Weather and climate are often described as different scientific problems, but they are physically inseparable. Weather concerns the rapidly changing state of the atmosphere, oceans, land surface, and cryosphere, while climate describes the statistics and long-term evolution of those same components. A storm forecast may depend on atmospheric structures that develop over hours, whereas climate projections examine how the frequency, intensity, and geographic distribution of such events may change over decades. The divide has also been reinforced by methodology: weather prediction is usually an initial-value problem, beginning with the best possible estimate of today’s atmosphere, while climate projection is largely a boundary-value problem, driven by factors such as greenhouse-gas concentrations, land-use change, and solar variability. AI offers a way to connect these viewpoints by learning patterns across time and space rather than treating each forecasting horizon as an isolated task.

The opportunity comes from the extraordinary growth of Earth-observation data. Satellites, weather stations, radar systems, aircraft, ocean buoys, ships, and research campaigns generate measurements with different spatial resolutions, sampling intervals, and error characteristics. Machine-learning systems can process these heterogeneous data streams far more efficiently than traditional workflows, identifying relationships that may be difficult to encode manually. Deep neural networks can represent nonlinear interactions between atmospheric temperature, humidity, pressure, wind, radiation, soil moisture, sea-surface conditions, and ice cover. Transformer architectures, graph neural networks, and neural operators are especially promising because they can model relationships across large geographical domains and learn how physical fields evolve over time. Their usefulness, however, depends on whether they can distinguish genuine physical signals from correlations caused by gaps, biases, or changes in observing systems.

Recent AI weather models have demonstrated that data-driven forecasts can be produced much faster than conventional numerical prediction systems. Traditional forecasting solves approximations of the governing equations of fluid dynamics, thermodynamics, and radiative transfer on a computational grid. This process requires repeated calculations at many time steps and can consume enormous computing resources. An AI model, once trained, can generate a forecast through a sequence of relatively inexpensive matrix operations. That speed could enable larger ensembles, higher-resolution predictions, or rapid updates as new observations arrive. Yet speed alone does not guarantee scientific value. A model that produces plausible maps but violates conservation of mass or energy may drift into unrealistic states, particularly when it is repeatedly applied far beyond the conditions represented in its training data.

For that reason, the authors emphasize the importance of hybrid approaches that combine machine learning with established physical knowledge. In a physics-informed model, equations or constraints can be incorporated directly into the training process, penalizing solutions that violate known principles. In other systems, AI may replace only the most computationally expensive component of a numerical model, such as a subgrid parameterization for clouds, convection, turbulence, or ocean mixing. These processes occur at scales smaller than the model grid but strongly influence large-scale circulation. A learned parameterization could adapt to local conditions more flexibly than a fixed empirical formula, while a physical solver would continue to enforce the broader structure of the simulation. The result would not be a choice between physics and AI, but a carefully designed partnership between them.

Data assimilation is another area where the weather–climate connection becomes especially important. Forecast models do not begin with a perfectly known atmosphere; they begin with an estimate constructed from incomplete and noisy observations. Data-assimilation methods combine measurements with a model’s previous state to produce an updated analysis. AI could accelerate this process, identify systematic observation errors, and infer unmeasured variables from spatial and temporal context. For example, a model might use satellite radiances and nearby observations to estimate three-dimensional humidity or wind fields. Better initial conditions improve short-range forecasts, while the long archives created through repeated assimilation can also support climate research. The same framework could help reveal how the frequency of extreme events is changing and how much of that change is attributable to natural variability or human influence.

Extreme weather exposes both the promise and the danger of AI forecasting. Heatwaves, floods, droughts, tropical cyclones, and compound events are often rare in the historical record, precisely where data-driven models have the least experience. A neural network trained primarily on ordinary conditions may produce overconfident predictions when confronted with an unprecedented combination of heat, humidity, soil dryness, and circulation. The article therefore highlights uncertainty quantification and robust evaluation as central requirements. Forecast systems should provide probabilities or ensembles rather than a single apparently precise outcome. They must also be tested on withheld regions, unusual seasons, historical extremes, and simulated future climates. Calibration—whether predicted probabilities match observed frequencies—is as important as average forecast accuracy. A model that is slightly less sharp but reliably communicates uncertainty may be far more useful for emergency planning.

