Key Insights from Weather Prediction Models

How AI is changing storm response in the U.S. — technically. Have you experienced it? Extreme weather response is no longer driven by single forecasts. It’s driven by ensembles + AI acceleration + real-time data fusion. Here’s what’s happening under the hood: AI-accelerated Numerical Weather Prediction (NWP) Deep learning models (graph neural nets, transformers) are trained on decades of reanalysis data to approximate full physics-based solvers. Result: • Inference in seconds instead of hours • Enables rapid ensemble generation (hundreds of scenarios, not dozens) This allows forecasters to update storm tracks and intensity continuously, not on fixed cycles. Multi-modal data fusion AI ingests: • Satellite imagery (GOES) • Doppler radar volumes • Ocean buoys & atmospheric soundings • Ground IoT sensors • Historical climatology Models correlate spatial-temporal patterns across modalities — something classical models struggle with at scale. Severe weather nowcasting Computer vision models detect: • Convective initiation • Tornadic signatures • Rapid intensification signals Lead times improve by 30–60 minutes for fast-forming events — which is operationally massive for emergency management. Probabilistic forecasting, not single answers ML-driven ensembles output probability distributions, not deterministic paths: • Flood depth likelihoods • Wind gust exceedance • Ice accumulation risk This feeds directly into risk-based decision systems. Infrastructure impact modeling Utilities combine AI weather outputs with: • Grid topology • Asset age & failure history • Load forecasts This enables pre-storm optimization: • Crew pre-positioning • Targeted grid isolation • Faster restoration paths Operational decision intelligence AI systems now bridge forecast → action: • When to evacuate • Where to stage responders • Which assets fail first This is no longer meteorology alone — it’s real-time systems engineering. Storms are getting more chaotic. Our response is getting more computational. AI doesn’t replace physics. It compresses it into time we can actually use. #AI #WeatherModeling #Nowcasting #ClimateTech #InfrastructureAI #DigitalTwins #ResilienceEngineering #HPC

So excited to share our new Nature paper on GenCast, an ML-based probabilistic weather forecasting model: https://lnkd.in/enzPUFbn It represents a substantial step forward in how we predict weather and assess the risk of extreme events. 🌪️ GenCast uses diffusion to generate multiple 15-day forecast trajectories for the atmosphere. It assigns more accurate probabilities to possible weather scenarios than the SoTA physics-based ensemble system from ECMWF, across a 2019 evaluation period. It’s vital that we ensure these new ML weather systems are safe and reliable. One thing I'm proud of is our range of evaluation experiments: per-grid-cell skill & calibration, spatial structure, renewable energy, extreme cold/heat/wind, and the paths of tropical cyclones (i.e. hurricanes). For example, we created a dataset of simulated wind power data at wind farm sites across the globe, and found that GenCast outperforms ENS by 10–20% up to 4 days ahead. This is promising, because better weather forecasts can reduce renewable energy uncertainty and accelerate decarbonisation. We also compared cyclone tracks from GenCast and ENS with ~100 cyclones observed in 2019. GenCast's ensemble mean cyclone track has a 12-hour position error advantage over ENS out to 4 days, and more actionable track probability fields out to 7 days. Cyclone maximum wind speeds are still generally underestimated (a common problem for ML weather models), but this performance on tracks is really promising. One recent devastating cyclone was Hurricane Milton, which caused >$85 billion in damages. GenCast predicted ~70% probability of landfall in Florida 8.5 days before the hurricane struck (and ~2 days before it even formed). A GenCast ensemble member takes 8 minutes on a TPU chip, versus hours on a supercomputer for physics-based models. This opens up the possibility of large ensembles (eg 1000s of members) which could better estimate risks of extreme events. We don't yet know how much value this will yield over conventional ensemble sizes (~50 members). Like its predecessor (GraphCast), the weights & code of GenCast have been made publicly available: https://lnkd.in/eg78dd7T. We’re looking forward to seeing how the community builds on this! It's been an honour to work on this study led by Ilan Price with such a talented team ✨: Alvaro Sanchez Gonzalez, Ferran Alet Puig, Andrew El-Kadi, Dominic Masters, Timo Ewalds, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Rémi Lam, & Matthew Willson

