Sylvie Malardel

Adaptive Observations: A Feasibility Study

The feasibility of the recently proposed concept of adaptive observations is tested on a typical case of poorly forecast North Atlantic cyclogenesis. Only numerical tools are employed, no special observations. Although based on simulated data, this study addresses both theoretical and practical problems of adaptive observations. In the first stage of this study, the role of the data assimilation processes is neutralized; the correction is done by forcing correct continuous fields within the target area. These experiments prove that it is necessary to correct the projection of the initial errors on the first unstable plane (the first two leading singular vectors) in order to significantly improve the forecast. These results also clearly demonstrate that the quality of the initial conditions on a limited, but quite large, area could be a major factor influencing the forecast quality. In a second stage, the focus is on operational aspects. The correction is done through the assimilation of a discrete set of simulated profiles using a 3DVAR analysis system. This leads to studying the impact of the assimilation scheme and to testing different sampling strategies. These experiments suggest that the concept of adaptive observations shows great promise in situations comparable to the one studied here. But the current assimilation systems, such as 3DVAR, require that all the structure of the target has to be well sampled to have a significant beneficial effect; sampling only the extremum does not suffice.

How does subgrid‐scale parametrization influence nonlinear spectral energy fluxes in global NWP models?

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.

The “Grey Zone” cold air outbreak global model intercomparison: A cross evaluation using large-eddy simulations

A stratocumulus-to-cumulus transition as observed in a cold air outbreak over the North Atlantic Ocean is compared in global climate and numerical weather prediction models and a large-eddy simulation model as part of the Working Group on Numerical Experimentation “Grey Zone” project. The focus of the project is to investigate to what degree current convection and boundary layer parameterizations behave in a scale-adaptive manner in situations where the model resolution approaches the scale of convection. Global model simulations were performed at a wide range of resolutions, with convective parameterizations turned on and off. The models successfully simulate the transition between the observed boundary layer structures, from a well-mixed stratocumulus to a deeper, partly decoupled cumulus boundary layer. There are indications that surface fluxes are generally underestimated. The amount of both cloud liquid water and cloud ice, and likely precipitation, are under-predicted, suggesting deficiencies in the strength of vertical mixing in shear-dominated boundary layers. But also regulation by precipitation and mixed-phase cloud microphysical processes play an important role in the case. With convection parameterizations switched on, the profiles of atmospheric liquid water and cloud ice are essentially resolution-insensitive. This, however, does not imply that convection parameterizations are scale-aware. Even at the highest resolutions considered here, simulations with convective parameterizations do not converge toward the results of convection-off experiments. Convection and boundary layer parameterizations strongly interact, suggesting the need for a unified treatment of convective and turbulent mixing when addressing scale-adaptivity.

Recent progress and review of issues related to Physics Dynamics Coupling in geophysical models

AbstractNumerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid ...Geophysical models of the atmosphere and ocean invariably involve parameterizations. These represent two distinct areas: a) Subgrid processes which the model cannot (yet) resolve, due to its discrete resolution, and b) sources in the equation, due to radiation for example. Hence coupling between these physics parameterizations and the resolved fluid dynamics and also between the dynamics of the different fluids in the system (air and water) is necessary. This coupling is an important aspect of geophysical models. However, often model development is strictly segregated into either physics or dynamics. Hence, this area has many more unanswered questions than in-depth understanding. Furthermore, recent developments in the design of dynamical cores (e.g. significant increase of resolution, move to non-hydrostatic equation sets etc), extended process physics (e.g. prognostic micro physics, 3D turbulence, non-vertical radiation etc) and predicted future changes of the computational infrastructure (e.g. Exascale with its need for task parallelism, data locality and asynchronous time stepping for example) is adding even more complexity and new questions. This paper reviews the state-of-the-art of the physics-dynamics coupling in geophysical models, surveys the analysis techniques, and points out the open questions in this research field.