The survey’s central argument is that weather and climate represent an unusually demanding, and unusually revealing, testbed for artificial intelligence. Unlike image recognition or language modelling, atmospheric science confronts machine-learning systems with petabyte-scale spatiotemporal data spread across a rotating sphere, governed by partial differential equations and constrained by global observational and reanalysis archives. On top of that sits a challenge that most mainstream AI benchmarks simply do not have: climate non-stationarity. The statistical properties of the atmosphere are themselves shifting as the planet warms, which means models trained on the past may be silently invalidated by the very future they are asked to predict. The authors argue that this combination of scale, physics and drift makes meteorology a proving ground whose lessons generalize far beyond forecasting.

To organize an enormous and sometimes chaotic literature, the survey classifies the major AI architectures by their physical inductive biases — the built-in assumptions each design makes about the structure of the world. Convolutional neural networks, the workhorses of early deep-learning weather prediction, assume local spatial structure and translation invariance. Graph neural networks treat the atmosphere as an irregular mesh, naturally handling the geometry of the sphere and unstructured computational grids. Transformers bring global attention mechanisms that can capture long-range teleconnections such as the El Niño–Southern Oscillation and the Madden–Julian oscillation, which link weather patterns across entire hemispheres. Generative models, including generative adversarial networks and diffusion-based approaches, address a different problem entirely: producing realistic ensembles and downscaling coarse global fields to fine local detail.

Two unifying mathematical themes run through this taxonomy. The first is geometric deep learning, the program of designing networks whose internal operations respect the symmetries of the underlying space — in this case, rotation and translation on a sphere rather than on a flat plane. The second is operator learning, exemplified by neural operators such as the Fourier neural operator and its spherical variant, which learn mappings between entire function spaces rather than between individual data points. This distinction matters because weather and climate models are fundamentally functions of functions: they map one continuous field of temperature, pressure and wind onto another. Architectures that respect the spherical geometry of the planet and learn operators rather than fixed-resolution maps are, the authors argue, better positioned to generalize across resolutions and physical regimes.

Beyond architecture, the survey gives systematic treatment to three areas it considers underappreciated. Representation learning and foundation models — large networks pre-trained on vast atmospheric archives and then adapted to many downstream tasks — are examined as the emerging backbone of the field. Uncertainty quantification receives extensive attention, covering techniques from Bayesian approaches to ensemble generation, all aimed at answering the question operational forecasters care most about: not just what will happen, but how confident we should be. And causal discovery is framed as a complement to pure prediction, a way of using machine learning not merely to reproduce correlations in reanalysis data but to probe the physical mechanisms connecting them — a distinction that becomes critical when the climate itself is changing.

The paper’s most provocative contribution, however, is its naming and dissection of what the authors call evaluation pathologies in scientific machine learning. The first is the RMSE smoothness bias. Because root mean square error and mean-squared-error training objectives penalize sharp, spatially displaced features more harshly than blurry, averaged ones, models optimized on these metrics are systematically pushed toward smooth, blurred forecasts. A prediction that gets the shape of a storm exactly right but places it a few dozen kilometers off can score worse than a smeared, featureless field that is wrong everywhere but mildly. The practical consequence is that the metrics used to declare AI models superior to numerical weather prediction may be quietly selecting for aesthetically smooth mediocrity while penalizing the crisp, high-impact detail that matters most to forecasters and the public.

The second pathology the authors identify is ERA5 training–evaluation circularity. ERA5, the European Centre for Medium-Range Weather Forecasts’ flagship reanalysis, is the de facto training ground for most AI weather models — but it is also the reference against which those models are scored. A model trained to reproduce ERA5 and then evaluated against ERA5 is, in a meaningful sense, being graded on its own homework. The circularity inflates apparent skill, obscures the reanalysis’s own biases, and makes it difficult to know how models would perform against genuinely independent observations. The third pathology, benchmark overfitting, compounds the problem: as the community iterates on a small set of standard test cases, models increasingly specialize to those cases, and leaderboard gains stop translating into real-world forecasting skill.