2026 - starting the year strong 💪 My colleagues at Google Research published a new paper in Science Advances that marks a significant step forward for large-scale precipitation forecasts. We’ve trained our hybrid AI-physics model, NeuralGCM, directly on NASA satellite observations to simulate global precipitation with a 40% average error reduction over land compared to leading climate models in multi-year runs. Precise precipitation forecasting is one of the "holy grails" of climate science—and it’s notoriously difficult because clouds are at smaller scales than traditionally modeled ☁️. Precipitation forecasts are so relevant in multiple scenarios: it's about knowing whether a farmer should plant seeds today or if a city needs to prepare for a 100-year storm. Here is why this development is a game-changer: ☁️ Smarter Tuning (compared to traditional models): Traditional models rely on fixed equations (parameterizations) that are difficult to tune perfectly for every scenario and rarely utilize the vast data available. NeuralGCM uses neural networks that are trained "online"—meaning they learn to work in harmony with the large-scale physics solver. ☁️ Learning Directly from Observations (compared to other hybrid models or ML models): While most AI models learn from "reanalysis" data (a mix of observations and model physics that can carry biases), NeuralGCM is trained directly on NASA satellite data. This allows the model to align its precipitation predictions with the best available record of actual rainfall. ☁️ Capturing Extremes:  NeuralGCM is significantly better at capturing extreme precipitation which traditional models often under-predict. ☁️ Correcting the Clock:  While many models predict peak rain too early in the day , NeuralGCM accurately reproduces the timing of peak precipitation, especially in complex regions like the Amazon. ☁️ Real-World Application:  This isn’t just theoretical. This past summer, a partnership with the University of Chicago and the Indian Ministry of Agriculture used NeuralGCM to provide AI-based monsoon forecasts for 38 million farmers. AI is learning the "parameterizations" of complex small-scale physics (like cloud formation) that have baffled traditional models for decades. A huge congratulations to Janni Yuval, Stephan Hoyer, Dmitrii Kochkov, Ian Langmore, Michael Brenner, Lizzie Dorfman, Olivia Graham, and the entire team for pushing the boundaries of what's possible for our planet’s resilience. Read the full story on the Google Research blog: https://lnkd.in/ga8V5jq8 Paper: https://lnkd.in/g3wfG4q2

AI is completely rewriting the rules of weather forecasting, and this video from NVIDIA is a perfect example of how fast things are moving. In just under 5 minutes, the video demonstrates Earth-2, a platform that allows you to run global weather forecasts in mere seconds using just a few lines of Python. You can seamlessly switch between data sources (like ERA5, GFS, IFS) and even swap out entire AI models (like FourCastNet, GraphCast, or Aurora) with a single line of code. But NVIDIA isn’t alone. We are witnessing an arms race among big tech to solve weather prediction: - Google DeepMind has GraphCast and NeuralGCM, which have already outperformed gold-standard physical models in many metrics. - Microsoft released Aurora, a foundation model trained on over a million hours of data, claiming to be 5000x faster than traditional numerical systems. - IBM & NASA recently open-sourced Prithvi, a "geospatial foundation model" designed not just for weather, but to be fine-tuned for specific climate applications. - Huawei has Pangu-Weather, which famously predicted the path of a typhoon more accurately than traditional methods. Why is this happening? - Compute: Traditional Numerical Weather Prediction (NWP) solves complex physics equations requiring massive supercomputers. AI models, once trained, infer results in seconds on a few GPUs. - Ensemble Forecasting: Because they are so cheap to run, we can generate thousands of scenarios (ensembles) instead of just a few. This is a game changer for predicting low probability extreme weather events. - Data Fusion: These models are proving incredibly good at learning patterns from historical data that pure physics equations might miss. For the geospatial practice, this is a big change. Weather is moving from a static dataset we download to a dynamic capability we run. You no longer need a supercomputer to generate high-resolution forecasts; you just need a GPU and a Python script. We may soon see fine-tuned weather models for specific geospatial use cases like hyper local wind for drones, precise precip for agriculture, or cloud cover for satellite tasking. The latency between data in and forecast out is shrinking to near zero, enabling true real time geospatial intelligence. Have you tried any of these models? What are your thoughts? 🌎 I'm Matt Forrest and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 12k+ others learning from my daily newsletter → forrest.nyc

Weather forecasting is starting to feel… solved After 7 years as a climate scientist, I’ve spent enough time inside physics-based modelling to know when something fundamentally changes. This is one of those moments. Google Deepmind just released a new AI weather model:  𝗪𝗲𝗮𝘁𝗵𝗲𝗿𝗡𝗲𝘅𝘁 𝟮 𝗧𝗵𝗲 𝘀𝗵𝗼𝗿𝘁 𝘃𝗲𝗿𝘀𝗶𝗼𝗻:  • Global forecasts in under a minute on a single TPU  • Hundreds of scenarios from one initial state  • Higher resolution and better skill on 99.9% of variables and lead times  • Stronger performance on extremes and cyclone tracks (validated in the paper) 𝗪𝗵𝗮𝘁'𝘀 𝗺𝗼𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝘁𝗼 𝗺𝗲: The model is only trained on marginals but it still learns the joint structure of the atmosphere. That’s something we thought required full physics. It clearly doesn't anymore. AI is competing with the methods we built entire institutions around. For teams in energy, insurance, agriculture, logistics: this enables use cases that were impossible even 2–3 years ago. Where do you see the biggest gap today between the forecasts we get… and the decisions we need to make?

