Ed Gerber

Ed Gerber

Professor at the Courant Institute

Can we make useful forecasts beyond a couple weeks?

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Deterministic weather forecast are only possible for one to two weeks. (Or in other words, we just can’t predict whether it will be sunny or rainy 14 days from now.) But can we say something about the weather over the next few weeks, for example, will it be warmer and drier than average, even if we can’t say exactly which days will be sunny?

The answer is yes, we can (sometimes) provide informative forecasts on the time scales of a few weeks to a month, or “sub-seasonal” in the weather prediction lingo. Please check our our chapter Sub-seasonal Predictability and the Stratosphere, where we show how the the stratosphere can help us provide useful forecasts on these time scales! This is just one chapter in the epic, 585 page Sub-Seasonal to Seasonal Prediction: The Gap Between Weather and Climate Forecasting.


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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!