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Our June luncheon speaker is Forrest M. Hoffman, a Computational Earth System Scientist who is a Corporate Fellow and the Group Leader for Integrated Computational Earth Sciences in the ORNL Computational Sciences and Engineering Division. This is a free public event. Doors open at 11:15 for networking and eating lunch (light lunch available for a donation); talk starts at noon.Forrest's abstract:
Earth system science data encompass temporal scales of seconds to hundreds of years, and spatial scales of microns to tens of thousands of kilometers. Because of rapid technological advances in sensor development, computational capacity, and data storage density, the volume, velocity, complexity, and resolution of these data are rapidly increasing. Machine learning (ML), data mining, and other approaches often referred to collectively as artificial intelligence (AI) offer the promise for improved prediction and mechanistic understanding, and the path for fusing data from multiple sources into data-driven and hybrid models comprising both process-based and deep learning elements. In addition, the increasing complexity of Earth system models (ESMs) is driving a growing need for comprehensive and multi-faceted evaluation of model predictions across spatial and temporal scales. Improved representations of biogeochemistry–hydrology interactions and ecosystem processes in ESMs are essential for reducing uncertainties over sub-seasonal and seasonal-to-decadal time scales. For over a decade, the terrestrial modeling community has been developing diagnostic approaches for evaluating ESM hydrological and biogeochemical process representations through the International Land Model Benchmarking (ILAMB) package, an open-source benchmarking system that leverages the growing collection of laboratory, field, and remote sensing data. This benchmarking system performs comparisons of model results with best-available observational data products, focusing on biogeochemistry, hydrology, nutrients and soil organic matter, ecosystem processes and states, and vegetation dynamics. To advance understanding of biogeochemical processes and their interactions with hydrology under conditions of changes in extreme events and atmospheric composition, new methods are needed that use observations to constrain model predictions, inform AI and process-based model development, and identify needed measurements and field experiments. This presentation will describe a variety of Earth characterization, uncertainty quantification, and model prediction approaches for applying ML methods; the current state of model evaluation capabilities employed for ESMs; and an international data infrastructure, called the Earth System Grid Federation (ESGF), for archiving and distributing ESM model output and related forcing and observational data.
Bio:
Forrest M. Hoffman is a Corporate Fellow, a Computational Earth System Scientist, and the Group Leader for the Integrated Computational Earth Sciences Group at Oak Ridge National Laboratory (ORNL). He develops and applies Earth system models (ESMs) to investigate global biogeochemical cycles and feedbacks between biogeochemical cycles and the Earth system. Forrest is a leader in community model benchmarking activities and the development of the International Land Model Benchmarking (ILAMB) and International Ocean Model Benchmarking (IOMB) packages. He is particularly interested in applying machine learning methods to explore the interactions of terrestrial and marine ecosystems with hydrology. Forrest leads development and deployment of a next generation Earth System Grid Federation (ESGF) distributed data infrastructure in the US. In addition, Forrest applies data mining methods using high performance computing to problems in landscape ecology, ecosystem modeling, remote sensing, and large-scale Earth science data analytics. Forrest is also a Joint Faculty Professor in the University of Tennessee’s Department of Civil & Environmental Engineering, a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE), and a Fellow of the American Association for the Advancement of Science (AAAS).
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Event Venue
UT Resource Center, 1201 Oak Ridge Turnpike, Oak Ridge, or via Zoom (link available soon) 37830, United States
Tickets
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