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Are you really equating daily weather predictions with meteorological science? That's like saying "they don't know what the next 3 coin flips are going to be but they know half of the next 10,000 will be tails"


Unfortunately weather predictions aren’t as simple as a coin flip. But I’m sure meteorologists would manage to fuck up coin flips too.


and humans haven't figured out anything more complex than a coin flip...


That's not exactly true, it seems. Forecasts become less accurate the further out you go, unlike coin flips.

Weather forecasts are generally accurate about 90% of the time for a five-day forecast and around 80% for a seven-day forecast. Forecasts beyond ten days are only correct about half the time.


Why do you pack a light jacket if you go to Tasmania for a week in June, regardless of the forecast?


Why might you wear a life jacket on a boat, even though you're 99.9% likely not to have the boat flip over?


There's a difference between being slightly uncomfortable for a week and dying.


you're conflating statistics and trends with discrete predictions


Well, I'm not. The previous poster is. I'm more simply pointing out, without using terminology, that we shouldn't.


You can’t predict a coin flip because it is random. However, we have an accurate understanding of the random process producing coin flips and therefore, we can make accurate predictions about large quantities of flips.

Weather may or may not be random. It could be entirely deterministic for all we know. However, we lack the ability to fully model all the factors that contribute to weather and therefore our predictions are inaccurate.

Now let’s consider long term climate predications. Do you think these predictions are more like coin flips, where we have an extremely accurate model of the process, or more like weather, where unknown unknowns have outsized impact on accuracy?

That’s not to say climate change isn’t real, but your analogy doesn’t make sense.


All responses are so focused on exact predictions. We have high certainty that 50% of flips will be tails over long enough timespan. We don't know what any single flip will be. Climate science works the same way. But climate is not a coin, let's say it's a multisided die and it appears the sides are changing sizes as we compare data year over year.


I think you’re missing my point: we’re only able to predict large numbers of coin flips because we have an accurate model.

We don’t have an accurate model for weather, so we can’t predict it well.

I don’t see a reason to assume our model for climate is accurate, either.


Our models of weather are so accurate that literally trillions of dollars per year bank on them in the agriculture sector, the shipping sector, and everywhere else. Similarly, our models of climate change have been refined and refined, and now are essentially irrefutable.


::multiple laughing emojis::

Our “models of climate change” have regularly been falsified at this point. It is absolutely unknown how much “climate change” is attributable to humans right now.

Nor is it actually known what the net favorability of mild warming might be…including the possibility of mitigating the next Ice Age!



Predictive models are not the same as historic data analysis and trend fitting.

Flipping coins: no predictive models, very definitive statistics Weather: +/- 2 week predictive models, 100 years of measurements getting more definitive each year where trend are headed


Hypothetically. None of what you said is testable, and there is no evidence that models today are any better than before except at matching historic data.

Compare with another topic like, say, evolution. Here outcomes are testable and verifiable because we can observe the theory at work by watching micro-ecosystems, or small animals with fast reproductive cycles.

Meteorology is short term accurate based on a linear regression of data points from historical data. Deviation like "warming" or "cooling" are relative descriptions of how closely aligned one theory is to the line, and how far back the specific model goes along with the number and quality of relevant factors you want to look at.

No matter which model you go with, you're proving the accuracy of a math function at matching historical data, and then hoping that it will match the future. And as we know, none of them match the future very accurately, which tells us there's something wrong with the theory.

This is only slightly better than day trading in the stock market. And much like the stock market, everyone thinks they know better than everyone else but statistically, most fund managers and professional stock callers underperform the market. They earn by selling you on the idea that they have the next model that finally DOES make accurate predictions. They tell you that they know that because this new model matches the historical data more accurately. No shit. Because there's more data now in a growing set of data. So the most recently calculated linear regression is the most accurate.

But we don't know how it works. That's the key here. More data, doesn't mean the theory is better. More accuracy in making predictions about the future, on the other hand, is a strong indicator, and maybe the only indicator, that something is worth believing in. That is to say, it's more likely to be true.

Making overzealous claims about how much we know is not science, it's ignorance. Let's help interest people in science by being cautious about what we claim to know for sure. At least don't claim to know the next 200 years, until we can at least make accurate predictions beyond the next few days.

I majored in biochem with a lot of extra classes I took for fun on environmental chemistry. You?


> more like weather, where unknown unknowns have outsized impact on accuracy

"Unknown unknowns" aren't the reason weather forecasts are inaccurate.

Weather is path-dependent. Small changes to starting conditions or minor differences between modeled and actual conditions shortly after the simulation begins lead to large differences by the end of the simulation. Errors propagate and magnify.

Over large time periods the errors average out.


I have noticed after a distant super nova I always feel better


I think he means Dart code resembles C# pretty closely.


Wrong mindset in my opinion. Uncomfortable workouts are for delayed satisfaction of a more healthy outcome. I actually think you have it backwards. I think it's more likely you exercise less and never reach consistency if you expect all exercise to be enjoyable.


That might be the wrong way to interpret this. It actually validates interest in the framework. This change personally excited me and injects some change into the ecosystem that could result in a better future.


Very different environment back then. The content was minimal, lower quality, less engaging and there were no sophisticated algorithms. It's been finely tuned to be addictive in the last 15 years and can suck users in for hours and keep them up all night with endless content streams and dopamine hits. I also grew up in the 2000s era and am not disillusioned to think myspace and Facebook were in the same universe as TikTok and Instagram of today. The negatives for kids far outweigh the positives at this point.


also was at a wedding there and people daring others to smell the oil jar. apparently the smell is absolutely rancid


It's actually a 100% death rate as all smokers sure. Jokes aside, your statement doesn't have meaning. Many deaths are not attributable to a single cause. However, it's been studies endlessly and is well accepted that smoking increases all-cause mortality.


Im not arguing against that at all. I was making a point about the magnitude of that impact.

People tend to over-estimate the impact of both smoking and obesity.


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