
This is an experimental product prepared on 20120130.
All information contained within reflect data collected prior to the time of issuance.
For additional information contact Dr. Les Bender at GERG, Texas A&M University.
[TABS]  [RTA]  [Climatology] 
[Buoy B]  [Buoy D]  [Buoy F]  [Buoy J]  [Buoy K]  [Buoy N]  [Buoy R]  [Buoy V]  [Buoy W] 
This helps to explain what a flow reversal probability is and how it is calculated.
Imagine you are the manufacturer of a mechanical widget and you want to know the probability of failure once the widget is put into operation. You begin a testing program and record the time at which each unit ultimately fails. This eventually leads to a lot of broken widgets and a probability distribution of failures. This distribution is best modeled by Weibull statistics because they were specifically developed by statisticians to answer the question of failure probability.
I have modeled flow reversal on the shelf as a failure that can be modeled with Weibull statistics. For each buoy I began with the 40hour lowpassed, alongcoast current data. I determined the length of time the flow remained downcoast before it switched to upcoast. I looked at all of the data together, as well as the winter months of September through May and the summer months of June through August. Separately I examined the upcoast to downcoast reversal. From these six separate data sets of component failure times I calculated the Weibull statistics for each of the combinations. The results are shown in each of the figures. It shows four curves, which reflect how long the flow has been unidirectional. For example, the start means the flow just reversed and the two weeks means the flow has already been upcoast/downcoast for two weeks. In any case, the curve tells you the probability of a reversal over the next ten days.
By examining a season, say the winter, you can readily tell the preferred flow direction for each buoy by noting whether the upcoast to downcoast or downcoast to upcoast reversal probability is smaller. The lower the probability of reversal the longer it is likely to stay flowing in that direction. You can also see if the system is inherently stable. If the component becomes less likely to fail the longer it is in service, then the probability of failure decreases with time. This is a stable system. But if the component is more likely to fail, then the probability of failure, i.e., reversal, becomes greater the longer the flow continues. This is an unstable system.