Latest Weather Forecasting Techniques

AI+Weather/Climate We just released a thorough study of problem design in AI+Weather/Climate. As a new field, there has been an urgent need to establish the importance of design components in weather+AI. What matters, by how much, and with what cost. A study that we aim to address. We study the nature of the input, output, time steps, fine-tuning, loss functions, static variables, pertaining, priors, noise injection, robustness, SSL, and many many more fundamental design questions.  University of Cambridge NVIDIA ---- We established our study on models in prior works, from the first work starting this field, ie FourCastNet, to recent models, including SFNO, SwinTransformer, GraphCast, PanguWeather. This is a massive and costly study that we hope helps navigate future of AI+Weather/Climate. PS, this is not a study comparing architectures. ---- In this work, we do not focus on "discretization agnostics" property since many existing methods, excluding the #NeuralOperators ones, do not posses this fundamental property, making their relevance questionable. To address that, we also present a way to advance neural network architectures to #NeuralOperators and boost performance, accuracy, and relevance. ---- Title: Exploring the design space of deep-learning-based weather forecasting systems Paper: https://lnkd.in/gEz3XzYW A joint work with Shoaib Ahmed Siddiqui Jean Kossaifi, Boris, Chris, Jan Kautz , David Krueger And in the end, thanks to Anima Anandkumar for all the insightful feedback. Important note: Shoaib is our stellar PhD student, please make sure you ask him questions.

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

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

Google DeepMind created a Gen AI model to predict extreme heat, and cyclones -- and it's faster and more accurate than traditional prediction models. It's going to be a huge deal as the climate crisis keeps getting worse. The model's called GenCast, and it uses a diffusion model, similar to those in image generation, adapted for Earth's spherical geometry. The model was trained on four decades of weather data from ECMWF's ERA5 archive. It generates 50+ possible weather scenarios, giving probabilistic ensemble forecasts. These forecasts predict daily weather and extreme events like cyclones with high accuracy. GenCast operates faster and more efficiently than traditional systems, needing just 8 minutes per forecast using TPUs. GenCast outperformed ECMWF’s ENS on 97.2% of forecasting targets, especially for extreme heat, wind, and cyclones. Its speed and precision help safeguard lives, improve renewable energy reliability, and support climate resilience. #GenAI #AI

🌦️ GenAI in Weather Forecasting: Decoding Unseen Patterns 🌦️ Imagine a world where weather predictions are so accurate, they can anticipate even the most subtle changes in the atmosphere. This is not science fiction—it's the power of Generative AI (GenAI) in weather forecasting. Why GenAI? 1. Decoding Satellite Images: Traditional weather forecasting relies heavily on interpreting satellite images. GenAI can process these images with unparalleled precision, identifying patterns and anomalies that human eyes might miss. 2. Unseen Patterns: The true strength of GenAI lies in its ability to detect unseen patterns in vast datasets. By analyzing historical and real-time data, it can predict weather events with greater accuracy. How Does It Work? - Data Processing: GenAI processes massive amounts of data from satellites, sensors, and historical records. - Pattern Recognition: It uses advanced algorithms to recognize patterns that indicate specific weather conditions. - Predictive Modeling: The AI generates predictive models that can forecast weather events with higher precision than ever before. The Impact 🌪️ Disaster Preparedness: More accurate predictions mean better preparation for natural disasters, potentially saving lives and reducing economic losses. 🚜 Agricultural Benefits: Farmers can make more informed decisions about planting and harvesting, leading to better yields and more sustainable practices. ✈️ Aviation Safety: Improved forecasts can enhance flight safety and efficiency, reducing delays and optimizing routes. The Future The integration of GenAI in weather forecasting is just the beginning. As technology evolves, we can expect even more refined and accurate predictions, leading to a safer and more efficient world. 🔍 Curious about the future of weather forecasting with GenAI? Let's explore it together! P.S. Have you experienced the benefits of advanced weather forecasting in your field? Share your story below! 🌍

Me and my colleagues at Google DeepMind and Google Research are sharing our latest work on tropical cyclone prediction, now available through a research tool, Weather Lab: https://lnkd.in/dNtjmiYq Over the past 50 years, tropical cyclones, also known as hurricanes or typhoons, have claimed more than 779,000 lives and caused $1.4 trillion in economic losses [WMO]. For the millions of people living in their path, the accuracy of weather forecasting is the most critical line of defense. In an effort to protect lives and property from this threat, we’ve built a powerful new machine learning (ML)-based ensemble weather model, deployed it operationally on Weather Lab, and partnered with experts from the U.S. National Hurricane Center (NHC) who will assess its live predictions alongside their established forecasting tools. The ensemble mean cyclone track of our new model gains about 1.5 days of position error advantage over ECMWF ENS in tests based on NHC protocols. And surprisingly, our model has a lower average intensity error than NOAA’s high-resolution hurricane model, HAFS-A, in more than 60 of the 74 cyclones evaluated in 2023 and 2024 in the East Pacific and North Atlantic basins. We achieved this by building a new kind of ML weather model, FGN [Ferran Alet Puig et al., 2025], which substantially outperforms GenCast on probabilistic metrics, and specialising it for cyclone tracking by training it on a record of nearly 5,000 tropical cyclones from the past 45 years. Most human forecasters do not trust a weather model until its performance is demonstrated in a real-time setting. That’s why we built Weather Lab, available globally, providing access to live and historical visualisations of tropical cyclone predictions from our new ML weather model, with WeatherNext and ECMWF models shown for comparison. We recently enabled live data downloads in CSV and ATCF format for experts to evaluate. This is a powerful new tool in the toolbox, but no single model is perfect. It will remain key that human forecasters evaluate a wide range of both ML and physics-based predictions when issuing public warnings for cyclone threats. And of course, ML weather models continue to depend on the historical and real-time availability of atmospheric analysis datasets produced by physical modelling centres, and the continued quality and coverage of the Earth’s observing system. Tropical cyclones will likely become more destructive over time [IPCC, 2023]. It is crucial we continue improving our monitoring, prediction, and understanding of these complex beasts of physics. Try Weather Lab: https://lnkd.in/dNtjmiYq  Blog post: https://lnkd.in/dkj8cYan  FGN (Alet et al., 2025): https://lnkd.in/dJhP9Kj2  WMO: https://lnkd.in/dPt94VX5 IPCC, 2023: https://lnkd.in/dj5n-Rqg 