The survey does not stop at diagnosis. It frames the field’s open problems as scientific machine learning challenges that extend well beyond meteorology. Distribution shift under a non-stationary climate is the paradigm case: any AI system deployed over years must cope with input statistics that drift, potentially violating the stationarity assumptions baked into training. Physical consistency of learned operators — whether a neural network’s predictions obey conservation laws and dynamical constraints even far from its training distribution — remains unsolved. Sample efficiency in data-sparse regimes, such as the ocean interior, polar regions and the developing world’s observation networks, tests whether foundation-model approaches can transfer knowledge to places with few measurements. And the authors argue for intrinsic interpretability: not post-hoc explanations bolted onto a black box, but models whose internal reasoning is transparent enough for scientists to trust and interrogate, a theme connected to the explainable AI research program the work was partly funded to advance.

Why does this matter now? Because the operational stakes are rising fast. Deep-learning weather prediction systems are already being trialed by major forecasting centers, and skill on benchmarks is being cited as evidence they can replace or supplement physics-based simulation. If the benchmarks reward blur and circularity, the field risks institutionalizing models that look excellent on paper while underperforming on the rare, extreme events — hurricanes, heat waves, flash floods — where forecasts save lives. The survey’s argument is that verification against rare extremes, using metrics such as the fractions skill score and the continuous ranked probability score alongside traditional correlation measures, must become central rather than peripheral to how AI forecasters are judged.

The broader lesson, the authors contend, is that weather and climate offer scientific machine learning a uniquely honest mirror. The domain combines massive data, hard physics, distribution drift and unforgiving operational verification — a combination that strips away the comfortable assumptions of mainstream AI benchmarking. The survey, published open access with funding support from the Austrian Science Fund and the European Union’s Horizon Europe RI-SCALE project, is intended as both a map and a challenge: a structured account of where AI methods for the atmosphere stand today, and a warning that the path forward runs through better evaluation, not just bigger models. If the field heeds it, the same rigor that makes forecasting trustworthy in a changing climate could reshape how machine learning is validated across the sciences.

Subject of Research: Artificial intelligence methods, benchmarking, and scientific machine learning challenges for weather and climate prediction

Article Title: Artificial intelligence for weather and climate: a survey of methods, benchmarks, and scientific machine learning challenges

Article References: Holzinger, A., Fiore, S., Degiacomi, T., Antonio, F., & Müller, H. (2026). Artificial intelligence for weather and climate: a survey of methods, benchmarks, and scientific machine learning challenges. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11690-8

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11690-8

Keywords: artificial intelligence, weather forecasting, climate modeling, machine learning, neural operators, geometric deep learning, uncertainty quantification, benchmark design, distribution shift, spatiotemporal learning, foundation models, scientific machine learning

At the heart of this research lies a fundamental question: Can AI models trained on past weather data accurately predict phenomena that have no prior precedent in recorded history? This becomes particularly crucial when considering gray swan events—disastrous but not entirely unforeseeable weather occurrences such as centennial floods, unprecedented heat waves, and devastating hurricanes. The study, published on May 21, 2025, in the Proceedings of the National Academy of Sciences, rigorously tested the predictive capacity of neural networks for such out-of-distribution weather extremes.

Traditional neural network models rely solely on the vast datasets of past meteorological observations, typically encompassing several decades. By ingesting this historical data, they attempt to forecast future weather scenarios based on detected patterns. While highly efficient under normal conditions, this strategy inherently assumes that future weather will not diverge significantly from the historic record. However, the Earth’s atmosphere is a complex, nonlinear system capable of producing events that transcend existing datasets, meaning that these AI models might be ill-equipped to anticipate the rare but catastrophic extremes.

To concretely investigate this challenge, the research team devised an innovative experimental design focused on tropical cyclones, or hurricanes, as their test subject. They trained a neural network model using decades of atmospheric data but deliberately excluded any hurricanes stronger than Category 2 from its training set. They then input weather conditions conducive to the formation of a Category 5 hurricane, the most extreme classification for tropical cyclones. The neural network consistently underestimated the hurricane’s intensity, capping predictions at Category 2, thus failing to extrapolate beyond the intensity it had previously seen.

Such a failure to forecast extreme, previously unseen events carries grave consequences. False negatives—where a model under-predicts severity—may leave populations unprepared for catastrophic natural disasters, resulting in loss of life, property, and economic stability. In contrast, false positives, while disruptive, generally err on the side of caution. This limitation underscores the pressing need for advancing weather AI research to better handle out-of-distribution events, which are precisely the kinds of extremes most detrimental to society.

