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

Müller, Andréas; Deconinck, Willem; Kühnlein, Christian; Mengaldo, Gianmarco; Lange, Michael; Wedi, Nils; Bauer, Péter; Smolarkiewicz, Piotr K.; Diamantakis, Michail; Lock, Sarah‐Jane; Hamrud, Mats; Saarinen, Sami; Mozdzynski, George; Thiemert, Daniel; Glinton, Michael; Bénard, Pierre; Voitus, Fabrice; Colavolpe, Charles; Marguinaud, Philippe; Zheng, Yongping; Bever, J. Van; Degrauwe, Daan; Smet, Geert; Termonia, Piet; Nielsen, Kristian Pagh; Sass, Bent Hansen; Poulsen, Jacob Weismann; Berg, Peter; Osuna, C.; Fuhrer, Oliver; Clément, Valentin; Baldauf, Michael; Gillard, Mike; Szmelter, Joanna; O'Brien, Enda; McKinstry, Alastair; Robinson, Oisín; Shukla, Parijat; Lysaght, Michael J.; Kulczewski, Michal; Ciznicki, Milosz; Pia̧tek, Wojciech; Ciesielski, Sebastian; Błażewicz, Marek; Kurowski, Krzysztof; Procyk, Marcin; Spychala, Pawel; Bosak, Bartosz; Piotrowski, Zbigniew; Wyszogrodzki, Andrzej A.; Raffin, Erwan; Mazauric, Cyril; Guibert, David; Douriez, Louis; Vigouroux, Xavier; Gray, Alan; Messmer, Peter; Macfaden, Alexander J.; New, Nick

doi:10.5194/gmd-2018-304

Cited by 7 publications

(8 citation statements)

References 27 publications

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“…In performing the 1.4 km simulation, the focus was placed on the efficient use of CPUs on Summit. A GPU acceleration of the spectral transforms that has already shown great promise (Müller et al, 2019) was not yet available for the simulations presented here. With the use of GPUs in the future, we anticipate to further reduce the cost of transforms and therefore to increase the throughput of the 1.4 km simulations.…”

Section: Discussionmentioning

confidence: 99%

A Baseline for Global Weather and Climate Simulations at 1 km Resolution

Wedi

Polichtchouk

Dueben

et al. 2020

J Adv Model Earth Syst

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In an attempt to advance the understanding of the Earth's weather and climate by representing deep convection explicitly, we present a global, four-month simulation (November 2018 to February 2019) with ECMWF's hydrostatic Integrated Forecasting System (IFS) at an average grid spacing of 1.4 km. The impact of explicitly simulating deep convection on the atmospheric circulation and its variability is assessed by comparing the 1.4 km simulation to the equivalent well-tested and calibrated global simulations at 9 km grid spacing with and without parametrized deep convection. The explicit simulation of deep convection at 1.4 km results in a realistic large-scale circulation, better representation of convective storm activity, and stronger convective gravity wave activity when compared to the 9 km simulation with parametrized deep convection. Comparison of the 1.4 km simulation to the 9 km simulation without parametrized deep convection shows that switching off deep convection parametrization at a too coarse resolution (i.e., 9 km) generates too strong convective gravity waves. Based on the limited statistics available, improvements to the Madden-Julian Oscillation or tropical precipitation are not observed at 1.4 km, suggesting that other Earth system model components and/or their interaction are important for an accurate representation of these processes and may well need adjusting at deep convection resolving resolutions. Overall, the good agreement of the 1.4 km simulation with the 9 km simulation with parametrized deep convection is remarkable, despite one of the most fundamental parametrizations being turned off at 1.4 km resolution and despite no adjustments being made to the remaining parametrizations. Plain Language Summary We present the world's first global simulation of an entire season of the Earth's atmosphere with 1.4 km average grid spacing and the top of the modeled atmosphere as high as 80 km. Albeit only a single realization due to its considerable computational cost, the resulting model output provides a reference and guidance for future simulations. For illustration we compare to simulations at 9 km grid spacing that represent the state of the art in numerical weather prediction and are still considerably finer when compared to models that are used for climate projections today. Thanks to its unprecedented detail, the simulation output will support future model development and satellite mission planning and may be seen as a prototype contribution to a future digital twin of our Earth.

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Section: Discussionmentioning

confidence: 99%

A Baseline for Global Weather and Climate Simulations at 1 km Resolution

Wedi

Polichtchouk

Dueben

et al. 2020

J Adv Model Earth Syst

Self Cite

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“…However, the computational and data access patterns featured by Lagrangian schemes may not map nicely onto the emerging hardware architectures which will empower the next generation of supercomputers (Mengaldo et al., 2019). This has triggered a concerted shift toward Eulerian methods based on local stencil computations (e.g., Bauer et al., 2020; Müller et al., 2019). Hereinafter we sketch a prototype problem which is representative of both Lagrangian and Eulerian approaches.…”

Section: Canonical Modelmentioning

confidence: 99%

A Numerical Analysis of Six Physics‐Dynamics Coupling Schemes for Atmospheric Models

Ubbiali

Schär

Schlemmer

et al. 2021

J Adv Model Earth Syst

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Atmospheric motions span a wide spectrum of spatial and temporal scales. To cover the full range efficiently, weather and climate models consist of two main parts: the dynamical core, which integrates the governing fluid-dynamics equations on a computational grid and so captures the resolved flow; and the parameterizations, which account for the subgrid-scale phenomena. This latter class of phenomena include both unresolved dynamical features (e.g., turbulence), physical processes (e.g., radiation), and in some models chemical processes. The suite of parameterizations is referred to as the physics of a model, as opposed to the resolved dynamics. Hence the expression physics-dynamics coupling to signify the procedure molding the dynamical core and the physical parameterizations into a cohesive model.

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“…In the simulation of complex multi-scale flows arising in weather and climate (W&C), 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 [1]. One way to tackle this challenge is to use reduced-precision arithmetic in W&C models.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows

Ackmann¹,

Dueben²,

Palmer³

et al. 2021

Preprint

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Semi-implicit grid-point models for the atmosphere and the ocean require linear solvers that are working efficiently on modern supercomputers. The huge advantage of the semi-implicit time-stepping approach is that it enables large model time-steps. This however comes at the cost of having to solve a computationally demanding linear problem each model time-step to obtain an update to the model&#8217;s pressure/fluid-thickness field. In this study, we investigate whether machine learning approaches can be used to increase the efficiency of the linear solver.Our machine learning approach aims at replacing a key component of the linear solver&#8212;the preconditioner. In the preconditioner an approximate matrix inversion is performed whose quality largely defines the linear solver&#8217;s performance. Embedding the machine-learning method within the framework of a linear solver circumvents potential robustness issues that machine learning approaches are often criticized for, as the linear solver ensures that a sufficient, pre-set level of accuracy is reached. The approach does not require prior availability of a conventional preconditioner and is highly flexible regarding complexity and machine learning design choices.Several machine learning methods of different complexity from simple linear regression to deep feed-forward neural networks are used to learn the optimal preconditioner for a shallow-water model with semi-implicit time-stepping. The shallow-water model is specifically designed to be conceptually similar to more complex atmosphere models. The machine-learning preconditioner is competitive with a conventional preconditioner and provides good results even if it is used outside of the dynamical range of the training dataset.

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

Cited by 7 publications

References 27 publications

A Baseline for Global Weather and Climate Simulations at 1 km Resolution

A Baseline for Global Weather and Climate Simulations at 1 km Resolution

A Numerical Analysis of Six Physics‐Dynamics Coupling Schemes for Atmospheric Models

Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows

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