How Advanced Models Improve Disaster Preparedness

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

The Nature Communications article "How to stop being surprised by unprecedented weather" outlines a comprehensive framework to anticipate and manage the risks of extreme, previously unobserved weather events. The article’s central thesis is that surprise should not be the default response to such events—and that science, policy, and disaster planning can work in concert to build resilience. These methods help anticipate extreme weather events beyond what has occurred in the observational record: a. Conventional Statistical Methods - Use historical weather data and extreme value theory to estimate probabilities of rare events. Limitations: Short observational records, underestimation of extremes, and inability to simulate events beyond past climate conditions. b. Past Events and Proxy Data - Extend the view of climate risk through historical documentation, oral history, and paleoclimate proxies (tree rings, sediments, etc.). Benefits: Reveal long-term variability and past extremes that modern records miss. Limitations: Coarse resolution, dating uncertainty, and difficulty aligning with present-day conditions. c. Event-Based Storylines - Construct physically plausible scenarios of specific high-impact events using counterfactuals and modeling. Useful for local decision-making and public engagement. Limitations: Focused on specific events, often non-probabilistic, and dependent on expert input. d. Weather and Climate Model Data Exploration Mine large ensembles of model outputs (e.g., UNSEEN, SMILEs, CORDEX) for unobserved but plausible extremes. Enables exploration of events outside the observational record using physical consistency. Limitations: Computationally intensive, resolution trade-offs, and model biases.

Google DeepMind just unveiled GenCast, an AI forecasting model capable of predicting weather patterns up to 15 days in advance. With severe weather events on the rise, this tool could be life-saving—providing critical lead time for disaster preparedness, agriculture, and energy planning. One researcher described GenCast’s impact as “decades’ worth of improvements in a single year.” Its outpu is impressive: it outperformed a leading forecasting model 97.2% of the time. Why it matters for #sustainability professionals: • Better #weather predictions can inform #climaterisk assessments and #resilience strategies. Historical weather models just don't cut it anymore. • AI models like GenCast demonstrate how data and machine learning can help tackle weather-related challenges, paving the way for #innovation in areas like agricultural optimization and supply chain logistics. • These breakthroughs remind us that the intersection of #AI and sustainability isn’t just about tech—it’s about driving impactful solutions for people and the planet. To accelerate collaboration, DeepMind has made GenCast open source, sharing its code and weights to empower researchers worldwide: ➡️ Announcement - "GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy" https://lnkd.in/eEVB7v6q ➡️ Academic paper in Nature Magazine - "Probabilistic weather forecasting with machine learning" https://lnkd.in/e7zsmXbZ ➡️ GenCast model code on GitHub - https://lnkd.in/en7DikWk and weights on Google Cloud - https://lnkd.in/eunX7BHC