Can we predict rainfall everywhere on Earth—even where weather radars don’t exist? The research paper (Agrawal et al. 2025) introduces Global MetNet, an operational deep-learning system designed to produce high-resolution global precipitation nowcasts using satellite observations and numerical weather prediction data. Why is this important? Because the global weather observation network is extremely uneven. Today, most advanced short-term rainfall forecasting systems rely heavily on weather radar. But radar coverage is concentrated mainly in North America, Europe, Japan, and a few other regions. Large parts of the Global South—Africa, South America, and parts of Asia—have very sparse radar coverage, which means the quality of short-term precipitation forecasts there is often much lower. The new system aims to close that gap: Global MetNet combines several global data sources: • Geostationary satellite observations • Global Precipitation Measurement (GPM) CORRA datasets • Numerical weather prediction (NWP) model outputs • Radar observations where they are available Using these inputs, the AI model produces global precipitation forecasts up to 12 hours ahead, with a spatial resolution of about 0.05° (~5 km) and a temporal resolution of 15 minutes. Some impressive facts from the study: • The model can generate forecasts in under one minute, making it suitable for real-time operational use. • It shows significantly higher skill than standard hourly forecast products across multiple evaluation metrics such as the Critical Success Index (CSI) and Fractions Skill Score (FSS). • In regions with sparse observations, the system can outperform even high-resolution numerical models used in data-rich regions like the United States. Perhaps the most surprising fact: This research is not just experimental. The system is already running operationally and powering precipitation forecasts seen by millions of users in Google Search. The broader implication is fascinating. Weather forecasting is entering a new era where AI models integrate satellite observations, numerical models, and sparse ground observations to produce global real-time predictions. This could significantly improve early warnings for floods and extreme rainfall in regions where observational infrastructure is limited. And in the context of a warming climate—with more frequent high-impact rainfall events—such tools could become increasingly critical for protecting lives and infrastructure. Paper: https://lnkd.in/dySbu-gM #Meteorology #AI #WeatherForecasting #Nowcasting #ClimateScience #EarthObservation

August 29, 2024 - By Newcastle University, "Scientists have developed new guidance and tools that could significantly improve the prediction of life-threatening flash flooding. ----- With human-induced #climatechange leading to more #extremeweather conditions, the need for accurate #earlywarningsystems is more critical now than ever before. New research by an international team of climate experts shows intense, localised, heavy bursts of #rainfall can be caused by a rapid rise of air through clouds and proves that these rises in air can be forecast. The team have developed a unique, cutting-edge modelling system marking a fundamental change in how we identify and forecast life threatening, short-duration, #extremerainfall. Better prediction of these intense #downpours will help provide crucial time for communities to prepare for extreme #weather which can lead to devastating #flashfloods such as was seen in Boscastle in August 2004 or London in August 2022. Published (https://lnkd.in/ejA7j5fn) in the journal #Weather and #Climate Extremes, the study was led by the Met Office and Newcastle University, with support from the Universidad de Costa Rica, San Jose, Costa Rica and the Adam Mickiewicz University, Poznań, Poland. Improving public safety and preparedness Study lead author, Met Office Principal Fellow, and Visiting Professor at Newcastle University’s School of Engineering, Paul Davies, said: “The new model is aimed at enhancing the UK’s resilience to extreme weather events, which are becoming more frequent and intense due to climate change. This approach addresses the urgent need for improved prediction capabilities and will help both UK and global communities in mitigating the risks associated with increasingly extreme weather events.” Paul added: “In order to understand these extreme rainfall events we have made an exciting discovery: the presence of a three-layered atmospheric structure, consisting of Moist Absolute Unstable Layers sandwiched between a stable upper layer and a near-stable low layer.” The new research focuses on the atmospheric properties of the extreme rainfall environment, with a particular focus on the thermodynamics associated with sub-hourly rainfall production processes. It identifies a distinctive three-layered atmospheric structure crucial to understanding localised downpours. and associated large-scale atmospheric regimes which might enable further-ahead prediction of the occurrence of #extremedownpours and #flashflooding. Study co-author, Hayley Fowler, Professor of #Climate Change Impacts at Newcastle University, added: “I am delighted to help to lead such exciting new research which provides a paradigm shift in thinking about extreme rainfall processes. We will further develop this model into an operational system which can help to deliver on the UN’s call for Early Warnings for All (https://lnkd.in/eY6WChKv), which aims to ensure universal...” Continue reading

