Abstract. Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's Law driving forward rapid specialization of hardware architectures, building simulation codes on a low-level language with hardware specific optimizations is a significant risk. As a solution, we present Pace, an implementation of the nonhydrostatic FV3 dynamical core which is entirely Python-based. In order to achieve high performance on a diverse set of hardware architectures, Pace is written using the GT4Py domain-specific language. We demonstrate that with this approach we can achieve portability and performance, while significantly improving the readability and maintainability of the code as compared to the Fortran reference implementation. We show that Pace can run at scale on leadership-class supercomputers and achieve performance speeds 3.5–4 times faster than the Fortran code on GPU-accelerated supercomputers. Furthermore, we demonstrate how a Python-based simulation code facilitates existing or enables entirely new use-cases and workflows. Pace demonstrates how a high-level language can insulate us from disruptive changes, provide a more productive development environment, and facilitate the integration with new technologies such as machine learning.
Abstract. Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's law driving forward rapid specialization of hardware architectures, building simulation codes on a low-level language with hardware-specific optimizations is a significant risk. As a solution, we present Pace, an implementation of the nonhydrostatic FV3 dynamical core and GFDL cloud microphysics scheme which is entirely Python-based. In order to achieve high performance on a diverse set of hardware architectures, Pace is written using the GT4Py domain-specific language. We demonstrate that with this approach we can achieve portability and performance, while significantly improving the readability and maintainability of the code as compared to the Fortran reference implementation. We show that Pace can run at scale on leadership-class supercomputers and achieve performance speeds 3.5–4 times faster than the Fortran code on GPU-accelerated supercomputers. Furthermore, we demonstrate how a Python-based simulation code facilitates existing or enables entirely new use cases and workflows. Pace demonstrates how a high-level language can insulate us from disruptive changes, provide a more productive development environment, and facilitate the integration with new technologies such as machine learning.
<p>The geomagnetic field has been continuously monitored from low-Earth orbit (LEO) since 1999, complementing ground-based observatory data by providing calibrated scalar and vector measurements with global coverage. The successful three-satellite ESA Swarm constellation is expected to remain in operation up to at least 2025. Further monitoring the field from space with high-precision absolute magnetometry beyond that date is of critical importance for improving our understanding of dynamics of the multiple components of this field, as well as that of the ionospheric environment. Here, we will report on the latest status of the NanoMagSat project, which aims to deploy and operate a new constellation concept of three identical 16U nanosatellites, using two inclined (approximately 60&#176;) and one polar LEO, as well as an innovative payload including an advanced Miniaturized Absolute scalar and self-calibrated vector Magnetometer (MAM) combined with a set of precise star trackers (STR), a compact High-frequency Field Magnetometer (HFM, sharing subsystems with the MAM), a multi-needle Langmuir Probe (m-NLP) and dual frequency GNSS receivers. The data to be produced will at least include 1 Hz absolutely calibrated and oriented magnetic vector field (using the MAM and STR), 2 kHz very low noise magnetic scalar (using the MAM) and vector (using the HFM) field, 2 kHz local electron density (using the m-NLP) as well as precise timing, location and TEC products. In addition to briefly presenting the nanosatellite and constellation concepts, as well as the evolving programmatic status of the mission (which already underwent a consolidation study funded by the ESA Scout programme), this presentation will illustrate through a number of E2E simulations the ability of NanoMagSat to complement and improve on many of the science goals of the Swarm mission at a much lower cost, and to bring innovative science capabilities for ionospheric investigations. NanoMagSat could form the basis of a permanent collaborative constellation of nanosatellites for low-cost long-term monitoring of the geomagnetic field and ionospheric environment from space.</p>
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