#  ClimaTea Journal Club 

 



####  calendar\_today Date and Time 

 **March 6, 2018** 

 03:00PM - 03:00PM EST 

####  pin\_drop Location 

 **Seminar Room MCZ, 429**  



 

 



 

 Speaker: Aleyda Trevino

 Aleyda will lead a discussion on **using machine learning techniques and data assimilation to improve the parameterization schemes in climate models**.  
In the discussion, Aleyda will lead us through the following key points:

- A potential of using the techniques in data assimilation and machine learning to improve the current parameterization schemes in climate models.
- As a proof of concept, Schneider et al show that the computational efficiency can be improved using a low-order model (e.g. Lorenz-96) and methods like Bayesian inversions and ensemble Kalman inversions.
- We will discuss some detailed examples for adopting machine learning techniques in the climate and Earth sciences modeling.



 

 

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 Attachments- [  picture\_as\_pdf  schneider\_et\_al-2017-geophysical\_research\_letters.pdf ](/sites/g/files/omnuum11466/files/climate/files/schneider_et_al-2017-geophysical_research_letters.pdf)
 
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 See also:- [ ClimaTea ](/type-event/climatea-lecturejournal-club)
 
 

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