If I had to place a bet on this being the worst fire season ever in California, or not, my chips are shifting in a not good direction. Drought monitor from today: 26% of the state is in exceptional drought. Up from 16% last week. https://droughtmonitor.unl.edu/data/jpg/20210525/20210525_ca_text.jpg …
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Is there a literal model linking preconditions to outcomes (complete with random factors)? Conclusions can be very different if we're talking about wf = pre + rand, or pre ** 2 + rand, or pre * rand, etc (to caricature)
The severity of a car crash has a large random component (based on everything that happens at that moment) but because it is a sigmoid function of speed (preconditions) there is a regime in which the preconditions dominate the severity
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Broadly speaking, I think that's an apt analogy to the wildfire situation. High speed, generally, is a necessary but not sufficient precondition for a severe car crash; the same is usually true of extreme vegetation dryness and very intense fires.
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But that is only true at reasonable speeds, if the speed is high enough you can infer the outcome regardless of the random component (because of the sigmoid relationship). Is this the case for wildfires?
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