This shortcoming stems largely from a critical distinction between AI weather models and traditional physics-based forecasting systems. Conventional weather forecasting relies on numerical models grounded in established principles of atmospheric physics and fluid dynamics. These models numerically solve equations governing air motion, temperature, moisture, and other physical variables over time and space. Although computationally demanding—often requiring supercomputer resources—these approaches inherently incorporate the causal mechanisms of weather phenomena, providing more robust extrapolation capabilities.

In stark contrast, neural networks used for forecasting function primarily as sophisticated pattern recognition machines. Much like text-generation AI such as ChatGPT, they generate predictions by drawing statistical analogies to historical data, without explicit knowledge of the underlying physical laws. While this black-box approach delivers efficient and surprisingly accurate short-term forecasts under typical conditions, it is fundamentally dependent on the breadth and diversity of its training data.

Interestingly, the study revealed a nuanced insight: when the model’s training data included extreme hurricane events but from a different geographical basin, such as the Pacific Ocean instead of the Atlantic, the neural network could generalize better and successfully predict stronger hurricanes in the Atlantic. This indicates that exposure to extreme events, regardless of their specific location, can improve the model’s ability to forecast rare, severe phenomena. Still, without such extreme examples in the training set, the AI systems remain markedly constrained.

Recognizing this systemic limitation, the researchers advocate for a hybrid approach that synergistically combines AI methodologies with physically informed models. By embedding mathematical representations of atmospheric physics within AI frameworks, future weather models could progressively “learn” the governing dynamics of the atmosphere in a way that transcends mere pattern memorization. Such integration promises to enhance the AI’s ability to predict gray swan weather events and possibly other unprecedented climate phenomena.

One promising avenue being pursued is known as active learning. This approach leverages AI to guide traditional physics-based models in generating synthetic but physically plausible scenarios of extreme weather events. These artificially expanded datasets could then be used to train neural networks more effectively, allowing the AI to recognize and respond to weather phenomena beyond what has been historically observed. Active learning emphasizes intelligent data generation rather than passive accumulation, addressing the scarcity of rare-event data that handicaps current AI models.

Moreover, this research exemplifies a broader need within the scientific community to rethink how big data and AI can be ethically and effectively incorporated into critical infrastructure like weather forecasting systems. As climate change escalates the frequency and intensity of extreme weather, predictive tools must evolve to keep pace with novel and unusual events that could have devastating consequences globally.

While no major meteorological service relies exclusively on AI models for weather forecasting today, their use is rapidly expanding. The findings of this study serve as both a cautionary tale and an inspiration. They emphasize that AI in weather forecasting, while impressive, is not an infallible oracle but a powerful tool whose limitations must be understood and addressed. Through continued interdisciplinary innovation spanning computer science, atmospheric physics, and applied mathematics, next-generation forecasting models could someday foresee the unthinkable, offering society a critical edge in preparing for an increasingly volatile climate.

In conclusion, the advancement of AI-based weather forecasting represents a fascinating frontier marked by both promise and challenges. Neural networks excel in day-to-day predictions and dramatically reduce computational costs compared to traditional models, yet they falter when confronted by novel, extreme conditions outside their training data. By integrating physics-informed constraints and deploying smart data generation techniques like active learning, researchers hope to illuminate the path toward AI models capable of anticipating gray swan events. Such breakthroughs could profoundly impact disaster preparedness, public safety, and policy planning, highlighting the vital role of scientific rigor and innovation in harnessing AI’s potential for the common good.

Subject of Research: Not applicable

Article Title: Can AI weather models predict out-of-distribution gray swan tropical cyclones?

News Publication Date: 20-May-2025

Web References:
https://www.pnas.org/doi/10.1073/pnas.2420914122

References:
Sun et al., “Can AI weather models predict out-of-distribution gray swan tropical cyclones?”, Proceedings of the National Academy of Sciences, May 21, 2025.

