At the moment the wave charts are not looking too epic. However, in about a week’s time things are due to improve as the general situation over the North Pacific changes.
“That’s all I can say. Why? Because the best way to look at the long-term charts is to just get a general picture and not try to be too specific; otherwise you’ll be frustrated when the forecast keeps changing.”
Forecasting is a trade-off between three parameters: precision, accuracy and length. If we want a really detailed forecast it needs to be short-term; otherwise it won’t be accurate. Likewise, if we want an accurate long-term forecast we mustn’t specify too many details.
As our understanding of the atmosphere and ocean improves, and computing power increases, forecasts will, of course, get better. But there are several reasons why they will never be perfect: the most fundamental of which has to do with their dependence on initial conditions.
Atmospheric and oceanic prediction models rely on initial measured values of parameters such as pressure, windspeed and temperature. The more accurate these measurements are, the better the prediction will be.
The ocean-atmosphere system is highly complex and behaves in a non-linear way, with feedback loops, tipping points and snowball effects. Any slight errors in the initial measurements will not only feed through to the end result, but will be amplified. As the forecast length increases, so does the amplification of errors, so that the forecasts end up diverging uncontrollably.
So, all we need to do to get that perfect forecast is to measure those initial conditions perfectly. In fact, this is what the great French physicist Pierre-Simon Laplace (1749-1827) had in mind. He postulated that, if we could somehow measure the exact position and velocity of every particle in a system, we could use Newton’s laws of motion to predict their next position and velocity, and the next, and so on. As long as we knew the present, we could predict the future
Laplace’s hypothesis – called determinism – was proved wrong about a century later. Scientists like Werner Heisenberg (1901-1976) started discovering the paradoxes of quantum mechanics, one of which is that you cannot measure the position and velocity of a particle at the same time.
This means you can never describe the present state of anything with 100 per cent accuracy. And if you can’t describe the present state with total accuracy, you’ll never be able to make an error-free prediction of the future.
So, bearing in mind that there are inevitably going to be errors, the best thing we can do is to know how precise to be, and when. The MSW ‘probability’ parameter helps us do that by giving us an idea of how confident we can be of a particular forecast. You’ll notice that it doesn’t just change with forecast length – but I’ll talk more about that in a future article.