Abstract
AbstractEarth’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.
Highlights
A question of both theoretical and practical importance is how many bits of real information are carried in the hundreds of millions of variables in a weather or climate prediction model
As a motivation to undertake such an activity, we study the impact in a global operational weather prediction model of reducing the precision of real number variables from double to single precision
To assess the model climate and compare against observations, a 4-member ensemble started from initial time shifted by 36 h to mitigate an effect of diurnal cycle was integrated for 13 months
Summary
A question of both theoretical and practical importance is how many bits of real information are carried in the hundreds of millions of variables in a weather or climate prediction model. Related to it is the need of minimal numerical precision of weather models still sufficient to grant a simulation of the desirable quality. As a motivation to undertake such an activity, we study the impact in a global operational weather prediction model of reducing the precision of real number variables from double to single precision.
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