Keywords:
Geophysics; Artificial neural networks

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

Amidst escalating climate change concerns, extreme weather events are becoming increasingly common. As storms, hurricanes, and heatwaves wreak havoc on communities globally, the urgency for reliable numerical weather predictions (NWPs) has never been more critical. These predictions, delivered through complex mathematical models that simulate atmospheric conditions, serve as vital tools in disaster preparedness. However, the foundational accuracy of NWPs hinges on the availability of vast amounts of data collected daily from diverse sources, including satellites and ground-based weather stations.

The research team’s quest began with a simple yet transformative question: Could drones serve as a viable alternative to traditional weather balloons? The conventional method of launching weather balloons involves significant costs and logistical challenges, limiting meteorological agencies’ ability to obtain frequent observational data. In this context, the researchers investigated whether drones equipped with meteorological sensors could not only replicate traditional data collection methods but do so more efficiently.

Over the course of their study, the researchers employed three different types of drones, demonstrating a range of capabilities in meteorological data collection. Among these, a specialized meteorological hexacopter was embedded with advanced sensors designed to measure atmospheric variables. In contrast, two commercial quadcopters were converted for the task, showcasing the versatility of drone technology in meteorological applications. Each drone was launched daily during the study period, ascending to a maximum altitude of 900 meters, constrained by urban flight regulations.

A significant portion of the study focused on data comparison. By synchronously collecting data with drones and traditional weather balloons through radiosonde launches, the team was able to assess data integrity and usability. They specifically highlighted key parameters such as air temperature, humidity levels, and wind speed. The results were promising, revealing no significant discrepancies between the two data sources, indicating that drones can indeed match—if not exceed—the accuracy of existing meteorological methods while also providing cost savings.

An additional advantage observed during the campaign was the rapid data processing capabilities of drones. The team reported the ability to convert collected meteorological data into a format suited for NWP systems within a mere 30 minutes, allowing for more agile forecasting processes that align closely with operational protocols currently in place. This efficiency not only streamlines real-time data availability but also supports timely decision-making in weather-sensitive environments.

Prof. Inoue expressed optimism about the outcomes, emphasizing that the study provided a clear demonstration that twice-daily drone profiling is not only feasible but also practical from a data quality and application perspective. He underscored the essential need for long-term validation of data collected by these unmanned systems, advocating for a rigorous framework to ensure operational reliability in forecasting scenarios.

The implications of this research are vast and multifaceted. Beyond simply enhancing data collection efficiency, drones hold the potential to expand the network of meteorological observations in regions where traditional systems are scarce or non-existent. This is particularly relevant in remote, polar, or mountainous areas where ground-based weather stations are few, and capturing atmospheric dynamics is critical for understanding local climate variations and risks.

Moreover, the incorporation of drones into meteorological practices empowers local communities, as these systems can be operated without extensive specialized knowledge. By training individuals in these communities, the effort can decentralize meteorological data collection, fostering a globally distributed and dense network of weather observations. This democratization of meteorological data has staggering potential to enhance forecasting accuracy on an international scale, ultimately leading to improved disaster management and response strategies.

The study culminates in a stark realization: integrating modern technology like drones into meteorological methods could profoundly influence future practices. It paves the way for next-generation observational systems that embrace innovation while addressing immediate climate challenges. Through cost-effective and accessible solutions, the research not only positions drones as allies in atmospheric science but also enhances our capability to protect lives from the escalating threats posed by climate change.

As the global community grapples with the implications of climate shifts, research such as this signifies a crucial step towards building resilience. Supporting numerical weather predictions with real-time drone data could one day lead to accurate, immediate forecasts capable of mitigating the impact of extreme weather events. The road ahead is undoubtedly challenging, but the integration of drones into meteorological research heralds a new era of precision and affordability in weather observation.

Subject of Research:
Article Title: Operational Capability of Drone-Based Meteorological Profiling in an Urban Area
News Publication Date: January 16, 2025
Web References:
References:
Image Credits: Prof. Jun Inoue from the National Institute of Polar Research, Japan

Keywords: drone technology, meteorology, numerical weather predictions, climate change, uncrewed aircraft systems, atmospheric data collection, disaster preparedness, weather observation.

