One of the more intriguing parts of Tim Palmer's interview (audio linked here, transcript here) was his caveat on using a more adaptive mesh approach to modeling weather patterns. Much of the first 30 minutes of the interview discusses the issue of modeling physical behavior using grids, where grids made of smaller squares (higher resolution) can capture more detail but are increasingly difficult to model and compute [more information here]. Prof. Goodman references a technique used adaptive mesh refinement, which essentially allows for more resolution in important areas at the cost of less resolution in other areas (explained in more detail in prior source) and speculates on whether the technique is helpful for modeling regions like the Bay Area, which experience large fluxes in weather conditions. However, Palmer pushes back on this notion by asking the question of where the additional detail would really go - would it be on the Bay Area itself, or in a larger region outside the Bay Area that allows one to forecast some time off? It seems the point there is essentially that every square is somewhat crucial for accurate meteorological predictions, which makes a tool that is crucial in other fields largely unhelpful in weather modeling.
If I had been part of the interviewing process, the one point I would have perhaps continued is the discussion of Palmer's idea of the climactic Turing Test, which is an assessment of models via whether or not it can "pass" as real-world data under the inspection of a trained expert [the study that references this is here]. In the case of weather, it can do so handily - in the case of climate, we are still quite far. When listening to that part of the conversation, I was struck by how it mirrored in many ways the historical transformation of the overall concept of prediction, which started as rooted in a humanistic approach but slowly developed towards the more distanced approach we know today. In other ways, it represents the still present rationalist/empiricist [or justificationist/critical rationalist] conflicts within our approach to modern science, as well as the lingering question of how predictive machine models can contrast (or complement) an explanatory theory.