Climate applications introduce an additional problem known as distribution shift. The atmosphere of the future will not be statistically identical to the atmosphere on which an AI system was trained. Rising greenhouse-gas concentrations, changing land surfaces, warming oceans, and melting ice alter the underlying distribution of states. Standard machine learning assumes that training and deployment data are drawn from similar populations, an assumption climate change directly undermines. Researchers therefore need methods that can extrapolate safely, detect when a model is operating outside its domain, and incorporate physically based scenarios. Hybrid simulations, transfer learning, causal discovery, and carefully constructed synthetic data may help, but none eliminates the need for independent validation. Future systems will have to explain not only what they predict, but also why the prediction should be trusted under unfamiliar conditions.

The proposed bridge also has consequences for how scientific models are developed and judged. AI systems should not be evaluated solely by comparing maps against observations with a single score. Their performance must be assessed across spatial scales, lead times, variables, regions, and types of event. Researchers need transparent benchmarks, shared datasets, reproducible training procedures, and records of how models respond to changes in sensors or preprocessing methods. Interpretability is equally important. A forecast model does not need to produce a simple verbal explanation for every prediction, but scientists must be able to diagnose failure modes, identify influential observations, and determine whether the model has learned a physically meaningful relationship. Open collaboration among meteorologists, climate scientists, statisticians, computer scientists, and affected communities will be essential because the most accurate model is not necessarily the most useful decision tool.

The emerging vision is a multiscale Earth-intelligence system in which observations, physical equations, and AI models interact continuously. Fast neural forecasts could provide immediate guidance, high-fidelity numerical models could supply physical consistency, and long climate simulations could test how risks evolve under alternative emissions and adaptation pathways. Such a system might help connect a local warning about extreme rainfall with the broader climate conditions that make that event more likely, while also allowing climate projections to benefit from the detailed information contained in short-term forecasting. The authors’ central message is both ambitious and cautious: artificial intelligence can narrow the weather–climate divide, but only if it is developed as a scientific instrument rather than a black-box shortcut. Its success will depend on trustworthy uncertainty estimates, physical consistency, rigorous testing, and a clear understanding of where prediction ends and speculation begins.

Subject of Research: The use of artificial intelligence to connect weather forecasting and climate prediction through integrated, physics-aware, data-driven Earth-system modeling.

Article Title: Bridging the weather and climate divide with artificial intelligence

Article References: Camps-Valls, G., Carrassi, A., de Melo Viríssimo, F. et al. Bridging the weather and climate divide with artificial intelligence. Nature Communications 17, 8578 (2026). https://doi.org/10.1038/s41467-026-75787-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41467-026-75787-y

Keywords: Artificial intelligence, machine learning, weather forecasting, climate prediction, Earth-system modeling, data assimilation, physics-informed AI, extreme weather, uncertainty quantification, neural operators

Modern weather forecasting already depends on enormous volumes of information. Satellites, weather stations, ocean buoys, aircraft sensors and radar systems continuously measure temperature, humidity, wind, pressure and precipitation. Traditional numerical weather prediction then uses physical equations to simulate how the atmosphere evolves. These models have become dramatically more capable, but they require immense computing power and can take substantial time to run. AI introduces a different approach. Machine-learning systems can identify patterns in historical observations and generate forecasts directly, sometimes producing predictions in seconds rather than hours. That speed could be crucial when governments have only a short window to issue warnings or evacuate vulnerable populations.

The most powerful AI weather systems combine several technical strategies. Neural networks can learn relationships between atmospheric variables across vast geographical areas, while “foundation” models trained on decades of reanalysis data can generate forecasts without calculating every atmospheric process from first principles. Some systems use hybrid methods, combining machine learning with physical constraints so that predictions remain consistent with conservation laws and known dynamics. AI can also improve “downscaling,” converting broad regional forecasts into neighborhood-level estimates of rainfall, wind or heat. For a city preparing for flash floods, that additional resolution could mean the difference between a general warning and a precise alert identifying which roads, hospitals or homes face the greatest danger.

Yet forecast quality is only one part of the equation. A warning has value only when it reaches people in a form they can understand and when they have the means to act. AI-generated information may be delivered through smartphone applications, online dashboards, automated messages or digital platforms, but access to reliable electricity, mobile networks, affordable data and internet-connected devices remains deeply uneven. Rural communities and low-income households may also lack the local agencies, emergency shelters or financial resources needed to respond. A highly accurate prediction cannot prevent harm if residents receive it too late, cannot interpret it, or are unable to leave danger.

The inequality problem begins long before an AI model produces its first forecast. Machine-learning systems depend on data, and data are not distributed evenly across the planet. Wealthier countries generally operate denser networks of weather stations, radar installations and satellites, creating detailed records for training and validating algorithms. Many low-income regions have sparse or deteriorating observation systems, particularly in rural areas and across parts of Africa, Asia and small island states. When models are trained primarily on data from well-monitored regions, they may perform less reliably in places with different climates, landscapes or seasonal patterns. This can create a feedback loop in which the areas most in need of better information remain the least represented in the datasets used to build it.