Weather forecasting is a complex discipline that relies strongly on data assimilation, a technique used to combine diverse sources of information to produce more accurate and reliable predictions. Traditionally, this method involves considerable computational demands and can lead to longer processing times, ultimately delaying forecast outputs. Romit Maulik points out that the current methods, while useful, face limitations in speed and efficiency, particularly as the volume of atmospheric data continues to grow. Thus, there is a compelling need to innovate and streamline these processes.

One of the promising avenues the research team plans to explore is computer vision, a subset of AI that empowers machines to interpret and understand visual information. This technology leverages machine learning and neural networks to draw insights from complex datasets, enabling computers to recognize patterns and make predictions. By incorporating computer vision into the forecasting process, Maulik and his team believe they can not only enhance the accuracy of atmospheric predictions but also significantly reduce the time required for data assimilation.

The proposed research involves the utilization of satellite images as a primary input source, which offers a wealth of real-time data on atmospheric conditions and ocean surface dynamics. By employing transformer-based AI algorithms and cutting-edge machine learning models, the team aims to refine existing forecasting approaches. This would enable them to create a more agile and responsive system that can adapt to the evolving requirements of weather predictions in real-time.

A crucial aspect of Maulik’s work entails retraining portions of the current forecasting models to effectively integrate these new datasets. The enhanced algorithms will then be incorporated into the NASA Goddard Earth Observing System. This integration represents a significant advancement, as it will enable the operational data assimilation workflows to incorporate satellite observations more rapidly, thereby improving the overall efficiency of the forecasting process.

The collaboration is not limited to Penn State; the research team also includes experts from Argonne National Laboratory, NASA Goddard Space Flight Center, the National Oceanic and Atmospheric Administration, and the University of Chicago. This diverse team brings together a wealth of expertise across various domains, ensuring that the project benefits from a well-rounded approach to solving complex meteorological challenges. By fostering interdisciplinary collaboration, it enhances the potential for groundbreaking discoveries and innovations in weather forecasting.

Maulik and his colleague Steven Greybush, who also serves as an associate professor of meteorology, are additionally co-hires at the Penn State Institute for Computational and Data Sciences. Their collaboration reflects the importance of computational approaches to modern meteorology and highlights the role that universities play in advancing scientific research. This strategic partnership is expected to yield valuable insights and push the boundaries of what is currently achievable in the field of weather forecasting.

This initiative represents a pivotal moment in the field of atmospheric and oceanic science; as our understanding of these systems continues to evolve, so too does our capacity to predict their behaviors. The implications of this research are vast. Enhanced forecasting capabilities can lead to more accurate warnings about severe weather events, ultimately saving lives and mitigating damage from natural disasters. Furthermore, improved models can assist in the management of natural resources and support environmental conservation efforts.

Furthermore, by deploying AI-driven methodologies, researchers aim to produce more granular forecasts that account for localized weather patterns and anomalies. This could significantly benefit sectors that are highly sensitive to weather conditions, such as agriculture, transportation, and energy management. By providing stakeholders with precise and timely information, the research has the potential to enhance decision-making processes across various industries.

As we look forward to the outcomes of this promising project, the integration of artificial intelligence into meteorological practices signals a transformative shift in how we approach weather forecasting. As data continues to proliferate, harnessing advanced computational techniques will be essential to navigate the complexities of our changing climate. The ongoing collaboration between academic institutions and federal agencies exemplifies the commitment to fostering innovation and addressing some of the most pressing challenges related to climate science today.

In conclusion, this research initiative by Penn State and its partners is set to redefine the landscape of weather forecasting, highlighting the pivotal role that technology plays in enhancing our understanding of atmospheric phenomena. By merging computer vision with established forecasting models, the team aims not only to improve the accuracy of predictions but also to address the time-consuming challenges of data assimilation. The potential applications of this research extend far beyond academia, emphasizing the critical importance of timely and accurate weather information in an era of increasing climate uncertainty.

Subject of Research: Improving weather forecasts through AI and satellite data integration
Article Title: Penn State Research Team Receives NASA Grant to Enhance Weather Forecasting with AI
News Publication Date: [Not Provided]
Web References: [Not Provided]
References: [Not Provided]
Image Credits: [Not Provided]

Keywords

Weather forecasting, artificial intelligence, computer vision, data assimilation, meteorology, satellite data, Penn State, NASA, climate science.

Credit: The University of Queensland