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About the workshop

This virtual workshop is a continuation of the NOAA series of workshops on “Leveraging AI in Environmental Sciences.” The third event continues the successes of previous workshops and encourages participation by scientists, program managers, and leaders from the public, academic and private sectors who work in AI and environmental sciences. The theme for this year’s workshop is “Transforming Weather, Climate Services, and Blue Economy with Artificial Intelligence.” This year’s workshop is led by the NOAA Center for Artificial Intelligence (NCAI), a program in the formulation stage.

All time listed on this page is Mountain Time (UTC-6) by default. You can change to your local time zone on the right side of this page. Questions regarding the workshop can be addressed to ai.workshop@noaa.gov. View the participant handbook for how to navigate the workshop technology platforms.

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Tuesday, September 7 • 9:00am - 10:30am
Tutorial - Model calibration using ESEm – an open, scalable Earth System Emulator

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Sign up for the hackathon and tutorial here: https://ncsu.zoom.us/meeting/register/tJIkcOChpzoiH9JX9M7qF9bkbfYBNhZ660-e

Large computer models are ubiquitous in the environmental sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core-hours to run to completion while outputting terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce GCEm: an open-source tool providing a general workflow for emulating and validating a wide variety of environmental models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well established, high performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of GCEm through three case-studies using GCEm to reduce parametric uncertainty in a general circulation model, explore precipitation sensitivity in a cloud resolving model and scenario uncertainty in the CMIP6 multi-model ensemble.

avatar for Duncan Watson-Parris

Duncan Watson-Parris

Postdoctoral Researcher, University of Oxford


Tuesday September 7, 2021 9:00am - 10:30am MDT
Zoom N/A