Climate change makes that limitation more serious. AI systems learn from historical examples, but the atmosphere is moving into conditions that have no exact precedent in the observational record. Extreme heat, compound droughts, intense rainfall and rapidly strengthening tropical cyclones may occur at frequencies or combinations that were rare in the past. A model that recognizes patterns efficiently can still struggle when those patterns shift beyond its training experience. Researchers therefore face a central technical challenge: making AI forecasts adaptable to a changing climate while communicating uncertainty honestly. A prediction should not be treated as an unquestionable answer; it is an estimate shaped by data quality, model design and the unpredictability of atmospheric processes.

The study’s warning extends beyond forecasting to the ownership and governance of climate information. The most advanced AI systems are often developed by a small number of technology companies, research institutions and governments with access to specialized chips, massive datasets and highly skilled engineers. If critical forecasting tools become proprietary, poorer countries could depend on external providers for information essential to public safety. Commercial systems may also prioritize profitable markets, high-value infrastructure or users able to pay for premium services. This raises questions about whether climate information should be treated as a private product or as a public good, comparable to clean air, emergency broadcasting or basic health data.

There are ways to prevent AI from becoming another mechanism of exclusion. Investment in national and regional weather services, open observation networks and public-interest computing could help broaden access to the technology. International data-sharing agreements would allow under-monitored regions to contribute to and benefit from global forecasting systems. Models should be evaluated not only by their average accuracy, but also by how well they perform across different countries, climates, languages and socioeconomic settings. Local experts and communities must be involved in designing warning systems, because technical precision does not guarantee cultural relevance. A flood alert written in an unfamiliar language or delivered through an inaccessible platform may fail even if the underlying forecast is excellent.

Researchers also emphasize the importance of transparency. Users need to know what data an AI system was trained on, how often it is updated, where its predictions are reliable and when uncertainty is high. Independent testing can reveal whether a model systematically performs worse in particular regions or during specific hazards. Human expertise remains essential, especially for interpreting unusual events and translating forecasts into decisions. The strongest systems are likely to be collaborative rather than fully automated, pairing rapid machine-generated predictions with meteorologists, disaster managers, local authorities and community organizations that understand conditions on the ground.

AI could ultimately help close the climate-information gap, but only if access and accountability are built into its development from the beginning. Faster forecasts may support earlier evacuations, smarter water management, more resilient agriculture and better preparation for extreme heat. Without equitable infrastructure and public governance, however, the technology could concentrate lifesaving knowledge in the hands of those already best protected from climate hazards. The message of the npj Climate Action analysis is therefore both urgent and practical: the future of AI in weather and climate science will not be judged solely by how accurately it predicts the next storm, but by who receives that prediction, who can trust it and who has the power to act.

Subject of Research: The role of artificial intelligence in weather and climate information, including its potential to improve forecasting and its implications for global inequality.

Article Title: The rise of AI in weather and climate information and its impact on global inequality

Article References: Mozaffari, A., Duarte, A., Teckentrup, L. et al. “The rise of AI in weather and climate information and its impact on global inequality.” npj Climate Action (2026). https://doi.org/10.1038/s44168-026-00412-z

Image Credits: AI Generated

DOI: 10.1038/s44168-026-00412-z

Keywords: Artificial intelligence, weather forecasting, climate information, global inequality, climate change, machine learning, extreme weather, early warning systems, climate justice, meteorology

The traditional approach to weather forecasting has long been characterized by a convoluted process requiring an intricate array of steps, often executed over several hours on specialized supercomputers. This method is not only time-consuming but also necessitates significant human resources, including teams of expert meteorologists and data scientists, to maintain and operate these complex systems. These constraints have limited the scope and accessibility of effective forecasting, especially in regions with fewer technological resources.

Recent collaborative efforts by tech giants such as Huawei, Google, and Microsoft have revealed the potential for integrating machine learning into weather prediction. By substituting portions of the traditional numerical solver—a component that simulates atmospheric changes over time—with artificial intelligence, these companies have been able to produce forecasts more quickly and accurately than previous models. The European Centre for Medium-Range Weather Forecasts has begun to implement this hybrid methodology, marking a step forward in computational meteorology.

