Pondering perspective in ensemble modeling

When forecasting anything, one must always consider the perspective one has. This is not easily achieved since our point of view is necessarily biased, either by a previous forecast, previous experience, analogs, or instinct.

Perspective "is the choice of a context or a reference (or the result of this choice) from which to sense, categorize, measure or codify experience, cohesively forming a coherent belief, typically for comparing with another." - From wikipedia.

Note the implied bias: "typically for comparing with another".

This is why ensembles are so neat in modeling the weather. The whole point of an ensemble is provide perspective or perhaps more appropriately predictability and by extension certainty (or uncertainty). This is particularly true even if the range of solutions does not cover the phase space of what is possible. In most instances, the mean of the ensemble is better than any individual member.

In the case where the outlier has the most value (no matter how wrong it is), the forecasters perspective may be the only real clue that it is even remotely likely for it to be correct. That is the value of the human forecaster and their experience is most likely to recognize the value of an outlier. There is significant risk associated with favoring an outlier. For one, you are going against what all the other members of the ensemble are trying to convey. So, you have to have good reasoning and great perspective on why so many members could be wrong.

In my opinion, this is why so many BIG forecast failures have occurred. It is difficult to trust an outlier, in a timely manner, because it takes a long time to discount a lot of members AND analyze the outlier in question in great detail such that you trust the solution. This is a major issue in severe storms research since the forecast period is short, the lifetime of some storms and their hazards are even shorter, and the models we use are shrouded in uncertainty (initial data, model spin up time, resolution, physics, dynamics).

Let us not forget that even ensembles have difficulty in predicting the certainty. Just because all the members are similar does not mean the forecast is certain. The issues we face now are just as much technical as they are scientific. Navigating the world of coarse ensembles and fine resolution ensembles will be fascinating.