613 Webex Seminar: Gregory Elsaesser (NASA/GISS)

Gregory Elsaesser

Columbia University, Dept. of Applied Physics and Applied Mathematics

NASA Goddard Institute for Space Studies

 

Using Pixel-Level Satellite Data to Improve a Coarse-Resolution Global Climate Model

 

This talk will begin with an overview of recent development in the NASA GISS General Circulation Model (GISS-E3 GCM, currently about to be submitted to CMIP6).  Global satellite products are important references for continual GCM development and evaluation, and I will discuss recent effort to use machine learning (ML) to optimize a multitude of GCM free parameters in a way that leads to agreement with numerous NASA satellite climatologies.   Even though ML can constrain a far greater number of free parameter settings using more observational references than are typically used in non-ML efforts, GCM discrepancies at cloud and convection lifecycle scales (i.e., process-level scales) remain.   Since satellite products often provide data at coarse temporal and/or spatial resolutions, thus making it difficult to conceptualize and evaluate process representations in a GCM, I will discuss the various approaches I am taking to average global satellite retrievals in new ways to derive composite high-resolution evolutions clouds and the nearby environment (with a focus on deep convection lifecycles in this talk).  These depictions will not only inform future parameterization development, but they are also crucial for ensuring that recent improved mean-state representations are not the result of errors cancelling at the process level.  

 

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613 Seminar Series Coordinators

Yuekui.Yang-1@nasa.gov

Jae.N.Lee@nasa.gov

 

Please do not forward the meeting invitation to outside recipients as the presentation, discussions, and questions may contain proprietary or confidential information.

 

Meeting number (access code): 199 051 6388

Meeting password: PNijn3wR?27

 

Wednesday, August 19, 2020

1:15 pm  |  Eastern Daylight Time (New York, GMT-04:00)  |  1 hr 30 mins

 

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