After a rather heated debate last night about climate models, betting on the model runs and trying to analyse the data from these massive collections of model runs (ensembles) I have a few thought provoking questions for people:
Is the mean meaningless or useful?
What would you do if the mean is consistently wrong?
Would you bet on the mean of an ensemble?
How do you cater for the variability shown by the forecasts, especially if they encompass extreme that can feasibly happen?
Do you feel its responsible to give a policy-maker or decision-maker the mean without a proper feeling for the envelope of possibilities? Could policy-makers design policies that used that variability? Would these be more robust than the current solutions?
2 comments:
Well, I feel I expressed my view pretty well last night, so I won't attempt to type it all in here again. And I hope you're right; I hope what you want to do is possible, because if it is, then I certainly agree that it would be better than what we've got now. So I'm not trying to shoot you down. I'm just not yet convinced that it's achievable. But whether you succeed or not, you'll learn a huge amount trying, and that's really the point. Go for it, and don't let anybody stand in your way.
As a voice from afar and dislocated from the discussion ....
Means seek to suppress noise through averaging ... surely one wants to filter noise (as defined as the non-deterministic response to a forcing / boundary condition) *prior* to any averaging / aggregation of ensemble members?
Also: the mean of what? The mean of a derivative attribute may be more skillful than the mean of the base variable.
Post a Comment