How Data Improves Hurricane Predictions

Nature has just published Microsoft Research's Aurora, the first foundation #model of the #earth system. Aurora outperforms operational #forecasts in predicting #air quality, #ocean waves, tropical #cyclone tracks and high-resolution weather, all at orders of magnitude lower computational cost. Aurora first learns how to generate forecasts through training on #weather patterns from over one #million hours of data. These data are derived from satellites, radar and weather stations, simulations, and forecasts. The model can then be fine-tuned to perform a variety of specific tasks such as predicting wave height or air quality. When #Typhoon Doksuri hit the Philippines in July 2023, the damage was devastating. As reported in Nature, Aurora accurately predicts Typhoon Doksuri’s landfall in the Philippines using measurements from four days in advance of the event (image below). Official predictions at that time mistakenly placed the storm off the coast of Northern Taiwan. Results like this show how #AI is paving the way toward democratizing high-quality climate and weather prediction.  Learn more here: https://lnkd.in/gNiM5tsQ Try it here: https://lnkd.in/gn9DZsry

The link below is for the SAR data for Hurricane Helene. SAR (Synthetic Aperture Radar) is one of the most advanced methods we have for measuring wind speeds. And yet - it doesn’t do so directly. The cool thing about SAR is it can see through clouds, all the way down to the surface of the ocean. We derive wind speeds from the degree of perturbation that is evident on the ocean surface. There is tons of research over many years that shows that this method works extremely well. At Worldsphere.ai we are building generative AI models that derive wind fields from satellite images. One of the ways that we verify the accuracy of our predictions is using SAR. When it comes to understanding hurricane wind fields, SAR is as close as we can get to the ground truth. At some point there will be enough SAR data available that we will be able to train our models on this. We’ve experimented with this, but we just done have enough SAR examples available. The SAR equipment is on a polar orbiting satellite, which means that it can only capture data every 12 hours or so as it travels around the poles. So the only way to get more SAR images is to get more polar orbiting satellites out there. SAR image site for Helene: https://lnkd.in/dvWXuxED

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