Yes; they basically just extrapolated from these "rain blobs" on the visualization as the short-term forecast they provided to users. There are some long-since wiped blog posts that provide a bit more context on how they do a little bit of statistical processing of the general forecast model output to help with they "hyper-localization," but the reality is that it was terribly unsophisticated relatively to what is traditionally done in meteorology.
The rain nowcasting feature that Dark Sky popularized is now table stakes in any consumer weather app. There's little value in making these types of forecasts any more complex (e.g. using AI or other contemporary techniques) because they still have egregious and noticeable failure modes. And it's so trivial to make this type of forecast that there is open source software you can easily run to do it [1].
The rain nowcasting feature that Dark Sky popularized is now table stakes in any consumer weather app. There's little value in making these types of forecasts any more complex (e.g. using AI or other contemporary techniques) because they still have egregious and noticeable failure modes. And it's so trivial to make this type of forecast that there is open source software you can easily run to do it [1].
[1]: https://pysteps.github.io/