However, Aardvark stands out as a complete rethinking of the weather prediction process. Rather than relying on an array of separate models and methods, Aardvark features a unified machine learning model that fundamentally alters the data input-output relationship in meteorology. This model leverages data from satellites, ground-based weather stations, and other sensory inputs, producing localized and global forecasts in mere minutes—operable on standard desktop computers. Such efficiency allows for real-time applications and updates that are indispensable for both daily forecasting and crisis situations.

Initial testing of Aardvark demonstrates its impressive capabilities; with only 10% of the input data utilized by existing systems, it has already begun to surpass the accuracy of the United States’ Global Forecasting System (GFS) on various parameters. The results illustrate that Aardvark is not only competitive with traditional weather forecasts, which draw input from numerous models and require human analysis, but it also demonstrates the potential for a more agile and responsive forecasting environment.

One of the most promising aspects of Aardvark is its inherent adaptability. The model can rapidly learn from various datasets, allowing it to be fine-tuned for specific geographical areas or industries. For instance, it can generate tailored predictions for agricultural planners in Africa, advising on optimal planting conditions, or supply critical wind speed forecasts for renewable energy operations in Europe. This flexibility is a stark contrast to conventional forecasting systems, which necessitate prolonged development periods and extensive collaboration among extensive teams.

The implications of this technology are profound, particularly for developing nations where access to the requisite computational power and meteorological expertise is often lacking. Aardvark’s design indicates a shift towards democratizing weather forecasting, a critical tool for disaster preparedness and resource management that has historically been inaccessible to many. This transition could improve agricultural yields and enhance response strategies for natural disasters across the globe.

Lead researcher Professor Richard Turner from the Alan Turing Institute emphasizes that Aardvark represents a significant re-evaluation of existing methodologies within meteorology. He notes that the project combines speed, cost-effectiveness, adaptability, and accuracy in a manner that could reshape how forecasts are generated and utilized, especially in underserved areas. The underlying technology is rooted in decades of prior development in physical models, underscoring the collaboration between traditional meteorology and modern computational techniques.

Dr. Anna Allen, the study’s lead author from the University of Cambridge, articulates that the success of Aardvark is merely the beginning. This end-to-end data-driven approach could be extended to address other urgent meteorological challenges, such as anticipating hurricanes, managing wildfire risks, and predicting tornado occurrences. Beyond weather-specific applications, the AI model’s potential could extend to monitoring air quality, analyzing ocean dynamics, and even forecasting changes in sea ice, illustrating its broad utility in environmental science.

Matthew Chantry, the Strategic Lead for Machine Learning at the ECMWF, reaffirms the collaborative spirit of this initiative, expressing enthusiasm about the exploration of next-generation weather forecasting systems. His insights highlight the importance of paving the way for operational AI-driven forecasts while promoting data sharing practices that empower both scientific inquiry and public service.

Dr. Chris Bishop from Microsoft Research echoes this sentiment, praising Aardvark as a noteworthy achievement in the realm of AI-enhanced weather prediction. He underscores the collaborative effort behind this innovation, which brings together academia and industry to harness AI technology for widespread benefit. This partnership signifies a collective stride towards addressing technological hurdles while leveraging new opportunities presented by advances in machine learning.

In summation, Aardvark Weather introduces an era where weather forecasting is not only faster and more precise but also accessible to a broader spectrum of users, including those in geographically or economically disadvantaged areas. The transition from relying on supercomputers to utilizing everyday computing devices symbolizes a paradigm shift in meteorological practice.

As research progresses and further iterations of Aardvark are developed, the potential for this technology to positively impact global weather prediction practices, especially in critical situations requiring timely and accurate forecasts, cannot be overstated. This work advocates for a future where forecasting is seamless, sophisticated, and inclusive—characteristics essential for our increasingly interconnected world.

Subject of Research: End-to-end data-driven weather prediction
Article Title: Aardvark Weather: Revolutionizing Meteorological Predictions with AI
News Publication Date: 20-Mar-2025
Web References: Nature DOI: 10.1038/s41586-025-08897-0
References: Allen, A., et al. 2025. ‘End-to-end data-driven weather prediction’, Nature, DOI: 10.1038/s41586-025-08897-0
Image Credits: Not applicable

Keywords

Weather forecasting, AI technology, machine learning, meteorology, computational power.

At the heart of the AIFS is a potent fusion of vast observational datasets and advanced machine learning techniques. Each day, the AIFS processes around 800 million observations derived from over 100 diverse sources, including satellites, aircraft, marine vessels, and various terrestrial sensors. From this immense collection of data, roughly 60 million high-quality observations are extracted, refined, and incorporated into the forecasting models. This thorough selection process establishes the initial conditions utilized by the Integrated Forecasting System (IFS)—the cornerstone of ECMWF’s weather forecasting methods.

