In Part I of this series, we discussed the mathematics underpinning Gaussian process (GP) and Gaussian random field (GRF) models, how they can incorporate observations of either points or spatial averages, and how these two kinds of observation have differing effects on the model output.

As we’ll see, things get even more interesting when we combine both types of observation.

Why might we want to do this? Let’s look at a motivating situation. Suppose you have an autonomous vehicle recording a stream of observations relating to the rainfall at its current location. These tell you a lot about the situation…

Rachel Prudden

Rachel is a researcher in the Informatics Lab. Her current focus is on probabilistic super-resolution of weather models at convective scales.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store