In numerical atmosphere models, values of relevant physical parameters are often uncertain by more than 100% and weather forecast skill is significantly reduced after a couple of days. Still, numerical operations are typically calculated in double precision with 15 significant decimal digits. If we reduce numerical precision, we can reduce power consumption and increase computational performance significantly. If savings are reinvested to build larger supercomputers, this would allow an increase in resolution in weather and climate models and might lead to better predictions of future weather and climate.
I will discuss approaches to reduce numerical precision beyond single precision in high performance computing and in particular in weather and climate modelling. I will present results that show that precision can be reduced significantly in atmosphere models and that potential savings can be huge. I will also discuss how rounding errors will impact model dynamics and interact with model uncertainty and predictability.
- Computational Mathematics and Applications Seminar