After six years of scientific, technical developments and meteorological validation, the Application of Research to Operations at Mesoscale (AROME-France) convective-scale model became operational at Météo-France at the end of 2008. This paper presents the main characteristics of this new numerical weather prediction system: the nonhydrostatic dynamical model core, detailed moist physics, and the associated three-dimensional variational data assimilation (3D-Var) scheme. Dynamics options settings and variables are explained. The physical parameterizations are depicted as well as their mutual interactions. The scale-specific features of the 3D-Var scheme are shown. The performance of the forecast model is evaluated using objective scores and case studies that highlight its benefits and weaknesses.

For numerical weather prediction models and models resolving deep convection, shallow convective ascents are subgrid processes that are not parameterized by classical local turbulent schemes. The mass flux formulation of convective mixing is now largely accepted as an efficient approach for parameterizing the contribution of larger plumes in convective dry and cloudy boundary layers. We propose a new formulation of the EDMF scheme (for Eddy Diffusivity\Mass Flux) based on a single updraft that improves the representation of dry thermals and shallow convective clouds and conserves a correct representation of stratocumulus in mesoscale models. The definition of entrainment and detrainment in the dry part of the updraft is original, and is specified as proportional to the ratio of buoyancy to vertical velocity. In the cloudy part of the updraft, the classical buoyancy sorting approach is chosen. The main closure of the scheme is based on the mass flux near the surface, which is proportional to the sub-cloud layer convective velocity scale w * . The link with the prognostic grid-scale cloud content and cloud cover and the projection on the nonconservative variables is processed by the cloud scheme. The validation of this new formulation using large-eddy simulations focused on showing the robustness of the scheme to represent three different boundary layer regimes. For dry convective cases, this parameterization enables a correct representation of the countergradient zone where the mass flux part represents the top entrainment (IHOP case). It can also handle the diurnal cycle of boundarylayer cumulus clouds (EUROCS\ARM) and conserve a realistic evolution of stratocumulus (EUROCS\FIRE).

Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.

The paper examines horizontal wind variance (kinetic energy spectra) and available potential energy spectra in simulations conducted with a state-of-the-art global numerical weather prediction (NWP) model: the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts. The formulation of the spectral energy budget of the atmosphere by Augier and Lindborg (2013) is used to illustrate how the nonlinear spectral fluxes differ for a hierarchy of reduced models, adiabatic dynamical core, Held-Suarez dry, and idealized moist aquaplanet simulations, compared to NWP simulations with full complexity. The results identify surface drag and momentum vertical mixing as the key processes for influencing the transfer of energy in a stratified atmosphere. Moreover, the circulation generated by topography plays a significant role in these transfers. Given that subgrid-scale vertical mixing is parametrized, and that the treatment of orography filtering varies vastly between NWP models, the magnitude and scale of the nonlinear interactions can differ substantially from model to model, and depends on the choices made for the physical parametrizations. The need to appropriately parametrize the essential influence of subgrid-scale processes in global NWP and climate simulations has the effect that the physical energy cascade is replaced by a parametrized energy transfer. This explains the seeming failure of the IFS to produce a shallower mesoscale energy spectrum. In contrast, neither the horizontal filtering, typically applied in NWP models to avoid a spectral blocking at the smallest scales, nor implicit numerical dissipation significantly constrain, at sufficiently high resolution, the nonlinear interactions or the dominant slope of the energy spectra at synoptic and mesoscales. Key Points: • Apply the formulation of the spectral energy budget by AL13 to a hierarchy of models • Show the influence of physical parametrizations on the energy cascade • Show the relative insignificance of horizontal filtering to the cascade at the smallest scales Correspondence to: S. Malardel, sylvie.malardel@ecmwf.int Citation: Malardel, S., and N. P. Wedi (2016), How does subgrid-scale parametrisation influence nonlinear spectral energy fluxes in global NWP models?,

Abstract. We present a nonhydrostatic finite-volume global atmospheric model formulation for numerical weather prediction with the Integrated Forecasting System (IFS) at ECMWF and compare it to the established operational spectral-transform formulation. The novel Finite-Volume Module of the IFS (henceforth IFS-FVM) integrates the fully compressible equations using semi-implicit time stepping and non-oscillatory forward-in-time (NFT) Eulerian advection, whereas the spectral-transform IFS solves the hydrostatic primitive equations (optionally the fully compressible equations) using a semi-implicit semi-Lagrangian scheme. The IFS-FVM complements the spectral-transform counterpart by means of the finite-volume discretization with a local low-volume communication footprint, fully conservative and monotone advective transport, all-scale deep-atmosphere fully compressible equations in a generalized height-based vertical coordinate, and flexible horizontal meshes. Nevertheless, both the finite-volume and spectral-transform formulations can share the same quasi-uniform horizontal grid with co-located arrangement of variables, geospherical longitude–latitude coordinates, and physics parameterizations, thereby facilitating their comparison, coexistence, and combination in the IFS. We highlight the advanced semi-implicit NFT finite-volume integration of the fully compressible equations of IFS-FVM considering comprehensive moist-precipitating dynamics with coupling to the IFS cloud parameterization by means of a generic interface. These developments – including a new horizontal–vertical split NFT MPDATA advective transport scheme, variable time stepping, effective preconditioning of the elliptic Helmholtz solver in the semi-implicit scheme, and a computationally efficient implementation of the median-dual finite-volume approach – provide a basis for the efficacy of IFS-FVM and its application in global numerical weather prediction. Here, numerical experiments focus on relevant dry and moist-precipitating baroclinic instability at various resolutions. We show that the presented semi-implicit NFT finite-volume integration scheme on co-located meshes of IFS-FVM can provide highly competitive solution quality and computational performance to the proven semi-implicit semi-Lagrangian integration scheme of the spectral-transform IFS.