The world is changing, and so are the risks. Floods, droughts, and heatwaves are now regular, making old risk models obsolete. Actuaries are becoming climate strategists , integrating satellite data, AI, and geospatial analytics to quantify exposure in real-time. Adapt your models today. #FutureOfRisk #DataAnalytics #Sustainability

Illumia #Research | Weather Forecast AI-based weather models — such as AIFS, Aurora — may face challenges when it comes to predicting Sudden Stratospheric Warming (SSW) events. The reason is quite simple: none of today’s major AI models include data from the upper stratosphere (1-30 hPa), where the stratospheric polar vortex (SPV) is active. They usually stop around 50 hPa. This limitation is generally critical when trying to simulate the behaviour of the SPV and its potential impacts at the ground. This is even more so as we write: ECMWF, amongst aother NWP centres, are already highlighting the potential for an early SSW event at the beginning of December. Since no SSW event has occurred yet during the operational phase of AI weather models, we decided to look back at an historical case — the famous “Beast from the East” event occurred in February 2018. This SSW led to an anomalously long cold period which tightened the electricity and gas markets in Europe. While the SPV breakdown was well predicted by most the weather prediction centres, the forecast of the temperature response at the ground was far more difficult. In the two top maps (→ see GIF), you can see the forecast of near-surface temperature anomalies from our AI model (left) and from the GFS forecast (right), both initialized on February 12th, 2018 (considered as the initial date of the SSW). Interestingly, both models perform well during the first forecast week, but then start to diverge. The bottom left map indicates which if the two models is closer to the observed field (green for our own AI model and red for GFS). The former is significantly better as it manages to simulate the first substantial cold spell (end of February) - while GFS yields a "warm" forecast as the lead time increases, the AI model moves "colder" and remains close to the observations until up to day+12. This analysis, albeit simple, poses interesting questions on the reasons behind a skillful forecast with such boundary conditions. Where does the source of predictability come from for the AI model? With such limited knowledge of the initial state of the SPV is seems unlikely this originates from aloft. And yet the mechanisms in the troposphere seem to be well captured. This is certainly a topic worth studying, from both a research and market standpoint. #WeatherForecasting #Burian #SSW

In the modern era, the accuracy of weather forecasting has significantly improved due to advancements in satellite technology, high-resolution computer models, and the integration of artificial intelligence. Forecasts for short-term weather—up to five days—are now remarkably reliable, with accuracy rates exceeding 90% in many regions. Modern meteorological models can simulate atmospheric conditions with increasing precision, incorporating vast amounts of real-time data from land, sea, and air. However, while short-term forecasts are highly dependable, long-term predictions still face challenges due to the chaotic nature of weather systems and the limits of current computational power. Nonetheless, today's forecasts are more accurate and accessible than at any point in history, playing a critical role in disaster preparedness, agriculture, aviation, and daily life.

🇨🇳 China Launches AI Weather Satellite That Predicts Storms Days Before Formation China’s newest AI-powered meteorological satellite, Fengyun-X, is rewriting how the world forecasts extreme weather. Using deep learning and real-time atmospheric modeling, it can identify storm patterns days before they form, offering unprecedented time for preparation and prevention. This advancement could save thousands of lives yearly by enabling early disaster response and infrastructure protection worldwide.

A very insightful panel discussion lead by Florence Rabier at the opening of the AI for Weather Forecasting event at the WMO extraordinary congress where best practices and interventions on AI in forcasting like tools in the box and Masu could inspire more innovations and AI applicability on a larger scale where forcasters can better save lives and properties with faster and more accurate warnings. It’s great to witness how innovations in AI are reshaping how we forcast with such a fast pace where humans can still have the essential role of verification and validation of the issued warning. #EW4All #WMO75

Could #AI models democratize weather forecasting? Our newly launched Human-Centered Weather Forecasts Initiative (HCF)–led by Pedram Hassanzadeh, Amir Jina and Michael Kremer–is working with governments to harness AI-powered #WeatherForecasts to help low- and middle-income countries adapt to climate change. In a feature article by The Economist, explore how the HCF team coordinated a project that reached 38 million farmers, predicting rainfall 30 days in advance and accurately forecasting a mid-season stall that traditional models missed. Read: https://lnkd.in/gyYV2KMg 

From orbit to Earth, SkyNova’s Weather Vision brings precision forecasting powered by AI and satellite intelligence. Supporting disaster readiness, climate monitoring, and sustainable planning worldwide.

