Ed Gerber

Ed Gerber

Professor at the Courant Institute

Two good papers just got better

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I am aware that things have been rather quiet on my blog in the last months. In addition to my new found profession as an elementary school teacher (alas, not a very good one, but our efforts to get the kids transferred to another class were fruitless), we’ve been hard at work on revisions. Some very detailed and careful reviews allowed us to make two good papers even better!

Numerical impacts on tracer transport: A proposed intercomparison test of Atmospheric General Circulation Models has been substantially restructured to more clearly present and motivate our experiments and results. It also includes more details on the inner workings of the dynamical cores, and how they relate to comprehensive AGCMs. An anonymous reviewer provided the most careful, detailed review that I have received in my career; he/she really went the extra 1.61 km to make our paper stronger.

Downward migration of the zonal-mean circulation in the tropical atmosphere now includes more detailed analysis of the dynamics driving these intriguing circulation anomalies in the tropical circulation. We also delve into the tricky subject of what it means to be “annular” in some new supplemental figures. We unfortunately had to jettison some interesting experiments with a dry dynamical core to fit the new analysis, but I do feel these would be better presented in a longer, follow up manuscript.

Great work Aman and Kevin!

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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!