Their corrections may be correct, but it's still always nicer when the magnitude of the signal is larger than that of the corrections to the data.

What is nice is vetting and correcting data for reliability and consistency.

Part of aiming for objectivity, and better understanding, isn't worrying about which direction a correction takes. At all. Only that it is a good correction.

Our brains constantly make up distracting narratives, if we let them.

If the correction had been the other way, some people would say scientists have been downplaying data and probably still are.

> Part of aiming for objectivity, and better understanding, isn't worrying about which direction a correction takes. At all. Only that it is a good correction.

I don't think you fully grasp the implications of arbitrarily correcting old measures. In the end, and accepting at face value these corrections, you're still manipulating old data to use the result of said manipulation as the whole basis of your hypothesis.

This approach automatically leads to questions on whether you draw your conclusions from the data, or you change the data to fit your conclusions.

Do you understand the risk that this poses in any discussion on a politically sensitive topic?

Think of the hit to the credibility of any claim supported by this data manipulation if later your method is deemed untrustworthy because it needs further updates, and how it would look if you had to correct it to move the dial either way (i.e., "they were lying from the start and are now covering their ass" vs "they felt their lie wasn't fooling anyone and decided to double down.")

> arbitrarily correcting old measures.

Why do you say the correction is arbitrary? Are there papers arguing for corrections in the other direction?

> Why do you say the correction is arbitrary?

Because it follows a method picked and chosen by the corrector.

> Are there papers arguing for corrections in the other direction?

It doesn't really matter if these corrections sway one way or the other. What matters is that someone decided that the original values weren't good, and proceeded to pick a way to come up with other values by changing the original ones.

I still don't understand your choice of words. It feels like you jumped into epistemology at the deep end and don't know how to swim in it. One article that pops up frequently around here is "Reality has a surprising amount of detail"

http://johnsalvatier.org/blog/2017/reality-has-a-surprising-...

I think you could profit from reading it. And I found "The Golem" a pretty good read too, if you want to go down that path:

https://www.cambridge.org/ch/universitypress/subjects/genera...

The point was that the corrections aren’t arbitrary.

> The point was that the corrections aren’t arbitrary.

But they are, aren't they? It matters nothing if you apply a precise rule to change values. What matters is that you picked a rule.

To add to the "not arbitrary corection" comments.

Correction is also not lying. Anyone who states that is not grasping something about a totally political topic/method.

It’s just reductionism: if changing data is bad, changing data is always bad.

Same nonsense gets you how terrible surgeons are (if cutting people with knives is bad, cutting people with knives is always bad).

It’s a good rhetorical technique because if you remove enough context you can pretty much always find something “exactly the same” to prove a point.

> It’s just reductionism: if changing data is bad, changing data is always bad.

It's not reductionism, and you're missing the point.

The whole point is that changing the original data to be able to support/reject hypothesis naturally raises the question of whether there is foul play.

If original measurements don't support your claim and suddenly by changing data you get it to fit your belief, any rational analysis would quickly flag the risk of data manipulation and scientific malpractice.

If instead you're dealing with a politically charged topic that attracts denialists and contrarians then you're making yourself vulnerable to accusations of fraud that will certainly be used to poison the well.

Do you understand why this is a problem?

> The whole point is that changing the original data to be able to support/reject hypothesis naturally raises the question of whether there is foul play.

People are applying a stronger point here where changing the original data is hard evidence of foul play and sufficient to completely discount any changes.

We'd be grappling with the reality of faster than light neutrinos now, though, if that was correct logic.

> People are applying a stronger point here where changing the original data is hard evidence of foul play and sufficient to completely discount any changes.

Manipulating field measurements is already frowned upon in all applications. There is no point. Things like data provenance is a serious issue, which is directly targeted by peer reviews and investigations on scientific malpractice. Even performance benchmarks highly favor standardized test and data sets.

Being objective matters. Once you start messing with original measurements, you place yourself in a position where you need to answer questions on whether you're just adapting data to fit your belief instead of the other way around.

In this case there is an observable relationship between wind speed and central surface pressure which has been observed over decades, but it has changed between the 1950s and the current day (although it has been stable across recent decades). The difference is in the measurement of wind speeds and with modern dropsondes we have much better measurement of wind speeds than existed in the 1950s. There is a clear and consistent bias in the windspeed-pressure relationship between decades. The correction that has been applied has been to apply a bias to correct the wind speed to the central pressure measurement. That correction was proposed in 2005 and so is nearly 20 years old at this point and had nothing to do with the current paper on cat 6 storms.

> The whole point is that changing the original data to be able to support/reject hypothesis

How do you know this? Do you understand the reasoning behind bias correction?

> Do you understand why this is a problem?

The problem is people do not understand bias correction and you are certainly not arguing on a methological level.

Tough shit. Nature doesn't care about political inconvenience. You must work with your most accurate representation of the data.

I wonder how these comments measure on the Henderson Scale of plot-derailment.

> It really feels like they cajoled the data to fit their conclusion.

(Emphasis mine.)

That Guy!

I could be wrong but I read your post as saying "politics can't deal with progress in scientific insight into the observable universe, thus scientists better be careful what they say." That's medieval. We are better than that... or at least we used to be. Otherwise the sun would still rotate around the earth.

Yup it’s a good way to make it fit the narrative and get more funding.

While I like a good conspiracy, it seems to me as though this was a perfectly appropriate outcome of technological advancement. Surely old storms would be biased high given a variety of reasons.

It's been acknowledged in the meteorological community that reconnaissance wind speed estimates for storms in the 1950s and 1960s have a high bias in the most intense storms, as the field was still in its infancy during that time period. Further advancement in later decades has improved the confidence in the estimates.

No conspiracy needed. After all, the first time a hurricane was intentionally flown into by a storm was only in 1943: https://en.wikipedia.org/wiki/1943_Surprise_Hurricane. The 1950s and 1960s are not that far removed from that initial attempt.