Search

Abstract Accurate precipitation simulations for various climate scenarios are critical for understanding and predicting the impacts of climate change. This study employs a Cycle‐generative adversarial network (CycleGAN) to improve global 3‐hr‐average precipitation fields predicted by a coarse grid (200 km) atmospheric model across a range of climates, morphing them to match their statistical properties with those of reference fine‐grid (25 km) simulations. We evaluate its performance on both the target climates and an independent ramped‐SST simulation. The translated precipitation fields remove most of the biases simulated by the coarse‐grid model in the mean precipitation climatology, the cumulative distribution function of 3‐hourly precipitation, and the diurnal cycle of precipitation over land. These results highlight the potential of CycleGAN as a powerful tool for bias correction in climate change simulations, paving the way for more reliable predictions of precipitation patterns across a wide range of climates.

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.

Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a result, researchers who use high-productivity languages for exploratory work often find these codes hard to understand, hard to modify, and hard to integrate with their analysis tools. fv3gfs-wrapper is an open-source Python-wrapped version of the NOAA (National Oceanic and Atmospheric Administration) FV3GFS (Finite-Volume Cubed-Sphere Global Forecast System) global atmospheric model, which is coded in Fortran. The wrapper provides simple interfaces to progress the Fortran main loop and get or set variables used by the Fortran model. These interfaces enable a wide range of use cases such as modifying the behavior of the model, introducing online analysis code, or saving model variables and reading forcings directly to and from cloud storage. Model performance is identical to the fully compiled Fortran model, unless routines to copy the state in and out of the model are used. This copy overhead is well within an acceptable range of performance and could be avoided with modifications to the Fortran source code. The wrapping approach is outlined and can be applied similarly in other Fortran models to enable more productive scientific workflows.

sympl (System for Modelling Planets) and climt (Climate Modelling and Diagnostics Toolkit) are an attempt to rethink climate modelling frameworks from the ground up. The aim is to use expressive data structures available in the scientific Python ecosystem along with best practices in software design to allow scientists to easily and reliably combine model components to represent the climate system at a desired level of complexity and to enable users to fully understand what the model is doing.sympl is a framework which formulates the model in terms of a state that gets evolved forward in time or modified within a specific time by well-defined components. sympl's design facilitates building models that are self-documenting, are highly interoperable, and provide fine-grained control over model components and behaviour. sympl components contain all relevant information about the input they expect and output that they provide. Components are designed to be easily interchanged, even when they rely on different units or array configurations. sympl provides basic functions and objects which could be used in any type of Earth system model.climt is an Earth system modelling toolkit that contains scientific components built using sympl base objects. These include both pure Python components and wrapped Fortran libraries. climt provides functionality requiring model-specific assumptions, such as state initialization and grid configuration. climt's programming interface designed to be easy to use and thus appealing to a wide audience.Model building, configuration and execution are performed through a Python script (or Jupyter Notebook), enabling researchers to build an end-to-end Python-based pipeline along with popular Python data analysis and visualization tools.

This paper discusses in detail an undergraduate student industrial project which forms an integral part the degree of Master of Engineering from the University of Glasgow, Scotland. The host company was Mesit Pristroje spol. s r.o., Uherske Hradiste, Czech Republic co-operating with the Technical University of Brno (VUT Brno), Czech Republic. The project carried out was concerned with a contribution to the design and with the testing and development of a data measurement system designed principally for use in the testing of light aircraft. The system designed in capable of recording static and dynamic measurements up to 1000 Hz, and consists of a piece of hardware interfaced with a personal computer running purposely written software. The paper presents a detailed account of the work carried out by the student together with an explanation of the design of the system. Some possible future developments for the system are also discussed. A description of the European Credit Transfer System (ECTS), used to evaluate the work of a student in a host university or enterprise, is also presented, the project being part of this scheme.