This is a screenshot of my weather app showing forecasted (smooth blue/purple) rain of the French Arome model VS rain radar (sharp green/yellow contours). It actually does a pretty good job on the large scale but the details aren't always quite right. Dies anyone know how rain probabilities are computed from these models instead of fixed predictions?

How would you design a tool to specify deeply nested forms, basically a complex decision tree, eg for medical diagnosis or complex tax stuff etc, where future form elements can dependent on a previously specified one, but statically ie chosen from few option not generated via a function so that all options and branches are known without executing code. The form specification should just be declarative, data. What data structure would you use to represent that form (I think it would basically be a DAG)? What language would you write it in? How would you generate a graphical representation (eg converting to .dot)? How would you generate a fillable form from it?

I always wish I wouldn't have to pull in python/pandas to do simple data transformations on smaller local datasets/csv files and stick with sqlite. But then I tend to quickly hit a problem that seems to require dynamic SQL. Anything in between SQL and a general programming language that is made for transforming tabular data?