This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: If you partitioned the entire Earth, from just below the surface to the top of the atmosphere, into 1-kilometer-square chunks, there would be billions of them. Now run each of those cells through a series of complex physics equations, and you can see how the entire Earth system interacts and evolves in detail, one step at a time.
Modeling a decade into the future takes months on the world's best supercomputers, and the simulation still doesn't capture all the physics at play. At Lawrence Livermore National Laboratory (LLNL), researchers are deploying emulators to shorten that timeline and provide faster and more accurate weather predictions, months to years into the future. These predictions would offer strategic national security advantages, from protecting critical infrastructure and preparing for natural disasters to planning military operations.
Atmospheric modeling was the original high-performance computing and big-data problem. The challenge stems from bridging small, local turbulence and clouds to the Earth as a whole. "It takes on the order of a week for a chunk of air to move all the way around the globe, so when you're making a weather forecast, you really need the whole globe in your simulation," said LLNL scientist Peter Caldwell, who leads the U.S.
Department of Energy (DOE) Exascale Earth System Model (E3SM). E3SM can simulate the atmosphere with a resolution of 3 kilometers by solving a series of physics equations to determine what each chunk of air will do next. Instead of performing those computationally expensive calculations, Caldwell and colleagues like LLNL scientist Yunyan Zhang are looking toward emulators, like one they have named STRATA.
"For an emulator, we use machine learning, like a deep neural network, to mimic what the real physics would have done based on data from observations or high-fidelity models," said Zhang. To accomplish that, scientists from LLNL, DOE and NVIDIA train STRATA on the outputs from a model like E3SM. Then they feed in a set of starting conditions and, based on the training, ask it to predict how the atmosphere looks days to months to years later—without ever solving the physics equations.
"If you do a good job, you can't tell the difference between the two," said Caldwell. "But the thing about using the emulator model is that it's orders of magnitude faster." That speed is more than just convenient. It is essential for getting a full picture of the weather, which is so chaotic that even great models can make bad predictions on occasion.
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