Time‐parallel integration and phase averaging for the nonlinear shallow‐water equations on the sphere

We describe a proof‐of‐concept development and application of a phase‐averaging technique to the nonlinear rotating shallow‐water equations on the sphere, discretised using compatible finite‐element methods. Phase averaging consists of averaging the nonlinearity over phase shifts in the exponential of the linear wave operator. Phase averaging aims to capture the slow dynamics in a solution that is smoother in time (in transformed variables), so that larger timesteps may be taken. We overcome the two key technical challenges that stand in the way of studying the phase averaging and advancing its implementation: (1) we have developed a stable matrix exponential specific to finite elements and (2) we have developed a parallel finite averaging procedure. Following recent studies, we consider finite‐width phase‐averaging windows, since the equations have a finite timescale separation. In our numerical implementation, the averaging integral is replaced by a Riemann sum, where each term can be evaluated in parallel. This creates an opportunity for parallelism in the timestepping method, which we use here to compute our solutions. Here, we focus on the stability and accuracy of the numerical solution. We confirm that there is an optimal averaging window, in agreement with theory. Critically, we observe that the combined time discretisation and averaging error is much smaller than the time discretisation error in a semi‐implicit method applied to the same spatial discretisation. An evaluation of the parallel aspects will follow in later work. We demonstrate a phase‐averaging technique for numerical weather prediction that makes use of parallel computation to enable more accurate solutions at larger timesteps than standard approaches. This is illustrated through numerical simulations of the rotating shallow‐water equations on the sphere. The image shows the potential vorticity for such a simulation after 50 days.