Another new milestone is broken by AI. Just two years after producing a better short-term forecast model, a recent model has now comprehensively beaten the existing weather models much, much less compute power. 💥 But how its beaten it, and when, is where the interesting part begins. .. A new model from Google's Deepming ('GenCast') has beaten our best models in forecasting weather events. This is a big deal for two reasons: 1. Climate is changing, and bigger storms, and bigger economic events are happening. Accurate and fast weather predictions are crucial to mitigating the economic impact of extreme conditions. Faster, more precise forecasts can help governments, businesses, and communities act sooner—reducing the damage from floods, heatwaves, and other disruptive events. The forecast of weather, specifically winds, is also critical for our use of wind energy, and turbine management. To avoid damaging infrastructure, and optimally switch these on and off for example (Which I'm told is extremely expensive to do regularly). 2. Traditionally, weather prediction relies on numerical weather prediction (NWP) models, which simulate complex atmospheric processes using physical equations. While powerful, NWPs require enormous computational resources and often struggle to deliver high-resolution forecasts quickly enough, particularly for extreme conditions. So importantly, we are not talking about the 'middle' of the distribution, but rather around the edges, and unusual events. GenCast, by contrast, is a diffusion model that generates highly accurate forecasts at lower computational costs and with faster turnaround times. Fourty years of training, it generates many scenarios in parallel, effectively creating distributions of uncertainty. This approach enhances the prediction of severe weather events, allowing for more timely warnings and better preparation. Importantly, and again, the advent of machine learning means we are allowing data and processes to learn patterns (potentially not linked or driven by our understanding of the underlying causal processes). This should make us nervous, because again, 'it just works'. The paper: https://lnkd.in/gavxZvjX (they open sourced the model!) What are your thoughts on the role of AI in tackling climate risks? Bureau of Meteorology Andrew Huang, CFA Well done the team at: Google DeepMind #climate #esg #ai #artificialintelligence #genai Marcos Lopez de Prado

Google’s AI Stuns Scientists with Unprecedented Hurricane Forecast Accuracy Introduction Google’s DeepMind has achieved a breakthrough in weather prediction, outperforming traditional supercomputer-based models in forecasting this year’s hurricanes. The AI’s stunning accuracy — described as “gobsmacking” — signals a potential revolution in how meteorologists and emergency agencies prepare for severe weather. Key Highlights Record-Breaking Forecast Performance: DeepMind ranked first among 11 leading models evaluated for hurricane prediction. It outperformed even the U.S. National Weather Service’s (NWS) supercomputer forecasts — traditionally regarded as the gold standard. The AI’s performance also exceeded multi-model expert forecasts, which combine outputs from several established prediction systems. AI Efficiency and Speed: Google’s AI can generate high-precision forecasts within minutes on a single computer. By contrast, conventional forecasting models require supercomputers with tens of thousands of processors and hours of processing time. The efficiency gains mean disaster planners could receive earlier, more localized warnings — critical for saving lives and protecting infrastructure. Self-Learning Model Advantage: Unlike static weather systems, DeepMind’s AI learns from its errors, continuously refining future predictions. This capability allows rapid adaptation to changing atmospheric patterns — a key advantage as climate change increases storm volatility. Implications for Public Safety and Science: Faster, more accurate forecasting could transform disaster preparedness worldwide, especially for hurricane-prone regions. The breakthrough suggests a broader future where AI models enhance or replace legacy systems in climate modeling, energy forecasting, and environmental risk analysis. Why It Matters Google’s success marks a turning point for predictive science. If validated by global meteorological agencies, AI-driven forecasting could democratize weather intelligence — offering supercomputer-level precision to governments and communities without the cost or infrastructure burden. It’s a powerful example of AI’s potential to protect lives and reshape how humanity confronts natural disasters. I share daily insights with 31,000+ followers and 11,000+ professional contacts across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

🌊 A common assumption is that sea-level rise and stronger hurricanes will simply lead to higher storm surges along our coasts. New science shows the reality is far more complex and more dangerous. A 2025 study published in Climatic Change (Danso et al.), analysing 20 historical hurricanes under future climate scenarios, reveals five critical insights that matter for coastal risk management and early warning systems: 🔹 Higher sea levels consistently lead to greater average surge heights and a much larger inundated area, pushing water far inland. In combined scenarios, flooded areas increase by up to 400%. 🔹 A 10% increase in hurricane intensity, consistent with upper-end climate projections, significantly raises surge levels and flood extent, even without sea-level rise. 🔹 Storm surge response varies strongly by coastal morphology. Wide, shallow shelves respond differently than steep, narrow ones, reinforcing the need for location-specific risk assessments. 🔹 In low-lying regions, the population affected by storm surge could be more than 25 times higher than in past events, based on today’s population alone. For the World Meteorological Organization, this underscores the urgency of: • strengthening impact-based forecasting • improving coastal and marine observations • advancing Early Warnings for All • translating climate science into actionable services for decision-makers 🌍 In a changing climate, protecting lives and livelihoods depends on understanding not just how high the water rises but how far it reaches. read the article here 👇 https://lnkd.in/enHPehCC