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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Xingjian (Ken) Yan, a precocious undergraduate (now bound for a PhD at MIT) working with Lei Wang and I just submitted a paper exploring the utility of the traffic jam theory of blocking onset for perdiction to Geophysical Research Letters. Ken defined and explored “flux exceedance events”, meteorological situations where the jet stream gets overloaded with storm activity. Nakamura and Huang suggested that this overloaded jet situation creates a pile up storm activity – a traffic jam – leading to blocking events. Ken found that the climatological structure of exceedance events is remarkably similar to that of blocks, but that they appear to be distinct phenomenon: an overloaded jet stream is unfortunately not a reliable harbinger of an atmospheric block.

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Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational data

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Huan Zhang submitted our paper on how to use machine learning to predict the persistence of blocking events to the new Journal of Geophysical Research Machine Learning and Computation, published by the AGU. Blocking events are persistent high pressure systems that “block” the flow of the jet stream. They are associated with extreme weather, as they shift the direction of storms, and, in summer, create heat domes that drive heat waves. A key element of a block is its persistence. Huan developed a convolutional neural network to predict whether an nascent blocking anomaly would persist, or fade away, and then interrogated the network to understand why it worked, and how it could be trained with the short observational record.

So, you have an imperfect data-driven parameterization for climate modeling. How can you make it better?

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Minah Yang submitted a paper on how to combat data imbalance in regression problems to JAMES. The goal is to improve the performance of data-driven paramterizations, particularly for profiles that are rare, but important. This is a data imbalance problem: we need ensure that the parameterization works well on input-output pairs that are seldom seen in training. Minah proposes a technique based on histogram equalization, visualized below with help from Cece, Minah’s faithful companion! The idea is to oversample or reweight these rare cases during training, to ensure the method learns from them.