Ed Gerber

Ed Gerber

Professor at the Courant Institute

Virtual Presentations at EGU this April

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As my family and I cannibalize each other on our solitary descent into the abyss that is remote elementary education, intrepid collaborators will be hitting the virtual road to present at the vEGU this April! Check out these presentations:

EGU21-336: Nonlinear Interaction between the Drivers of the Monsoon and Summertime Stationary Waves, presented by Chaim Garfinkel in Session AS1.10 – The global monsoons in current, future and palaeoclimates and their role in extreme weather and climate events.

EGU21-1398: Machine Learning Emulation of Parameterized Gravity Wave Momentum Fluxes in an Atmospheric Global Climate Model, presented by Zac Espinosa in Session AS1.14 – Internal Gravity Waves.

EGU21-16323: Forecasting rare stratospheric transitions using short simulations, presented by Justin Finkel in Session NP2.1 – Nonlinear Multiscale and Stochastic Dynamics of the Earth System.

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See the forest and the trees!

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Congratulations to Dave Connelly, who just submitted his first paper! It is about the use of regression forest to represent atmospheric gravity wave momentum transport to JAMES! The manuscript makes two important steps forward. First, it shows that a “boosted forest” approach, where you train each subsequent decision tree on the residual (as sketched below), can out perform a “random forest” where you combine a number of decision trees, averaging the result. This was well known in the ML community, but less so in the climate sciences. Second, Dave found that techniques from interpretable AI could be used to improve the training of a data driven parameterization. Using feature importance metrics, he found that his origional boosted forest wasn’t using enough information about latitude. By forcing the method to predict the latitude as well, he could build trees that incorporate this information more effectively!

The graft-versus-host problem…

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Ofer Shamir just submitted a paper on the use of machine learning to represent atmospheric gravity wave momentum transport to QJRMS. He developed an idealized, one-dimensional model of the Quasi-Biennial Oscillation, where we could systematically compare different data-driven methods. In particular, how can one calibrate a data-driven scheme to work in a biased model (the graft-host problem), and how well can schemes generalize to new conditions, as in a climate change scenario?

Um, what happened to me?

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I flatter myself to think you may have stopped by my webpage over the last year and half and wondered, what happened to Ed? No new papers? No research? Did he drop off the face of the Earth? Sort of. I was on sabbatical at Free University Berlin and Ludwig Maximillian University, Munich for an academic year!