The frequency of exceedance, sometimes called the annual rate of exceedance, is the frequency with which a random process exceeds some critical value. Typically, the critical value is far from the mean. It is usually defined in terms of the number of peaks of the random process that are outside the boundary. It has applications related to predicting extreme events, such as major earthquakes and floods.
The frequency of exceedance is the number of times a stochastic process exceeds some critical value, usually a critical value far from the process' mean, per unit time. Counting exceedance of the critical value can be accomplished either by counting peaks of the process that exceed the critical value or by counting upcrossings of the critical value, where an upcrossing is an event where the instantaneous value of the process crosses the critical value with positive slope. This article assumes the two methods of counting exceedance are equivalent and that the process has one upcrossing and one peak per exceedance. However, processes, especially continuous processes with high frequency components to their power spectral densities, may have multiple upcrossings or multiple peaks in rapid succession before the process reverts to its mean.
Consider a scalar, zero-mean Gaussian process with variance and power spectral density, where is a frequency. Over time, this Gaussian process has peaks that exceed some critical value . Counting the number of upcrossings of, the frequency of exceedance of is given by
N(ymax)=N0e-\tfrac{1{2}\left(\tfrac{ymax
N0=\sqrt{
| ||||||||||
For a Gaussian process, the approximation that the number of peaks above the critical value and the number of upcrossings of the critical value are the same is good for and for narrow band noise.
For power spectral densities that decay less steeply than as, the integral in the numerator of does not converge. Hoblit gives methods for approximating in such cases with applications aimed at continuous gusts.
See also: Return period.
As the random process evolves over time, the number of peaks that exceeded the critical value grows and is itself a counting process. For many types of distributions of the underlying random process, including Gaussian processes, the number of peaks above the critical value converges to a Poisson process as the critical value becomes arbitrarily large. The interarrival times of this Poisson process are exponentially distributed with rate of decay equal to the frequency of exceedance . Thus, the mean time between peaks, including the residence time or mean time before the very first peak, is the inverse of the frequency of exceedance .
If the number of peaks exceeding grows as a Poisson process, then the probability that at time there has not yet been any peak exceeding is . Its complement,
pex(t)=1-
| -N(ymax)t | |
| e |
,
If is small, for example for the frequency of a rare event occurring in a short time period, then
pex(t) ≈ N(ymax)t.