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1 INTRODUCTION Coupled data assimilation has many definitions and naming conventions, a lot of which are not strictly necessary to be aware of in order to understand the aspect of the field that we tackle in this article. One can consider coupled data assimilation as the challenge of performing data assimilation in a system with multiple, relatively distinct, components. For example, these could be the atmosphere and the ocean, or waves and sea ice, etc.
Phil. Trans. R. Soc. A379, 20200089. (Published online 15 February 2021). (doi:10.1098/rsta.2020.0089) In this article, there was an error in equation 2.5, as the logarithm should have been multiplied by −2, not −1. This has now been corrected.
1. Introduction Machine learning (ML) has made rapid progress in diverse areas including the classification of images [1,2], translation between languages [3,4] and superseding human skill at the game of go (e.g. [5,6]). These applications can require neural networks with millions to billions of trainable parameters, large numbers of layers and specialized architectures, such as convolutional networks.
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