Single Precision in Weather Forecasting Models: An Evaluation with the IFS

Abstract

arth?s climate is a nonlinear dynamical system with scale-dependent Lyapunov exponents. As such, an important theoretical question for modeling weather and climate is how much real information is carried in a model?s physical variables as a function of scale and variable type. Answering this question is of crucial practical importance given that the development of weather and climate models is strongly constrained by available supercomputer power. As a starting point for answering this question, the impact of limiting almost all real-number variables in the forecasting mode of ECMWF Integrated Forecast System (IFS) from 64 to 32 bits is investigated. Results for annual integrations and medium-range ensemble forecasts indicate no noticeable reduction in accuracy, and an average gain in computational efficiency by approximately 40%. This study provides the motivation for more scale-selective reductions in numerical precision.