Can Artificial Intelligence really predict floods before they happen? 🤔 The answer is yes — and it’s already saving lives. Using GeoAI (Geospatial Artificial Intelligence), we can transform satellite data from missions like Sentinel-1, Sentinel-2, and MODIS into early-warning insights that help governments, NGOs, and communities act before disaster strikes. Here’s how the process works 👇 🛰️ Satellite Data Collection — Real-time imagery from global sensors 💻 Preprocessing & Indexing — NDWI, DEM & rainfall data fusion 🧠 GeoAI Model (U-Net / DeepLabV3+) — Learns flood patterns from history 🌊 Flood Prediction Map — Visualizes at-risk zones 🚨 Early Warnings — Empowering faster, data-driven response Floods are becoming more frequent, but our tools are becoming smarter. The real question is: 👉 Can we scale these technologies fast enough to protect everyone, everywhere? Let’s make GeoAI part of global climate resilience. 🌍 #GeoAI #FloodMapping #RemoteSensing #ClimateResilience #EarthObservation #GIS #GEE #AIForGood #Sustainability #MachineLearning #ClimateChange #DataScience #DisasterManagement #Python #SatelliteImagery

⏰ Early Warnings, Smarter Responses: The Future of Disaster Alerts 🌪️ In a world where seconds can save lives,  early warning systems (EWS) are evolving to become faster, smarter, and more reliable. The third chapter of our latest report, New Tech For NatCat, highlights how AI and remote sensing are enhancing our ability to detect and respond to natural hazards like tsunamis, dust storms, and explosive storms. From Global Navigation Satellite System (GNSS) ionospheric sounding that tracks tsunami waves in real time to AI-driven dust storm nowcasting, these technologies are redefining what’s possible in disaster management. But it’s not just about speed—it’s about accuracy and trust! The future of early warnings is here, and it’s powered by innovation. Let’s ensure these systems reach every corner of the globe, protecting lives and livelihoods. 🔗 Read Chapter 3 here: https://lnkd.in/gE7XWUGE #EarlyWarningSystems #AI #RemoteSensing #DisasterPreparedness #ClimateChange 

🔥 Wildfire behavior isn’t deterministic; therefore, wildfire modeling shouldn’t be either. Meet SWIM: the Stochastic Wildfire Impact Model built to match the true uncertainty of wildfire behavior, using methods adapted from infectious disease modeling to capture the stochastic nature of wildfire spread. In wildfire response, speed and clarity matter. SWIM delivers both, giving decision-makers clearer, faster intelligence in the moments that matter. What makes SWIM different: • Nationwide coverage at 0.25-acre precision • Real-time, continuously updating forecasts as weather conditions change • Stochastic simulations that integrate wind, temperature, humidity, fuel moisture, and topography • Over 90% accuracy in predicting final fire perimeters (retrospectively validated) • Monetized risk outputs that translate wildfire spread into expected impacts that consider variable land types and structures Why it matters: Utilities, insurers, and public agencies need more than static maps and deterministic predictions. SWIM provides probabilistic, actionable intelligence to improve resource allocation, mitigation efforts, and communication at the moments when decisions matter most. See it in action: Methodology overview: https://lnkd.in/eebicfzH Live forecasts and accuracy dashboard: https://lnkd.in/eueDvy_Q Turn uncertainty into informed decision making. #ATS #DataScience #RiskAnalysis #InformedDecisions #wildfire #utilities #insurtech #resilience #AI #climate #emergencymanagement #decisionanalysis