We all did some web displays for various components of the CI desk. I built a few web displays based on object identification of precipitation areas. I counted up the objects per hour for all ensemble or all physics members (separate web pages) in order to 1) rapidly visualize the entire membership and 2) to add a non-map based perspective of when interesting things are happening. It also allows the full perspective of the variability in time, and variability of position and size of the objects.
The goal was to examine the models in multiple ways simultaneously and still investigate the individual members. This, in theory, should be more satisfying for forecasters as they get more comfortable with ensemble probabilities. It could alleviate data overload by giving a focused look at select variables within the ensemble. Variables that already have meaning and implied depth. Information that is easy to extract and reference.
The basic idea as implemented was to show the object count chart and upon mousing over a grid cell you can call up a map of the area with the precipitation field. At the upper and right most axes, you call up an animation of either all the models at a specific time OR one model at all times. The same concept was applied to updraft helicity.
I applied the same idea to the convection initiation points only this time there were no objects, just the raw number of points. I had not had time to visualize this prior to the experiment, so we used this as a way to compare two of the definitions in test mode.
The ideas were great, but in the end there were a few issues. The graphics were good in some instances because we started with no precipitation or updraft helicity or CI points. But if the region already had storms then interpretation was difficult, at least in terms of the object counts. This was a big issue with the CI points, especially as the counts increased well above 400, for a 400 by 400 km sub domain.
Another display I worked hard on was the so-called pdf generator. The idea was to use the ensemble to reproduce what we were doing, namely putting our CI point on the map where we thought the first storm would be. Great in principle, but automating this was problematic because we could choose our time window o fit the situation of the day. The other complication was that sometimes we had to make our domain small or big, depending on how much pre-existing convection was around. This happened quite frequently so the graphic was less applicable, but still very appealing. It will take some refinement but I think we can make this a part of the verification of our human forecasts.
I found this type of web display to be very useful and very quick. It also allows us to change our perspective from just data mining to information mining and consequently to think more about visualization of the forecast data. There is much work to be done in this regard and I hope some of these ideas can be further built upon for visualization and Information Mining so they can be more relevant to forecasters.
