Digital twins estimated growth from USD $3.8 billion in 2019 to USD $35.8 billion in 2025
By Simone Ulmer, Contributing writer, ETH Zurich
Continued from the previous news post: Climate Information System for Decision-making and Strategic Planning…
Weather and climate models as a basis
In their paper, the researchers look back on the steady development of weather models since the 1940s, a success story that took place quietly. Meteorologists pioneered, so to speak, simulations of physical processes on the world’s largest computers. As a physicist and computer scientist, CSCS’s Schulthess is therefore convinced that today’s weather and climate models are ideally suited to identify completely new ways for many more scientific disciplines how to use supercomputers efficiently.
In the past, weather and climate modeling used different approaches to simulate the Earth system. Whereas climate models represent a very broad set of physical processes, they typically neglect small-scale processes, which, however, are essential for the more precise weather forecasts that in turn, focus on a smaller number of processes. The digital twin will bring both areas together and enable high-resolution simulations that depict the complex processes of the entire Earth system. But in order to achieve this, the codes of the simulation programs must be adapted to new technologies promising much-enhanced computing power.
With the computers and algorithms available today, the highly complex simulations can hardly be carried out at the planned extremely high resolution of one kilometer because, for decades, code development has stagnated from a computer science perspective. Climate research benefited from being able to gain higher performance by ways of new generations of processors without having to fundamentally change their program. This free performance gain with each new processor generation stopped about 10 years ago. As a result, today’s programs can often only utilize 5 percent of the peak performance of conventional processors (CPU).
For achieving the necessary improvements, the authors emphasize the need for co-design, i.e. developing hardware and algorithms together and simultaneously, as CSCS successfully demonstrated during the last ten years. They suggest paying particular attention to generic data structures, optimized spatial discretization of the grid to be calculated, and optimization of the time step lengths. The scientists further propose to separate the codes for solving the scientific problem from the codes that optimally perform the computation on the respective system architecture. This more flexible program structure would allow a faster and more efficient switch to future architectures.
More in the next news post…
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