AI In Environmental Monitoring

Further progress in AI+climate modeling "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model". Building on ACE2 model which uses our spherical Fourier neural operator (SFNO) architecture, this work shows that ACE2 can replicate climate model responses to sea surface temperature perturbations with high fidelity at a fraction of the cost. This accelerates climate sensitivity research and helps us better understand radiative feedbacks in the Earth system. Background: The SFNO architecture was first used in training FourCastNet weather model, whose latest version (v3) has state-of-art probabilistic calibration. AI+Science is not just about blindly applying the standard transformer/CNN "hammer". It is about carefully designing neural architectures that incorporate domain constraints like geometry and multiple scales, while being expressive and easy to train. SFNO accomplishes both: it incorporates multiple scales, and it respects the spherical geometry and this is critical for success in climate modeling. Unlike short-term weather, which requires only a few autoregressive steps for rollout, climate modeling requires long rollouts with thousands or even greater number of time steps. All other AI-based models fail for long-term climate modeling including Pangu and GraphCast which ignore the spherical geometry. Distortions start building up at the poles since the models assume domain is a rectangle, and they lead to catastrophic failures. Structure matters in AI+Science!

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

Another new milestone is broken by AI. Just two years after producing a better short-term forecast model, a recent model has now comprehensively beaten the existing weather models much, much less compute power. 💥 But how its beaten it, and when, is where the interesting part begins. .. A new model from Google's Deepming ('GenCast') has beaten our best models in forecasting weather events. This is a big deal for two reasons: 1. Climate is changing, and bigger storms, and bigger economic events are happening. Accurate and fast weather predictions are crucial to mitigating the economic impact of extreme conditions. Faster, more precise forecasts can help governments, businesses, and communities act sooner—reducing the damage from floods, heatwaves, and other disruptive events. The forecast of weather, specifically winds, is also critical for our use of wind energy, and turbine management. To avoid damaging infrastructure, and optimally switch these on and off for example (Which I'm told is extremely expensive to do regularly). 2. Traditionally, weather prediction relies on numerical weather prediction (NWP) models, which simulate complex atmospheric processes using physical equations. While powerful, NWPs require enormous computational resources and often struggle to deliver high-resolution forecasts quickly enough, particularly for extreme conditions. So importantly, we are not talking about the 'middle' of the distribution, but rather around the edges, and unusual events. GenCast, by contrast, is a diffusion model that generates highly accurate forecasts at lower computational costs and with faster turnaround times. Fourty years of training, it generates many scenarios in parallel, effectively creating distributions of uncertainty. This approach enhances the prediction of severe weather events, allowing for more timely warnings and better preparation. Importantly, and again, the advent of machine learning means we are allowing data and processes to learn patterns (potentially not linked or driven by our understanding of the underlying causal processes). This should make us nervous, because again, 'it just works'. The paper: https://lnkd.in/gavxZvjX (they open sourced the model!) What are your thoughts on the role of AI in tackling climate risks? Bureau of Meteorology Andrew Huang, CFA Well done the team at: Google DeepMind #climate #esg #ai #artificialintelligence #genai Marcos Lopez de Prado

AI has the potential to bring new waves of innovation, social and economic progress on a scale we’ve not seen before - including supercharging scientific progress. This week, Google published NeuralGCM: an openly available tool for fast, accurate climate modelling - critical to a changing global climate. We know that the Earth is getting warmer, but it’s hard to predict what that means for each different region. To figure this out, scientists use climate modelling. But current approaches have large uncertainty, including systematic errors - like forecasting extreme rain that is only half as intense as what scientists actually observe. That’s where NeuralGCM comes in. It combines physics-based modelling and AI to simulate the Earth’s atmosphere - making it faster and more accurate than existing climate models. For scientists exploring how to build better weather and climate models, it should make a huge difference in helping them understand the effects of the climate crisis on our world - and it could also be great for meteorologists making predictions about our daily weather! Interested in learning more? Read all about it here and watch the video below ⬇️ https://lnkd.in/e_bCuAhq 

AI-Powered Weather Forecasting: The GraphCast Innovation In the quest to enhance weather forecasting and provide early warnings for extreme events like hurricanes, artificial intelligence has emerged as a potent tool. Traditional forecasting systems have undoubtedly improved over the years, but AI's ability to swiftly analyze historical data and make predictions is transforming the field. Google #DeepMind's innovative AI tool, #GraphCast, has demonstrated its potential by outperforming conventional models and significantly expediting forecast delivery. Weather forecasts serve a crucial purpose beyond helping us decide our daily attire; they offer a lifeline in anticipating and preparing for severe weather events such as storms, floods, and heatwaves. However, traditional weather forecasting demands immense computational power. It involves processing hundreds of variables across various atmospheric layers worldwide. GraphCast takes a fundamentally different approach. Instead of attempting to model intricate atmospheric processes, it leverages machine learning to analyze extensive historical weather data, including output from the European Centre for Medium-Range Weather Forecasts (ECMRWF) model, to understand the evolution of weather patterns. This AI-driven approach enables it to predict how current conditions are likely to change in the future, with remarkable precision. GraphCast has demonstrated exceptional accuracy, outperforming traditional models on more than 90% of the factors crucial for weather forecasting. Moreover, it produces forecasts in under a minute, utilizing only a fraction of the computing power required by traditional numerical weather prediction (NWP) models. An illustrative example of its success is its prediction of Hurricane Lee's landfall in Canada in September. The AI tool accurately forecasted the storm's path nine days in advance, surpassing the ECMRWF's six-day prediction window. This extended lead time for forecasting can be pivotal in preparing for extreme weather events, potentially saving lives and mitigating property damage. Crucially, AI models like GraphCast do not supplant traditional weather forecasts but complement them. These AI models rely on data generated by traditional approaches, emphasizing the symbiotic relationship between AI and traditional meteorological methods. Despite advances, climate change brings unpredictable weather extremes, challenging AI models with data quality issues. Rising ocean temperatures introduce a previously unseen variable that can accelerate storm intensification — like Hurricane Otis's swift escalation from a tropical storm to a Category 5 hurricane within 24 hours. GraphCast by Google DeepMind represents a significant advancement in weather forecasting. As climate change continues to reshape weather patterns, AI's role in forecasting becomes increasingly crucial in safeguarding communities worldwide. #JordiPlusJavis Note: This is an #AI generated image

