if we can predict disasters more accurately, we can mitigate their damage. This has a sustainability angle – when a massive storm hits unprepared, it can destroy infrastructure, which then has to be rebuilt at great cost and carbon emissions (manufacturing cement, steel, etc., is carbon-intensive). By contrast, good forecasts allow for protective measures (like temporary flood barriers or controlled dam releases) that can reduce destruction. Fewer buildings destroyed means fewer new materials needed – effectively avoiding emissions. It also means less human suffering, of course, which is its own reward.

Adapting to Climate Change: Even with our best mitigation efforts, some climate change effects are inevitable. We need to adapt to changes like higher sea levels, shifting rainfall patterns, and more extreme weather. AI forecasting provides a sort of early warning system for climate change impacts, giving society time to adjust. For example, if long-term AI-enhanced predictions indicate a trend toward more frequent droughts in a region, that can spur investments in water-saving technologies or drought-resistant crops now, rather than later. On shorter timescales, when a once-rare event becomes more common (say, a 100-year flood now happening every 20 years), improved forecasting ensures communities aren’t caught off guard thinking “this shouldn’t happen in our climate.” Instead, they’ll have data-driven alerts that such an event is coming, because the AI has detected the changed pattern.

Globally, one of the exciting implications of AI weather tech is greater accessibility of high-quality forecasts. Traditional weather prediction prowess has been concentrated in a few countries with big supercomputers and expert teams. AI models, once trained, can be run with far fewer resources. This means developing countries could leapfrog to top-tier forecasting by using these AI models, without needing a giant supercomputer. More nations having better forecasts helps everyone – storms don’t respect borders. We’re already seeing collaborations where tech companies and international organizations provide AI-based forecast tools to regions that previously had very limited forecasting ability. This democratization of weather information strengthens global resilience to climate risks.

Finally, there’s a positive feedback loop: AI models themselves can help climate science progress faster, which in turn guides policy for sustainability. For example, AI can be used to run many climate simulations to explore how interventions (like cutting emissions) might change future extreme weather. They can also process satellite data to monitor deforestation, ice melt, or ocean health in real time, feeding into climate models. By handling massive datasets, AI is enabling more detailed climate modeling that was not feasible before, helping identify the most impactful paths to sustainability.

In summary, the long-term picture is that AI-driven weather forecasting is more than just knowing if you need a raincoat. It’s about creating a more efficient, responsive relationship with our environment. By predicting Earth’s moods with greater clarity, we can reduce waste, prevent harm, and make smarter choices that align with natural patterns. In the grand fight against climate change, these AI breakthroughs are invaluable tools – they help us cut emissions where we can and adapt where we must, guiding us toward a more sustainable and resilient future.

Conclusion: A Forecast for the Future

AI’s growing role in weather prediction represents a heartening convergence of technology and nature. What once required rows of supercomputers humming for hours can now be done by an AI in the span of a coffee break – and with equal or better accuracy. We’ve seen how Google DeepMind’s GraphCast and GenCast, NVIDIA’s FourCastNet, and Microsoft’s Aurora are pushing the boundaries of what’s possible in forecasting, from minute-by-minute updates to multi-week outlooks. But most importantly, we’ve seen what these advances mean for people and the planet: earlier disaster warnings, more resilient food supplies, smarter cities, and progress in the urgent effort to live sustainably on Earth.

The tone of these developments is optimistic and collaborative. Many of the breakthrough models are being open-sourced or shared with the scientific community, inviting meteorologists and data scientists around the world to improve them and apply them. It’s a wonderful synergy – domain experts in climate and weather working with AI experts to refine these tools. As this technology matures, we can expect forecasts to become even more personalized and hyper-local (imagine getting a custom warning for the street you live on), and perhaps extended further out in time with reliable detail. We may also see composite systems that merge the strengths of physics-based and AI-based approaches, for instance using AI to correct and speed up traditional models.

For readers interested in AI and climate, this is a thrilling space to watch. Not only does it show AI’s potential beyond chatbots and games, it underscores how AI can be a force for good – helping humanity adapt to a changing world. The next time you check the weather on your phone and make a decision – be it grabbing an umbrella or evacuating from a hurricane – there may well be an AI behind that forecast, working hard to get it right. And as these systems get better, our collective forecast for the future looks a bit brighter: one where we’re better equipped to protect our communities, use our resources wisely, and face the challenges of climate change with knowledge and preparedness. That’s something we can all look forward to, come rain or shine.

Sources:

1. DeepMind (Google) – GraphCast: AI model for faster and more accurate global weather forecasting
   
2. World Economic Forum – AI can now outperform conventional weather forecasting – in under a minute
   
3. Royal Academy of Engineering – DeepMind’s GraphCast wins 2024 MacRobert Award
   
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5. ECMWF – How AI models are transforming weather forecasting (FourCastNet, GraphCast, etc.)​
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6. NVIDIA Technical Blog – Earth-2 AI weather forecasting (UAE case study)
   
7. Microsoft Research – Introducing Aurora: AI foundation model of the atmosphere
   
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