NVIDIA today at SC24 announced two new NVIDIA NIM microservices that can accelerate climate change modeling simulation results by 500x in NVIDIA Earth-2.
Earth-2 is a digital twin platform for simulating and visualizing weather and climate conditions. The new NIM microservices offer climate technology application providers advanced generative AI-driven capabilities to assist in forecasting extreme weather events.
NVIDIA NIM microservices help accelerate the deployment of foundation models while keeping data secure.
Extreme weather incidents are increasing in frequency, raising concerns over disaster safety and preparedness, and possible financial impacts.
Natural disasters were responsible for roughly $62 billion of insured losses during the first half of this year. That’s about 70% more than the 10-year average, according to a report in Bloomberg.
NVIDIA is releasing the CorrDiff NIM and FourCastNet NIM microservices to help weather technology companies more quickly develop higher-resolution and more accurate predictions. The NIM microservices also deliver leading energy efficiency compared with traditional systems.
NVIDIA CorrDiff is a generative AI model for kilometer-scale super resolution. Its capability to super-resolve typhoons over Taiwan was recently shown at GTC 2024. CorrDiff was trained on the Weather Research and Forecasting (WRF) model’s numerical simulations to generate weather patterns at 12x higher resolution.
High-resolution forecasts capable of visualizing within the fewest kilometers are essential to meteorologists and industries. The insurance and reinsurance industries rely on detailed weather data for assessing risk profiles. But achieving this level of detail using traditional numerical weather prediction models like WRF or High-Resolution Rapid Refresh is often too costly and time-consuming to be practical.
The CorrDiff NIM microservice is 500x faster and 10,000x more energy-efficient than traditional high-resolution numerical weather prediction using CPUs. Also, CorrDiff is now operating at 300x larger scale. It is super-resolving — or increasing the resolution of lower-resolution images or videos — for the entire United States and predicting precipitation events, including snow, ice and hail, with visibility in the kilometers.
Not every use case requires high-resolution forecasts. Some applications benefit more from larger sets of forecasts at coarser resolution.
State-of-the-art numerical models like IFS and GFS are limited to 50 and 20 sets of forecasts, respectively, due to computational constraints.
The FourCastNet NIM microservice, available today, offers global, medium-range coarse forecasts. By using the initial assimilated state from operational weather centers such as European Centre for Medium-Range Weather Forecasts or National Oceanic and Atmospheric Administration, providers can generate forecasts for the next two weeks, 5,000x faster than traditional numerical weather models.
This opens new opportunities for climate tech providers to estimate risks related to extreme weather at a different scale, enabling them to predict the likelihood of low-probability events that current computational pipelines overlook.
Learn more about CorrDiff and FourCastNet NIM microservices on ai.nvidia.com.
]]>Siemens Gamesa Renewable Energy is working with NVIDIA to create physics-informed digital twins of wind farms — groups of wind turbines used to produce electricity.
The company has thousands of turbines around the globe that light up schools, homes, hospitals and factories with clean energy. In total they generate over 100 gigawatts of wind power, enough to power nearly 87 million households annually.
Virtual representations of Siemens Gamesa’s wind farms will be built using NVIDIA Omniverse and Modulus, which together comprise NVIDIA’s digital twin platform for scientific computing.
The platform will help Siemens Gamesa achieve quicker calculations to optimize wind farm layouts, increasing overall production while reducing loads and operating costs.
With the global level of annual wind power installations likely to quadruple between 2020 and 2025, it’s more important than ever to maximize the power produced by each turbine.
The global trillion-dollar renewable energy industry is turning to digital twins, like those of Siemens Gamesa’s wind farms — and one of Earth itself — to further climate research and accelerate the clean energy transition.
And the world’s rapid clean energy technology improvements mean that a dollar spent on wind and solar conversion systems today results in 4x more electricity than a dollar spent on the same systems a decade ago. This has tremendous bottom-line implications for the transition towards a greener Earth.
With NVIDIA Modulus, an AI framework for developing physics-informed machine learning models, and Omniverse, a 3D design collaboration and world simulation platform, researchers can now simulate computational fluid dynamics up to 4,000x faster than traditional methods — and view the simulations at high fidelity.
“The collaboration between Siemens Gamesa and NVIDIA has meant a great step forward in accelerating the computational speed and the deployment speed of our latest algorithms development in such a complex field as computational fluid dynamics,” said Sergio Dominguez, onshore digital portfolio manager at Siemens Gamesa.
Adding a turbine next to another on a farm can change the wind flow and create wake effects — that is, decreases in downstream wind speed — which lead to a reduction in the farm’s production of electricity.
Omniverse digital twins of wind farms will help Siemens Gamesa to accurately simulate the effect that a turbine might have on another when placed in close proximity.
Using NVIDIA Modulus and physics-ML models running on GPUs, researchers can now run computational fluid dynamics simulations orders of magnitude faster than with traditional methods, like those based on Reynolds-averaged Navier-Stokes equations or large eddy simulations, which can take over a month to run, even on a 100-CPU cluster.
This up to 4,000x speedup allows the rapid and accurate simulation of wake effects.
Analyzing and minimizing potential wake effects in real time, while simultaneously optimizing wind farms for a variety of other wind and weather scenarios, require hundreds or thousands of iterations and simulation runs, which were traditionally prohibited by time constraints and costs.
NVIDIA Omniverse and Modulus enable accurate simulations of the complex interactions between the turbines, using high-fidelity and high-resolution models that are based on low-resolution inputs.
Learn more about NVIDIA Omniverse and Modulus at GTC, running through March 24.
Watch NVIDIA founder and CEO Jensen Huang’s GTC keynote address in replay:
Discover how AI is powering the future of clean energy.
]]>