Keynote Speakers
Peter Caldwell is the leader of the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM), which is the US Department of Energy (DOE) global km-scale model. Exploiting technological opportunities is a central focus for DOE, as embodied in SCREAM’s use of C++/Kokkos to enable performance portability on the world’s biggest supercomputers.
Peter's original background is in math and much of his current research spans the boundaries between climate science, numerical analysis, statistics/data science, and computer science. He is also an expert in cloud physics and feedbacks. Peter received his PhD in Atmospheric Sciences in 2007 from the University of Washington and has been at Lawrence Livermore National Lab ever since.
Peter is the Head of the Earth System Modelling Section at the European Centre for Medium Range Weather Forecasts (ECMWF) developing one of the world’s leading global weather forecast models — The Integrated Forecasting System (IFS). He is also a Honorary Professor at the University of Cologne. Before, he was AI and Machine Learning Coordinator at ECMWF and University Research Fellowship of the Royal Society performing research towards the use of machine learning, high-performance computing, and reduced numerical precision in weather and climate simulations. Peter is coordinator of the WeatherGenerator Horizon Europe project that aims to build a machine-learned foundation model for weather and climate applications and has been the coordinator of the MAELSTROM EuroHPC-Joint Undertaking project.
Cathy did her PhD at ETH Zurich where she looked at the predictability of convection. It was the time when limited-area models were started to be used for numerical weather predictions. She did her PostDoc at ETH Zurich, then was a visiting scientist at the Department of Atmospheric Sciences at the University of Washington (Seattle) before moving to the Max Planck Institute for Meteorology.
Her research has always focused on deep convection, but her interests have shifted along the years from weather to climate. One question she is particularly interested in is the role that the surface, being the ocean or the land, plays in setting basic features of the climatological precipitation distribution. Her interests in moist convection explains her strong involvement in the use and development of coupled km-scale Earth System Models. She has been co-leading this development at MPI-M. One key achievement was the production of the first coupled global climate simulation run with a grid spacing of 5 km on seasonal time scales (external page https://doi.org/10.5194/gmd-16-779-2023) and, later on, on decadal time scales.
Stephan Hoyer is a Senior Staff Software Engineer at Google, where he leads the NeuralGCM team, building AI-based weather and climate models. His research spans the intersection of physics, numerical computing and machine learning. Stephan has also made significant contributions to open source libraries for scientific computing in Python, including Xarray, NumPy and JAX. He holds a Ph.D in Physics from the University of California, Berkeley.
Dr. Maria J. Molina is an Assistant Professor within the Department of Atmospheric and Oceanic Science at the University of Maryland, College Park, and is affiliated with the Artificial Intelligence Interdisciplinary Institute at Maryland, the University of Maryland Institute for Advanced Computer Studies, and the National Science Foundation (NSF) National Center for Atmospheric Research.
Maria serves as a member of the US Climate Variability and Predictability (CLIVAR) Predictability, Predictions, and Applications Interface panel and of the World Climate Research Program (WCRP) Scientific Steering Group for the Earth System Modeling and Observations (ESMO) core project. Recently, Maria earned the NASA Early Career Investigator Program in Earth Science award.
Inna joined the Earth System Modelling Section at ECMWF in 2018. Inna’s background is in atmospheric dynamics, and she works on improving the representation of resolved dynamical processes in ECMWF’s numerical weather prediction model IFS. Her current research interests are in km-scale global modelling and in hybrid modelling, combining machine learning models with the physics-based NWP models online.
Prior to joining ECMWF, Inna worked on stratospheric dynamics and stratosphere-troposphere coupling at the University of Reading. She completed her PhD on modelling atmospheres of extra-solar planets at Queen Mary, University of London in 2014.