Slashdot Mirror
A British Supercomputer Can Predict Winter Weather a Year In Advance (thestack.com)
The national weather service of the U.K. claims it can now predict the weather up to a year in advance.
An anonymous reader quotes The Stack: The development has been made possible thanks to supercomputer technology granted by the UK Government in 2014. The £97 million high-performance computing facility has allowed researchers to increase the resolution of climate models and to test the retrospective skill of forecasts over a 35-year period starting from 1980... The forecasters claim that new supercomputer-powered techniques have helped them develop a system to accurately predict North Atlantic Oscillation -- the climatic phenomenon which heavily impacts winters in the U.K.
The researchers apparently tested their supercomputer on 36 years worth of data, and reported proudly that they could predict winter weather a year in advance -- with 62% accuracy.
The researchers apparently tested their supercomputer on 36 years worth of data, and reported proudly that they could predict winter weather a year in advance -- with 62% accuracy.
I call BS on the headline. Let the damn thing prove it can do it before we claim it can. And doing regression model tweaking doesn't prove anything.
66% of the days in London contain some form of precipitation. So, I predict rain every day. I'm right 66% of the time. Wow, I'm smarter than a supercomputer!
The problem with that approach is that you will tweak the algorithm until it works in 1996.
In other words, you will incorporate 1996 into the test set.
This is the big problem with almost all climate studies, and the reason why people that understand statistics really hate the current climate "science" as it is done. You really do need to make a prediction, and then test the prediction. If you get it wrong, you cannot re-try against the same data set until it works.
while (sig==sig) sig=!sig;
There are an infinite number of functions that can go through the data points of the past. I could make you 1,000 perfect stock predictors for past data.
Ask yourself, how did they refine and improve this model over time? It's nothing but a pile of cooked books
No, not at all, because doing what you described is incorporating brand new data every year.
They kept adjusting the algorithm over and over until they got the right answer from 1980 onwards. The huge risk with that method is overfitting, and if you develop an algorithm this way, it's important to also show that you've managed to avoid overfitting.
You can do the same thing with stock market data: adjust it until you get nearly 90% correct returns on a test interval, then you will find that the next year, the model is completely wrong because of overfit. Even if you incorporate the next years data, you will still get incorrect results because the nature of the stock market is chaotic and also random.
"First they came for the slanderers and i said nothing."
Little better than random chance, then.
Chance only gives you 50/50 for a coin flip. The weather has many more states, so chance will give you something much worse than 50/50.
SJW n. One who posts facts.
That's only true if there are two types of weather to choose from.
And more ignorant nonsense gets modded Informative. The anti-science here is getting worse. Posters like you not only drastically overestimate your own knowledge of unfamiliar fields, you then insist to others it must all be a scam.
Weather and climate models aren't some arbitrary curve-fitting; they're physically based using ridiculously detailed physical simulations of air movements and ocean currents, starting from an observed state and running the simulation forward. Read up a little, and maybe you'll learn how to learn again.
Your claims of overfitting would mean something if they used a purely statistical model, but it's not - it's a physical simulation, constrained by laws of thermodynamics.
It amazes me that people say how trivial it is to fit statistical models perfectly to any random data (the stock market always gets mentioned here), yet don't think to wonder why they "only" got 62% accuracy. You'd think that this would be a huge red flag that your assumptions are wrong, but instead it's waved away as them all being dumber than a high school stats student - or more commonly a scam, with the researchers clearly hoping that no meddling high school stats students would notice.