🌦️ 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! 🌍

At #COP29 this year, climate adaptation, and the role of technology to support with early warning systems, adaptation and resiliency was high on the agenda. One area Google has been working on for a number of years is using AI to help forecast riverine floods, and I'm excited about our recent expansion: 🌎 Expanding coverage of our AI-powered riverine flood forecasting model to 100 countries (up from 80) in areas where 700m people live (up from 460m). 🔮 An improved flood forecasting model — which builds upon our breakthrough model — that has the same accuracy at a seven-day lead time as the previous model had at five days. 📖 Making our model forecasts available to researchers and partners via an upcoming API and our Google Runoff Reanalysis & Reforecast (GRRR) dataset. 👐 Providing researchers and experts with expanded coverage — based on “virtual gauges” for locations where data is scarce — via an upcoming API, the GRRR dataset, as well a new expert data layer on Flood Hub with close to 250,000 forecast points of our Flood Forecasting model, spread over 150 countries. 🕰️ Making historical datasets of our flood forecasting model available, to help researchers understand and potentially reduce the impact of devastating floods. Check out this blog from Yossi Matias for more information https://lnkd.in/ePYJQ-qN

Clouds block satellites. AI fills in the gaps. And, suddenly, we’re better at predicting typhoons. A new system called PARAN is helping scientists see the ocean more clearly—literally. When clouds get in the way of satellite readings, this AI model steps in to reconstruct sea surface temperature data in real time. That means better insight into how heat moves between the ocean and atmosphere… and way better forecasting of heatwaves, storms, and marine disasters. It’s a smart blend of AI and physics, and a huge leap for climate resilience. Because when you can’t see the problem, you definitely can’t solve it. Science, meet sharp vision. #ClimateTech #AI

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!

𝗔𝗜 𝗳𝗼𝗿 𝗚𝗢𝗢𝗗: 𝗡𝗔𝗦𝗔 𝗮𝗻𝗱 𝗜𝗕𝗠 𝗹𝗮𝘂𝗻𝗰𝗵 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝘄𝗲𝗮𝘁𝗵𝗲𝗿 𝗮𝗻𝗱 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴! 🌍 (𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗴𝗲𝘁 𝗺𝗼𝗿𝗲 𝘀𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁 𝗽𝗹𝗲𝗮𝘀𝗲 𝗮𝗻𝗱 𝗡𝗢𝗧 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗪𝗿𝗮𝗽𝗽𝗲𝗿!) In collaboration with NASA, IBM just launched Prithvi WxC an open-source, general-purpose AI model for weather and climate-related applications. And the truly remarkable part is that this model can run on a desktop computer. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄: ⬇️ → The Prithvi WxC model (2.3-billion parameter) can create six-hour-ahead forecasts as a “zero-shot” skill – meaning it requires no tuning and runs on readily available data. → This AI model is designed to be customized for a variety of weather applications, from predicting local rainfall to tracking hurricanes or improving global climate simulations. → The model was trained using 40 years of NASA’s MERRA-2 data and can now be quickly tuned for specific use cases. And unlike traditional climate models that require massive supercomputers, this one operates on a desktop. Uniqueness lies in the ability to generalize from a small, high-quality sample of weather data to entire global forecasts. → This AI-powered model outperforms traditional numerical weather prediction methods in both accuracy and speed, producing global forecasts up to 10 days in advance within minutes instead of hours. → This model has immense potential for various applications, from downscaling high-resolution climate data to improving hurricane forecasts and capturing gravity waves. It could also help estimate the extent of past floods, forecast hurricanes, and infer the intensity of past wildfires from burn scars. It will be exciting to see what downstream apps, use cases, and potential applications emerge. What’s clear is that this AI foundation model joins a growing family of open-source tools designed to make NASA’s vast collection of satellite, geospatial, and Earth observational data faster and easier to analyze. With decades of observations, NASA holds a wealth of data, but its accessibility has been limited — until recently. This model is a big step toward democratizing data and making it more accessible to all. 𝗔𝗻𝗱 𝘁𝗵𝘀 𝗶𝘀 𝘆𝗲𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗽𝗿𝗼𝗼𝗳 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝘀 𝗼𝗽𝗲𝗻, 𝗱𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱, 𝗮𝗻𝗱 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝘁 𝘁𝗵𝗲 𝗲𝗱𝗴𝗲. 🌍 🔗 Resources: Download the models from the Hugging Face repository: https://lnkd.in/gp2zmkSq Blog post: https://ibm.co/3TDul9a Research paper: https://ibm.co/3TAILXG #AI #ClimateScience #WeatherForecasting #OpenSource #NASA #IBMResearch