#  ClimaTea Journal Club 

 



####  calendar\_today Date and Time 

 **December 5, 2018** 

 12:00PM - 12:00PM EST 

####  pin\_drop Location 

 **HUCE Seminar Room MCZ 440**  



 

 



 

 **Speaker:** [PhD student Stephan Rasp from Ludwig Maximilians University](http://w2w.meteo.physik.uni-muenchen.de/people/phd_students/rasp_stephan/index.html)

 **Title:** ***"Machine learning to represent atmospheric sub-grid processes."***

 **Abstract:** The representation of sub-grid processes, especially clouds, remains the largest source of uncertainty for climate prediction. Cloud-resolving models alleviate many of the gravest problems but will remain too computationally expensive for climate predictions in the coming decades. In this talk I will discuss how machine learning, and deep learning specifically, can learn to parameterize atmospheric sub-grid processes from short-term high resolution simulations. Our results tie in with a recent push towards a more data-drive climate model development.



 

 



 

 See also:- [ ClimaTea ](/type-event/climatea-lecturejournal-club)
 
 

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