Abstract

An idealized fluid model of convective-scale numerical weather prediction, intended for use in inexpensive data assimilation experiments, is described here and its distinctive dynamics are investigated. The model modifies the rotating shallow water equations to include some simplified dynamics of cumulus convection and associated precipitation, extending and improving the model of Würsch and Craig. Changes to this original model are the removal of ad hoc diffusive terms and the addition of Coriolis rotation terms, leading to a so-called 1.5-dimensional model. Despite the non-trivial modifications to the parent equations, it is shown that this shallow water type model remains hyperbolic in character and can be integrated accordingly using a discontinuous Galerkin finite element method for nonconservative hyperbolic systems of partial differential equations. Combined with methods to ensure well-balancedness and non-negativity, the resulting numerical solver is novel, efficient and robust. Classical numerical experiments in the shallow water theory, such as the Rossby geostrophic adjustment and flow over topography, are reproduced for the standard shallow water model and used to highlight the modified dynamics of the new model. In particular, it exhibits important aspects of convective-scale dynamics relating to the disruption of large-scale balance and is able to simulate other features related to convecting and precipitating weather systems. Our analysis here and preliminary results suggest that the model is well suited for efficiently and robustly investigating data assimilation schemes in an idealized ‘convective-scale’ forecast assimilation framework.

Highlights

  • Numerical weather prediction (NWP) models solve non-linear partial differential equations (PDEs) that describe atmospheric motions on many scales, whilst parameterizing unresolved processes at the smaller scales as a function of the resolved state

  • Variational data assimilation (DA) algorithms have successfully exploited this notion that atmospheric dynamics are close to a balanced state, resulting in analysed states and forecasts that remain likewise close to this balance (Bannister, 2010)

  • We have presented an idealized fluid model, based on the rotating shallow water equations (SWEs) and the model of WC14 for cumulus cloud dynamics, intended for use in inexpensive DA experiments at convective scales

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Summary

Introduction

Numerical weather prediction (NWP) models solve non-linear partial differential equations (PDEs) that describe atmospheric motions on many scales, whilst parameterizing unresolved processes at the smaller scales as a function of the resolved state. In the context of NWP, data assimilation (DA) involves incorporating meteorological observations in the forecast model in a dynamically consistent manner to provide the ‘optimal’ initial condition for a forecast of the future atmospheric state, taking into account errors in both observations and previous forecasts (Kalnay, 2003). Optimality of the initial state is crucial in such a highly non-linear system with limited predictability. Despite the coarse resolution leaving many ‘subgrid’-scale dynamical processes unresolved, there has been a great deal of success in weather forecasting owing mainly to the dominance of large-scale dynamics in meteorology (Cullen, 2006). Variational DA algorithms have successfully exploited this notion that atmospheric dynamics are close to a balanced state (e.g. hydrostatic and semi-/quasi-geostrophic balance), resulting in analysed states and forecasts that remain likewise close to this balance (Bannister, 2010)

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