19th Workshop on high performance computing in meteorology

This talk will present an overview on the use of machine learning at ECMWF. It will outline ECMWF's Machine Learning Roadmap for the next 5-10 years as well as the MAELSTROM EuroHPC project that will realise a co-design cycle for machine learning applications in weather and climate science. The talk will present challenges for machine learning in Earth system modelling and outline...

Atos Centre Of Excellence R&D team will present their collaboration work with the ECMWF team on exploring machine learning models to emulate gravity wave drag, especifically the parameterisation of non-orographic gravity wave drag.

With the rise in the availability and importance of big data, machine learning approaches in the field of meteorology, including specifically in the alpine regions, have become rapidly more prevalent in the scientific literature. Artificial intelligence is a fast-changing field, with deep learning techniques (with important applications in computer vision) popularized in the last decade....

Exascale storage systems requires a core architecture in software, hardware and application IO protocol that can scale. Parallel filesystems, which feature a client that manages IO in parallel across the server infrastructure is an important component to remove storage backend congestion. i.e. Intelligence in compute, network and storage is needed to enable holistic scaling. Good economics is...

CDI-PIO is the parallel I/O component of the Climate Data Interface (CDI) that is developed and maintained by the Max-Planck-Institute for Meteorology and DKRZ. It is used by ICON, MPIOM, ECHAM, and the Climate Data Operator (CDO) toolkit. The two main I/O paths for output data are writing GRIB files using MPI-IO, and writing NetCDF4 files using HDF5 (which may then also use MPI-IO, or other...

The Distributed Asynchronous Object Storage (DAOS, see http://daos.io) is an open source scale-out storage system designed from the ground up to deliver high bandwidth, low latency, and high I/O operations per second (IOPS) to HPC applications. It enables next-generation data-centric workflows that combine simulation, data analytics, and AI. This talk will first provide an overview of DAOS...

In 2018, NCAR began its efforts to design and procure the successor to its current 5.34-petaflops Cheyenne system. More challenging that past procurements, the effort faced a dynamic landscape in terms of application evolution, including GPU-ready models and machine learning; scientific demands, including convection-permitting Earth system modeling and subseasonal to decadal Earth system...