The potential applications of the AIFS are vast and diverse. It will not only improve the accuracy of standard meteorological parameters such as temperature and wind, but it will also provide nuanced insights into precipitation variations, covering everything from light rain to heavy snowfall. This remarkable granularity in data will serve a wide range of user communities, from meteorological agencies to industries dependent on accurate weather forecasts, particularly in sectors such as agriculture and renewable energy, where operational outcomes hinge on precise weather data.

Dr. Florence Rabier, Director-General of ECMWF, heralded the AIFS as a transformative moment in the field of meteorology. Through combining conventional meteorological approaches with the efficiency of AI, this new model stands to revolutionize how weather science interprets data, forecasts impending weather patterns, and equips decision-makers with timely and relevant information. As the operational functionalities of the AIFS unfold, ECMWF anticipates exploring hybrid models that seamlessly integrate data-driven and physics-based forecasting to enhance accuracy and resilience.

One of the most prominent features of the AIFS is its ability to generate ensemble forecasts, which provide a spectrum of possible weather scenarios rather than a single deterministic outcome. Ensemble modelling is a sophisticated technique that allows meteorologists to understand the range of variability in weather predictions underpinned by slightly different initial conditions. While the inaugural version of AIFS focuses on singular forecasts—known as deterministic forecasts—plans are already in motion to develop ensemble capabilities that will enrich the predictive output further, making it even more relevant for users in varied sectors.

The implications of integrating AI into weather forecasting extend beyond sheer computational efficiency; they also encompass vast reductions in energy consumption traditionally associated with weather prediction models. ECMWF estimates that the AIFS could facilitate predictions with energy usage reduced by approximately 1,000 times compared to standard methods. This radical decrease not only fosters sustainability but also aligns with a broader commitment to reducing carbon footprints across the scientific and technological landscapes.

As ECMWF embarks on this pioneering journey, national weather services across its 35 Member and Co-operating States can expect to see substantial improvements in the precision of their forecasts. By providing them with access to the AIFS, meteorological agencies will be empowered to enhance their operational efficiencies and develop superior strategies for extreme weather preparedness. This shift is particularly crucial in our current climate, where extreme weather events are becoming more common and increasingly severe, necessitating robust predictive capabilities.

The AIFS does not operate in isolation; it is embedded within a robust framework of existing meteorological services, including the traditional Integrated Forecasting System. This rich ecosystem of data, models, and observations ensures that users benefit from an extensive toolkit tailored to meet their specific forecasting needs. By synergizing AIFS with its established capabilities, ECMWF strengthens its role as a pioneer in global weather prediction, ensuring reliability and trust within the meteorological community and beyond.

Operational readiness, though an impressive milestone, signifies just the beginning of the AIFS’s journey. Ongoing improvements, enhancements, and research opportunities are set to enrich the model further over the coming years. As collaboration remains a core focus, ECMWF is keen to engage with the scientific community, stakeholders, and end-users to refine the system based on real-world applications and feedback. This level of interaction is pivotal in the iterative process of model enhancement, ensuring that the AIFS accurately reflects the diverse needs of its user base.

Dr. Florian Pappenberger, Director of Forecasts and Services at ECMWF, emphasized the importance of operational stability and reliability within the AIFS framework. The interplay between ensemble and deterministic forecasting models will allow ECMWF to offer a comprehensive suite of products that align with the needs of diverse stakeholders. By presenting a spectrum of potential outcomes, the AIFS ensures that national meteorological services are equipped to make informed decisions that protect lives and livelihoods in the face of unpredictable weather patterns.

As ECMWF commemorates 50 years of innovation and leadership in the field of meteorology, the introduction of the AIFS signifies a bold step into a future where AI will fundamentally transform how we predict and respond to weather phenomena. The integration of machine learning into established weather models points to a transformational era of forecasting—one that emphasizes accuracy and efficiency while opening avenues for future technological advancements in the field.

In conclusion, the launch of the Artificial Intelligence Forecasting System stands as a testament to ECMWF’s commitment to merging state-of-the-art technology with weather science. This pioneering initiative will not merely enhance forecasting capabilities but will also set a benchmark for future developments in meteorological services. As the AIFS embarks on its operational phase, its eventual impact on global weather forecasting promises to usher in a new standard of excellence, shaping the trajectory of meteorology for decades to come.

Subject of Research:
Article Title: Artificial Intelligence Forecasting Revolutionizes Weather Predictions
News Publication Date: 25 February 2025
Web References: ECMWF Website
References: Not applicable
Image Credits: Credit: ECMWF

Keywords