A study of reduced numerical precision to make superparameterization more competitive using a hardware emulator in the OpenIFS model

The use of reduced numerical precision to reduce computing costs for the cloud resolving model of superparameterised simulations of the atmosphere is investigated. An approach to identify the optimal level of precision for many different model components is presented and a detailed analysis of precision is performed. This is non-trivial for a complex model that shows chaotic behaviour such as the cloud resolving model in this paper. results of the reduced precision analysis provide valuable information for the quantification of model uncertainty for individual model components. The precision analysis is also used to identify model parts that are of less importance thus enabling a reduction of model complexity. It is shown that the precision analysis can be used to improve model efficiency for both simulations in double precision and in reduced precision. Model simulations are performed with a superparametrised single-column model version of the OpenIFS model that is forced by observational datasets. A software emulator was used to mimic the use of reduced precision floating point arithmetic in simulations.