Model accuracy under climate change

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

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

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

We know the Earth is getting warmer, but not what it means specifically for different regions. To figure this out, scientists do climate modelling. 🔎 🌍 , Google Research has published groundbreaking advancements in climate prediction using the power of #AI! Typically, researchers use "climate modelling" to understand the regional impacts of climate change, but current approaches have large uncertainty. Introducing NeuralGCM: a new atmospheric model that outperforms existing models by combining AI with physics-based modelling for improved accuracy and efficiency. Here’s why it stands out: ✅ More Accurate Simulations When predicting global temperatures and humidity for 2020, NeuralGCM had 15-50% less error than the state-of-the-art model "X-SHiELD". ✅ Faster Results NeuralGCM is 3,500 times quicker than X-SHiELD. If researchers simulated a year of the Earth's atmosphere with X-SHiELD, it would take 20 days to complete —  whereas NeuralGCM achieves this in just 8 minutes. ✅ Greater Accessibility Google Research has made NeuralGCM openly available on GitHub for non-commercial use, allowing researchers to explore, test ideas, and improve the model’s functionality. The research showcases AI’s ability to help deliver more accurate, efficient, and accessible climate predictions, which is critical to navigating a changing global climate. Read more about the team’s groundbreaking research in   Nature Portfolio’s  latest article! → https://lnkd.in/e-Etb_x4 #AIforClimateAction #Sustainability #AI

Interpretable Physics-Informed Graph Neural Networks For Flood Forecasting -- https://lnkd.in/gS369ty7 <-- shared paper -- https://lnkd.in/gftbrmrA <-- shared GitHub repository -- H/T Mehdi Taghizadeh “Climate change has intensified extreme weather events, with floods causing significant socioeconomic and environmental damage. Accurate flood forecasting is crucial for disaster preparedness and risk mitigation, yet traditional hydrodynamic models, while precise, are computationally prohibitive for real-time applications. Machine learning surrogates, such as graph neural networks (GNNs), improve efficiency but often lack physical consistency and interpretability. This paper introduces HydroGraphNet, a novel physics-informed GNN framework that, for the first time, integrates the Kolmogorov–Arnold Network (KAN) to enhance model interpretability in unstructured mesh-based flood forecasting. The framework embeds mass conservation laws into the loss function, ensuring physically consistent predictions. Additionally, it employs an autoregressive encoder–processor–decoder architecture that captures spatiotemporal flood dynamics while mitigating error accumulation over long forecasting horizons. Validation on flood data from the White River near Muncie, Indiana, demonstrates a 67% reduction in prediction error, near-zero mass balance error, and a 58% improvement in the critical success index for major flood events compared to a baseline GNN model. These results highlight the potential of the proposed framework to advance real-time flood forecasting with improved physical consistency and interpretability. #GIS #spatial #mapping #FloodForecasting #GraphNeuralNetworks #PhysicsInformedAI #Hydrology #ClimateResilience #ScientificMachineLearning #CivilEngineering #HydroGraphNet #PhyiscsNeMo #hydrology #water #flood #flooding #spatialanalysis #spatiotemporal #climatechange #model #modeling #machinelearning #AI #extremeweather #prediction #forecasting #planning #humanimpacts #loss #lossoflife #publicsafety #cost #economics #infrastructure #risk #hazard #disaster #mitigation #preparedness #naturaldisaster #HydroGraphNet #floodforecasting #dynamics #remotesensing #earthobservation