Ed Gerber

Ed Gerber

Professor at the Courant Institute

Modeling our atmosphere with help from the machines

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Despite tremendous advances in our understanding of the atmosphere and our capability to simulate it with numerical models on the fastest computers in the world, their remain processes that we can not accurately represent from basic physical principles. In some cases, it is an issue of computational power: we cannot resolve all relevant scales for climate prediction, from planetary scale weather systems (10^6=1,000,000’s of meters) to cloud and aerosol particles on the microscale (10^-6=0.000001 m). In other cases, we do not yet know all the relevant physics! We still need to do our best to represent these processes based on what we can simulate. Traditionally this has been done with physically motivated schemes, but there’s growing in interest in using machine learning to help. Here we take the first steps of using an artificial neural network to help parameterize atmospheric gravity waves.

Atmospheric gravity waves (GWs) or “buoyancy waves” are generated by perturbations in a stably-stratified environment. They mediate momentum transport between the lower and middle atmospheres and play a leading-order role in driving middle atmospheric circulation. Due to computational constraints and a lack of observations, global climate models “parameterize” or crudely estimate the effect of GWs on the large-scale flow based on the state of the atmosphere that can be respresented by the model (the “resolved” flow). Current climate predictions are sensitive to uncertainties in these representations, particularly at the regional scale. Here, we present a novel approach to parameterizing GWs by training a neural network to emulate an existing gravity wave parameterization in a global climate model. This approach represents an appealing technique to build data-driven gravity wave schemes that can reduce existing uncertainties.

For the details, please see the paper: Machine Learning Emulation of Parameterized Gravity Wave Momentum Fluxes in an Atmospheric Global Climate Model, just submitted to Geophysical Research Letters. It was led by Zachary Espinosa, a graduate student working with Aditi Sheshadri at Stanford University, in collaboration with Gerald Cain and Kevin DallaSanta.

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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.