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X-WR-CALNAME;VALUE=TEXT:ClimaTea Journal Club
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SUMMARY:ClimaTea Journal Club
DESCRIPTION:<p>	Speaker: <span>Aleyda Trevino </span></p><p>	Aleyda  will lead a discussion on <strong>using machine learning techniques and data assimilation to improve the parameterization schemes in climate models</strong>.<br>In the discussion, Aleyda will lead us through the following key points:</p><ul>	<li>		<span>A potential of using the techniques in data assimilation and machine learning to improve the current parameterization schemes in climate models.</span>	</li>	<li>		<span>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.</span>	</li>	<li>		<span>We will discuss some detailed examples for adopting machine learning techniques in the climate and Earth sciences modeling.</span>	</li></ul>
LOCATION:Seminar Room MCZ, 429
STATUS:CONFIRMED
DTSTART:20180306T200000Z
DTEND:20180306T200000Z
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