MODELLING AND SIMULATION


”This investment is for a 10-year service delivery and includes two substantial increases in supercomputing capacity through two generations of supercomputing implementations”


initiative, from the European Union, which is tasked with developing a high-precision model of the Earth to monitor and simulate both natural and human activity. To create a digital twin of the Earth, an approximately one to three kilometre global grid spacing is required between neighbouring simulation (grid) points. These points represent as many physical processes as possible from first principles to ‘simulate as observed’ and make the digital twin seamlessly interact with other applications and users. To achieve this, vast amounts of natural


core (the numerical algorithm at the heart of its atmospheric model) for the next generation of supercomputers has just marked its 10th anniversary. ‘This is part of a larger programme to


reformulate and redesign our complete weather and climate research and operational/production systems to allow the Met Office and its partners to fully exploit future generations of supercomputers for the benefits of society,’ Petch explained. ‘It covers the atmosphere, land, marine and Earth system modelling capabilities and ranges from observation processing and assimilation, through the modelling components, to verification and visualisation,’ he added. One of the key parts of a weather or


climate model is the dynamical core – the numerical algorithm that solves the equations governing fluid motion. The Met Office’s current dynamical core is known as ENDGame, and it describes the Earth using a latitude-longitude grid. ‘However, it has been known since


the late 2000s that this grid will cause problems on future supercomputers, which will rely on spreading the calculations involved in the simulation over


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ever-increasing numbers of computer processors,’ Petch explained. ‘A programme to reformulate and


redesign the dynamical core at the heart of our weather and climate model is underway (GungHo) together with a programme to design, develop and implement a new model infrastructure with the specific aim of being as agnostic as possible about the supercomputer architectures (LFRic).’ With these solutions and increased


supercomputing capability, the Met Office can ‘make a step change in the level of precision with which it can forecast the impact of severe weather, with city-scale predictions of rainfall, winds and air quality, helping to protect life and property,’ Petch added. By replacing and increasing its


supercomputing capability, the Met Office also hopes to expand its localised climate projections to better inform future climate risk. This includes city-scale projections to ‘enable better investment in infrastructure and adaptation measures to keep people safe,’ said Petch.


Digital Earth Destination Earth (DestinE) is another key


and socio-economic information are required to continuously monitor the health of the planet and support EU policy- making and implementation. From a data management perspective, the challenges are extensive. Nils Wedi, head of the European Centre for Medium- Range Weather Forecasts’ (ECMWF) Earth Modelling Section, said: ‘To put it in perspective, a single simulation will produce 100 to 200TB per day, similar to todays’ entire volume of daily production at the ECMWF.’ Wedi added: ‘We anticipate using the


latest developments on federated data access, such as Polytope datacube access of weather data, and federated data lakes, combined with unsupervised learning and data reduction. It is not anticipated to be able to archive native resolution data for longer periods and beyond certain cut-off times raw data will have to be deleted.’ The Polytope datacube is one example of the new processes and technologies being put in place to help manage this data. It stores meteorological datasets in n-dimensional arrays (or datacubes) so data is returned in an accessible format. The ECMWF is also following a four-


strategy approach to adapt its HPC architectures and develop ‘an accelerator- enabled multi-architecture prediction model,’ according to Wedi. First, ECMWF is introducing the use of single (instead of double) precision accuracy in its forecast algorithms. Second, it uses platform-specific


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