Externalizing the lateral-boundary conditions from the dynamic core in semi-implicit semi-Lagrangian models

Research is still undertaken to develop so-called transparent lateral boundary conditions (LBC) for limited-area numerical weather prediction models. In the widely used semi-implicit semi-Lagrangian schemes, this naturally leads to LBC formulations that are intrinsically intertwined with the numerics of the dynamic core. This may have profound consequences for the implementation and the maintenance of future model codes. For instance, scientific development on the dynamics may be hindered by constraints coming from today's choices in the LBC formulation and vice versa. Building further on the work of Aidan McDonald, this paper proposes an approach where (1) the LBCs can be imposed by an extrinsic numerical scheme that is fundamentally different from the one used for the dynamic core in the interior domain and (2) substituting one such LBC scheme for another is possible in a manner that leaves the Helmholtz solver unmodified. It is argued that this concept may provide the necessary frame for formulating transparent boundary conditions in spectral limited-area models. Since this idea touches all aspects of the LBC problem, its feasibility can only be established by a rigorous systematic approach. As a first step, this paper provides promising experimental support in a one-dimensional shallow-water model

Well-posed lateral boundary conditions for spectral semi-implicit semi-Lagrangian schemes : tests in a one-dimensional model

The aim of this paper is to investigate the feasibility of well-posed lateral boundary conditions in a Fourier spectral semi-implicit semi-Lagrangian one-dimensional model. Two aspects are analyzed: (i) the complication of designing well-posed boundary conditions for a spectral semi-implicit scheme and (ii) the implications of such a lateral boundary treatment for the semi-Lagrangian trajectory computations at the lateral boundaries. Straightforwardly imposing boundary conditions in the gridpoint-explicit part of the semi-implicit time-marching scheme leads to numerical instabilities for time steps that are relevant in today's numerical weather prediction applications. It is shown that an iterative scheme is capable of curing these instabilities. This new iterative boundary treatment has been tested in the framework of the one-dimensional shallow-water equations leading to a significant improvement in terms of stability. As far as the semi-Lagrangian part of the time scheme is concerned, the use of a trajectory truncation scheme has been found to be stable in experimental tests, even for large values of the advective Courant number. It is also demonstrated that a well-posed buffer zone can be successfully applied in this spectral context. A promising (but not easily implemented) alternative to these three above-referenced schemes has been tested and is also presented here

Application of Boyd’s periodization and relaxation method in a spectral atmospheric limited-area model, part II : accuracy analysis and detailed study of the operational impact

Spectral limited-area models face a particular challenge at their lateral boundaries: the fields need to be made periodic. Boyd proposed a windowing-based method to improve the periodization and relaxation. In a companion paper, the implementation of this windowing method in the operational semi-implicit semi-Lagrangian spectral HARMONIE system was described and some first reproducibility tests, comparing this method to the old existing one, were presented. The present paper provides an in-depth study of the impact of this method for different configurations of the implementation. This is carried out in three steps in well-controlled experimental setups of increasing complexity. First, different aspects of Boyd’s method are analyzed in an idealized perfect-model test using a representative 1D shallow-water model. Second, the implementation is tested in an adiabatic 3D numerical weather prediction (NWP) model with perfect-model experiments. Finally, the impact of using Boyd’s method in a more operational-like NWP context is investigated as well. The presented tests show that, while the implementation of Boyd’s method is neutral in terms of scores, it is superior to the existing spline method in the case of strong dynamical forcings at the lateral boundaries

Application of Boyd’s periodization and relaxation method in a spectral atmospheric limited-area model, part I: implementation and reproducibility tests

This paper describes the implementation of a proposal of Boyd for the periodization and relaxation of the fields in a full three-dimensional spectral semi-implicit semi-Lagrangian limited-area model structure of an atmospheric modeling system called HARMONIE that is used for numerical weather prediction and regional climate studies. Some first feasibility tests in an operational numerical weather prediction context are presented. They show that, in terms of standard operational forecast scores, Boyd’s windowing-based method provides comparable performance as the old existing spline-based periodization procedure. However, the real improvements of this method should be expected in specific cases of strong dynamical forcings at the lateral boundaries. An extensive demonstration of the superiority of this windowing-based method is provided in an accompanying paper

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements

Étude de la faisabilité des conditions aux limites latérales bien posées et transparentes dans la dynamique des modèles Aladin et Arome

TOULOUSE3-SCD-Bib. electronique (315559904) / SudocSudocFranceF

A non‐spectral Helmholtz solver for numerical weather prediction models with a mass‐based vertical coordinate

Although semi-implicit semi-Lagrangian spectral atmospheric models have been very successful for decades, they are believed to face big challenges in the longer term. Foremost, the spectral method relies heavily on data-rich global communications, which may become problematic on future massively parallel machines. This paper investigates how the Helmholtz problem, as it arises in the dynamical core of a semi-implicit non-hydrostatic numerical weather prediction model with a mass-based vertical coordinate and a constant-coefficient reference state, can be solved efficiently without relying on spectral transforms, by using a multigrid-preconditioned iterative solver instead. In the particular case of a limited-area geometry, the convergence rate of this iterative solver can be determineda priori, which allows us to predict the required number of iterations. This knowledge is especially valuable for an atmospheric model that is used for operational weather forecasting, because it guarantees that the model runtime stays constant from one forecast to another. Thea prioriknowledge of the convergence rate also allows us to choose the parameters of the multigrid preconditioner optimally. Weak scalability experiments show the superior scalability of this solver with respect to a spectral solver