About Dr. Tianle Yuan: Dr. Yuan received his B.S. in Geophysics and Computer Science from Peking University, his Ph.D. from the University of Maryland, College Park in 2008. After graduation, he became affiliated with the Joint Center for Earth Systems Technologies (JCET) at the University of Maryland Baltimore County (UMBC) and NASA Goddard Space Flight Center (GSFC) as an Associate Research Scientist. His research interests include cloud and aerosol climate feedback, aerosol-cloud interactions, remote sensing, cloud physics, and application of ML/Deep Learning in Earth science. In deep learning applications, Dr. Yuan published a few papers in modeling sub-grid clouds, global scale clouds, hurricane prediction, finding ship-tracks, and supervised and unsupervised cloud morphology classifications.

ARTIFICIAL INTELLIGENCE-BASED CLOUD DISTRIBUTOR (AI-CD): MODELING CLOUDS AT DIFFERENT SCALES

Here we introduce the artificial intelligence-based cloud distributor (AI-CD) approach to generate cloud fields across different scales and cloud types. We show that generative adversarial nets (GANs) can not only generate realistic cloud fields with corresponding meteorological variables, but also capture known physical relationship between cloud fields and meteorological variables such as sea surface temperature, atmospheric stability, and relative humidity etc. We demonstrate that this approach works across a large range of spatial scales: from individual grid points (sub-grid process modeling), multiple grids, to global scale. In addition, the AI-CD approach is stochastic in nature. We suggest the AI-CD approach can be used as a data-drive framework for stochastic cloud parameterization.

The MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.

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