Even very SMALL systems present so much chaos that modeling requires generalizations that reduce fidelity, and hurricanes are MAMMOTH, HIGHLY chaotic systems. The difficulties encountered in modeling them at all accurately is evident in that MUCH human effort has been applied to the task, and -- even with modern technology, and the massive number-crunching power available -- we still have fallen well short of a good result; we still rely on real-time measurements, and frequent updates in our efforts to hone our predictions, and the degree of our remaining inaccuracy is evident in the "fan" shape of those moment-by-moment forecasts.
Too, there are factors that may influence the system more heavily than we know, and they would given with too little influence in the model, so -- again -- the fidelity of the model suffers. And there's what Briggs highlights: those factors "thought by the modeler to modify the probability of the observable Y..."
But DO they REALLY? Just because the modeler thinks so? Is it a valid assumption, or only that specific modeler's notion?
"It seems to me that they could take data from years past where an actual hurricane or hurricanes went, from start to finish, then use that data to fine tune a model to replicate the actual data path."
I respect the thought, but no two systems are EVER alike; the myriad variables are never the same; ocean surface temperature, humidity, prevailing winds, air temperatures at-altitude, winds aloft, position of the jet stream, other passing storm fronts, solar gain, time of day... it just goes on and on and on. Every last factor would have to be IDENTICAL at every place along the storm's route of travel for us to have any chance that data from a past hurricane would be reusable in a predictive context, and that's God level improbable.
Excellent.
The article points to some basic rules to view models through:
1. All models only say what they are told to say.
2. Science models are nothing but a list of premises, tacit and explicit, describing the uncertainty of some observable.
I would add a third: Models don’t know what they don’t know.
What bothers me is how much weight models (best guess predictions, aka science fiction) carry over actual observations. The significant deviation of climate models from actual observed data comes to mind.
This is why Freeman Dyson would not accept the validity of such models.
Yep, non-linear, chaotic systems are hard to predict over long time periods.