Which makes up a small fraction of those millions of tons of "cheese".

It means those with low yield have a lot of students that apply and get accepted but would rather go to another school, smart ass.

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lmao did you just make a shitty plot from 10 data points and then posted it on data is beautiful?

It depends what you're trying to show. Wind speed vs power for a single turbine is just physics. Weight all of the local wind speeds by the number of turbines on the grid, and you'd get a more precise national estimate. Based on OP's comments, I'd guess that a lot of Germany's wind power comes from Bremen.

OP has produced a bunch of 'some kind of marker for windy day' against 'power from various sources.' The set lets you tell that wind substitutes for petroleum, presumably because gas and oil plants are more dynamic than nuclear or coal. It seems like he's picked a city that's reasonably representative and accessible to humans, rather than a complicated formula that might be more predictive but not useful to readers. It gives the impression that there's got to be a pretty good wind blowing before you get much out of wind.

Might be helpful to have a histogram of wind speed at the bottom, so reader can get some sense of how often wind power is a significant factor.

The cat’s eaten it.

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I mean, I'm just looking at the cheese you eat (stuff like cheddar!).

You might have incredible cheese, but if it's only eaten by 1% of the population, it can't be compared to cheese from other countries that most people eat in these countries.

One taste of swiss cheese and you’ll realize why they don’t make more.

Holds true for almost everything except cell services. The Obama administration put a lot of funding into getting cell towers in rural areas which has definitely contributed to the lower prices we see now. It would be interesting to see how the price of goods/services in government assisted industries change over time, and what could cause the disparities to exist

Wind does blow in only one place and never changes speed so, this checks out.

Averages are fine but the power grid also cannot run on averages. You need supply to equal demand at all times. The spread on this chart is very large, with 10-20GW of difference for any given wind speed.

So while your math is fair, the application of it seems somewhat pointless. It does the grid no good that the average of wind is a certain value, instead the real time value is all that really matters.

Either we invest in more the storage or raise the amount of wind production to raise the lowest minimums, then the math changes. but then the outliers become more critical in analyzing, because failure to account for all outliers results in loss of power.

It looks like there might be a hard floor of 9GW fossil generation that is more apparent at higher wind speeds. Either turning the fossil plants completely off is not allowed by contract or law maybe? Or this grid needs the inertia of these fossil power plants? So, the apparent upswing in the curve fit at very high windspeeds is a bit misleading. Also, extremely high winds might be associated with cold fronts / snaps that cause wind farms to feather their turbine blades or electricity demand to spike.

"just a state" with 4 times the area of Switzerland, Denmark, and the Netherlands.

I remember when we got our first flat screen tv as a kid. 32" Tv (It was huge compared to the tube tv we had). It was over 500 dollars in the late 2000's for the cheapest model (Not sure exactly what year, probably like 2008-2009). Now you can get one for less than 200 dollars

According to the graph, UK and Denmark both produce more than Argentina.

Thanks for you quick answer. I think the picture would look very similar like Südtirol.

Is there any way to filter out posts like this and only get the beautiful data on my feed? Not only is this visually stale, the data itself is uninteresting.

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What is the y-axis based on? I get the logarithmic scaling, but why are the levels in the last part not lining up with the previous 2 parts? and why did you change the baseline (or the change factor from one baseline to the next part)?

In principle if there is a dataset with enough resolution it should be doable. This one had a spatial resolution of 250m x 250m which is why it doubles as a plot of the elevation so well.

In general datasets like this require quite a bit of extra effort to put together since regular measurements for long time periods are not always present in regions with difficult terrain. So for this dataset the group had to do a fair bit of gap filling and spatial interpolation for missing measurements.

Such a high resolution isn’t entirely necessary for large scale weather forecasting so it’s mostly intended for researchers. Sorry for going off on a tangent here but TLDR: yes if someone has put together a similar dataset for all of Austria which I’m not aware of at the moment (would have to look it up)

Of course you can absolutely compute the mean annual temperature even with a low resolution dataset as well if the temperature values are all you’re interested in.

What do you do with something like that jobwise?

Why no GOF report? This doesn't look like a great correlation

It's a visualization that I've never seen in the best graphical analysis pages in the criptocurrencies ecossystem. So I decided to make it on my own and share it. I think it's the best visualization for comparing all the moments since the beginning with the same metrics or time frames. But if it's not, let me know. I'm new here and I appreciate contructible feedbacks.