Advancements In Technology

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

26 January 2026: Key takeaways from this evening's Middle East update by the great AEI-CTP/ISW: Key Takeaways - Russian forces have been increasingly targeting Ukrainian logistics routes and positions in the near rear using mothership unmanned aerial vehicles (UAVs), particularly motherships based on variants of the Orlan and Molniya fixed-wing drones, since at least August 2025. - Russian developers are integrating fiber-optic cables into cheaper drones to scale Russian forces’ ability to conduct drone strikes at farther ranges. Russian forces are trying to scale the production of fiber-optic UAVs to increasingly intercept Ukrainian heavy bomber drones. - Russian developers reportedly introduced fiber-optic FPV UAVs that can function as repeater drones for other strike and reconnaissance UAVs, extending Russian tactical drone ranges to up to 60 kilometers. - Russian forces are pursuing moving targets in the near rear with Shahed (Geran) and Gerbera UAVs with integrated cameras and radio control capabilities. - Russian developers are fielding new countermeasures against Ukrainian drone interceptors, chiefly via newly integrated radio detectors. #ukraine #russia #leadership #linkedintopvoices

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 

First AI took code. Now it’s coming for patents Perplexity just made patent search conversational. No lawyers. No databases. No paywall. 🔥 Natural language patent search 🔥 Semantic links to related inventions 🔥 Side-by-side comparisons + prior art 🔥 Integrated research from papers + blogs If you’re a founder, researcher, or builder: You can now validate whether your “big idea” is already patented before you even write a pitch deck. IP is no longer slow, expensive, or mysterious. The moat just moved from legal paperwork → execution speed. This is the “GitHub Copilot moment” for patent research.

🇺🇦 In Ukraine, created a next-generation acoustic drone detection system that allows mobile fire groups to know the exact second and direction a drone will appear before visual or radar detection‼️The system uses sensor arrays with 7-microphone grids, deployed in fields covering tens to hundreds of square kilometers, using triangulation across dozens of nodes to continuously recalculate trajectory angles in real time. ⛰️ A 70 km² coverage area can be deployed for about $50,000 using ~10 stationary sensor units. ❗️This creates a dense detection corridor that complements radar systems that cost millions and are more failure-prone. Performance claims include near-100% detection with ~3–5% error margin, enabling pre-positioning of mobile strike units 30–40 km ahead of engagement zones.

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 isn’t just transforming how products are built it’s fundamentally changing how companies protect what they build. 🛡️ Modern patent-analysis platforms like Patsnap, LexisNexis, PatentSight - A LexisNexis Company, and PatSeer are now capable of scanning millions of patents, technical filings, and product descriptions to surface similarities and conflicts that traditional review processes often miss. These systems don’t replace legal teams, but they dramatically expand what those teams can see. 🛡️A global electronics manufacturer recently used an AI-enhanced patent analytics tool to review its wireless-charging portfolio. 🛡️ Within days, the system flagged multiple competitor patents with high semantic similarity. Instead of rushing into litigation, the insights shaped smarter decisions from prompting early licensing conversations to strengthening claim language during prosecution and redirecting R&D resources where needed. 🛡️This is the real impact of AI today: 💫Faster and deeper portfolio analysis 💫Earlier detection of potential conflicts 💫More informed offensive and defensive IP strategies 💫Stronger alignment between legal, product, and R&D teams 🛡️As AI uncovers overlaps that previously went unnoticed, we should expect a steady rise in both IP assertions and defensive actions. Not because companies are getting more aggressive but because the information is finally visible. 🛡️We’re witnessing the early phase of a more transparent IP ecosystem, driven by data and accelerated by AI. 🛡️The companies that adapt now will navigate this shift with confidence; those that don’t may find themselves reacting instead of leading.

🎯 #Ukraine is the first country to stop 𝟒𝟎𝟎+ Shahed #drones per night using #drone interceptors. The old model — sh00t down $20k drones with $1M missiles — no longer works. #NATO is studying Ukraine’s model, but has no working equivalent yet. 🛠 Ukraine uses at least 5 types of interceptor drones. Some look like flying wings, others like fins on a missile. The “Sting” by Wild Hornets is manually piloted. Most carry no warhead, they disable by collision Ukraine’s mobile teams use PKM and Browning M2 machine 𝐠𝐮𝐧𝐬 on pickups. These are effective below 300 meters altitude. Russia adapted by flying Shaheds at 400–600 meters, outside small arms range but below radar thresholds. 📶 Interceptor drones have limits. They’re short-range, weather-sensitive, and can’t operate without radar or clear sky. Jets, Patriots, Gepards, and jammers still close most gaps. Machine𝐠𝐮𝐧𝐬 still account for half of downed drones. US Army field tests of a 50kW vehicle-mounted laser in the Middle East showed problems with dust interference, charging, and target lock at range. Real-world drone defense remains limited by environmental factors. 🚀 Western firms are testing their own systems. Anduril sells Roadrunner and Anvil; MARSS has a cruciform drone; Epirus builds the LEONIDAS energy weapon. Ukraine is the only country deploying interceptors in combat. Britain has adapted ASRAAM air-to-air missiles for truck launch. These are short-range, high-speed missiles, now fired from improvised platforms. It’s a low-cost workaround to extend mobile air defense outside fixed systems. Ukrainian #military planning now treats interceptor drones as essential front-line equipment. Unlike missiles, they can be fielded in clusters, recovered after missions (if non-lethal), and adjusted faster than fixed battery systems. #Engineering #Technology

As Russia continues to launch massive waves of Shahed long-range one-way attack drones across Ukraine, the country’s armed forces are turning to innovative tactics to defend its airspace, particularly at night and in poor weather conditions. Ukrainian officials say the use of helicopters equipped with advanced Teledyne FLIR UltraFORCE 350 multi-sensor systems has emerged as one of the most effective solutions to counter these persistent aerial threats. The challenge of intercepting hundreds of drones over Europe’s largest country has been exacerbated by a shortage of air defense missiles, forcing commanders to rely on rapid and practical alternatives. The UltraFORCE 350 system was originally designed for security, reconnaissance, patrol, and search-and-rescue missions. But the realities of Ukraine’s conflict have given it a critical new role. “Teledyne FLIR Defense solutions are proving to be ideally suited for airborne counter-drone applications, as seen recently in Ukraine,” a company spokesperson told Defence Blog. “The precision of our airborne thermal imaging systems means they are versatile enough to support a wide range of missions to detect, track and identify targets under all kinds of conditions.” https://lnkd.in/dVXUYuCJ