Riley (2012) has predicted the probability of storms with Dst < −850 nT occurring within the next decade to be about ~12%% based on the assumption that the frequency of occurrence scales as an inverse power of the intensity of the event.
Then there's global warming as a potential factor to affect the probability of storms, he said.
Each forest has its own unique set of physical characteristics (e.g. geology, slope, elevation, soils), a different mix of vegetation types at varying stages of maturity, and as each is located in a different geographical region the probabilities of storm frequency and associated rainfall intensity will be different, as will the geomorphic response and resultant aftermath.
That joint distribution is combined with the probability distribution of storm depth, to produce a joint probability distribution for storm depth, duration, and peak intensity.
Storm-event based probabilistic models characterizing the probability distributions of storm depth and duration are developed.
The authors present a statistical procedure to estimate the probability distributions of storm characteristics.
An Archimedean copula is applied to describe the joint probability distribution of storm duration with intensity peak factor.
For example, if we observe the three variables of pressure, barometer reading and the occurrence of storms, there is a strong correlation between the readings of the barometer and the probability of a storm occurring.
Then there are bigger factors that can affect the probability of a storm occurring, which are more difficult to nail down right away.
Thus, there are two things that need to be learned in causal induction: (1) the causal structure (i.e. which variables are connected by arrows— Fig. 2A shows 4 possible structures) and (2) the strength of causal relations (e.g. what is the probability of a storm occurring given that the pressure is high? Fig. 2B shows a possible parameterisation given a particular structure).
