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Past Webinars

Watch US CLIVAR Videos/Webinars on YouTube

- Data Science Webinar Series
Using the Self-Describing netCDF Format to Accelerate Scientific Workflows

Charlie Zender, UC Irvine

- Data Science Webinar Series
Causal inference in Earth system sciences

Jakob Runge, German Aerospace Center

- Data Science Webinar Series
Uncovering and Predicting Patterns in Large Climate Data

Maria J. Molina, NCAR

- Data Science Webinar Series
Machine-learning-model-data-integration for a better understanding of the Earth System

Markus Reichstein, Max Planck Institute for Biogeochemistry

- Data Science Webinar Series
Untangling aerosol-cloud interactions with machine learning

Duncan Watson-Parris, University of Oxford

- Data Science Webinar Series
Machine Learning of Partial Differential Equations

Michael Brenner, Harvard University and Google Research

- Data Science Webinar Series
Physics-informed machine learning for weather and climate science

Karthik Kashinath, Lawrence Berkeley National Lab

- Data Science Webinar Series
Physics Guided Machine Learning: A New Framework for Accelerating Scientific Discovery

Vipin Kumar, University of Minnesota

- Data Science Webinar Series
Building trustworthy AI for environmental science

Amy McGovern, University of Oklahoma

- Data Science Webinar Series
Evaluating Machine Learning Approaches for Physical Parameterizations in a GCM Model Hierarchy

Christiane Jablonowski, University of Michigan

- Data Science Webinar Series
Empirical Leading-Order Analysis by Artificial Intelligence

Bryan Kaiser, Los Alamos National Laboratory

- Data Science Webinar Series
What is the role of hydrological science in the age of machine learning?

Grey Nearing, University of Alabama

- Data Science Webinar Series
Exploring physical and Machine Learning approaches for stochastic modeling and ensemble prediction of weather and climate

Aneesh Subramanian, University of Colorado

- Data Science Webinar Series
Identifying regions of spatial correlation inhomogeneities in spatial processes via a Mixture Multi-Resolution Approximation

Veronica Berrocal, University of California, Irvine

- Data Science Webinar Series
Influence of uncertainties on the tails of climate projections

Ryan Sriver, University of Illinois

- Data Science Webinar Series
Scalable Covariance Approximations for Environmental and Climate Data

Matthias Katzfuss, Texas A&M University

- Data Science Webinar Series
S2S forecasting using large ensembles of data-driven global weather prediction models

Jonathan Weyn, University of Washington

- Data Science Webinar Series
Deep Unsupervised Learning for Climate Informatics

Claire Monteleoni, University of Colorado

- Data Science Webinar Series
Efficient Surrogate Modeling for Uncertainty Quantification in Assessment of Remote Sensing Retrievals and Storm Surges

Pulong Ma, Duke University

- Data Science Webinar Series
Veridical data science: the practice of responsible data analysis and decision making

Bin Yu, University of California, Berkeley

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