  Neural networks have opened up many new opportunities to utilize remotely
sensed images in meteorology. Common applications include image classification,
e.g., to determine whether an image contains a tropical cyclone, and image
translation, e.g., to emulate radar imagery for satellites that only have
passive channels. However, there are yet many open questions regarding the use
of neural networks in meteorology, such as best practices for evaluation,
tuning and interpretation. This article highlights several strategies and
practical considerations for neural network development that have not yet
received much attention in the meteorological community, such as the concept of
effective receptive fields, underutilized meteorological performance measures,
and methods for NN interpretation, such as synthetic experiments and layer-wise
relevance propagation. We also consider the process of neural network
interpretation as a whole, recognizing it as an iterative scientist-driven
discovery process, and breaking it down into individual steps that researchers
can take. Finally, while most work on neural network interpretation in
meteorology has so far focused on networks for image classification tasks, we
expand the focus to also include networks for image translation.