NVIDIA just open-sourced a whole family of weather and climate ai models, and I think this is the moment serious #forecasting stops being something only national weather agencies can do. #Earth2 isn't one model, it's a stack. #Atlas does 15-day global forecasts and beats GenCast on benchmarks. #StormScope is the first AI to outperform physics-based systems on storm dynamics. #HealDA spins up initial atmospheric conditions in seconds on a GPU, the kind of step that used to take hours on a supercomputer. #CorrDiff downscales 500x faster while using 10,000x less energy. what gets me excited isn't any one of these though, it's that the whole pipeline is just sitting on #HuggingFace and #GitHub now. running weather ai used to mean physics models, now a small team in any country can fine-tune these and run them on their own hardware. bigger models are interesting, but this feels more important to me, taking something that lived behind a national lab's firewall and just handing it out. Link to Earth-2: https://lnkd.in/exReKuTV #climateAI #remotesensing #foundationmodel #weather #climate

GenCast is out in Nature Magazine! It's the first high res ML ensemble weather forecast which outperforms the operational state of the art. And a few more things have happened since the preprint was first released ⬇️ ⬇️ ⬇️ The paper shows GenCast provided better probabilistic weather forecasts, including better forecast of extreme weather, than the operational gold standard over our year-long evaluation period. This could mean earlier preparation for extreme events, more reliable wind power, and much more. Alongside publication in Nature Magazine, we are making the code and model weights available to the community (incl. a mini version of the model which gives passable results and can run in a free colab). And soon we'll share an archive of model historical and current forecasts. We also recently fine tuned the model to work on operational inputs so that it can be run live. We conducted a retrospective analysis of this model's forecasts of the track Hurricane Milton 🌪️ . GenCast predicted 60-80% probability of landfall in Florida already from 8.5 days before landfall eventually happened - a couple of days before Milton even formed - and more than 90% from 5.75 days before. *caveats on this example* a) individual examples should always be taken with a pinch of salt - we need rigorous evaluation over an extended period, see the cyclone track section of the paper. b) these are track predictions, not intensity predictions. Overall, GenCast marks something of an inflection point in the advance of AI for weather prediction, with SOTA raw forecasts now coming from AI. I think we can expect them to be increasingly incorporated operationally alongside traditional models (and to continue to improve!) Check out the paper: https://lnkd.in/dsasGbNb The code: https://lnkd.in/dHSjfW-3 And the blog: https://shorturl.at/NPvwL Work with a remarkable team: Alvaro Sanchez Gonzalez, Ferran Alet Puig, Tom Andersson, Andrew El-Kadi, Dominic Masters, Timo Ewalds, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Rémi Lam, & Matthew Willson

Thrilled to unveil our latest work: multi-modal machine learning to forecast localized weather! We construct a graph neural network to learn dynamics at point locations, where typical gridded forecasts miss significant variation. Paper: https://lnkd.in/eBmfsJin Weather dataset: https://lnkd.in/ejCG8bKs Code: https://lnkd.in/eQg-JzQJ AI weather models have made huge strides, but most still emulate products like ERA5, which struggle to capture near-surface wind dynamics. The correlation between ERA5 and ground weather station data is low due to topography, buildings, vegetation, and other local factors. In this work, we forecast near-surface wind at localized off-grid locations using a message-passing graph neural network ("MPNN"). The graph is heterogeneous, integrating both global forecasts (ERA5) and historical local weather station data as different nodes. What do we find? First off, ERA5 interpolation performs poorly, failing to capture local wind variations, especially in coastal and inland regions with complex conditions. An MLP trained on historical data at a location performs better than ERA5 interpolation, as it learns from the station's past observations. However, it struggles with longer lead times and lacks the spatial context necessary to capture weather patterns. Meanwhile, our MPNN dramatically improves performance, reducing the error by over 50% compared to the MLP. This is because the MPNN incorporates spatial information through message passing, allowing it to learn local weather dynamics from both station data and global forecasts. Interestingly, adding ERA5 data to the MLP does not improve its performance significantly. The MLP struggles to integrate spatial information from global forecasts, while the MPNN excels, highlighting the importance of combining global and local data. Large improvements in forecast accuracy occur at both coastal and inland locations. Our model shows a 92% reduction in MSE relative to ERA5 interpolation overall. This work showcases the strength of machine learning in combining multi-modal data. By using a graph to integrate global and local weather data, we were able to generate much more accurate localized weather forecasts! Congrats to Qidong Yang and Jonathan Giezendanner for the great work, and thanks to Campbell Watson, Daniel Salles Chevitarese, Johannes Jakubik, Eric Schmitt, Anirban C., Jeremy Vila, Detlef Hohl, and Chris Hill for a wonderful collaboration. Thanks also to our partners at Amazon Web Services (AWS) for providing cloud computing and technical support!

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