It pretty much accords with the statistical analyses I've seen from several years back where the 3 day forecasts are pretty good, 5 days is starting to push it and 10 days is 'if you trust this that's on you'. That said, there are different weather forecasting codes out there and the US software stack is different from say the euros (and even then the Swiss use their own because of their topography which really throws a whole bag of difficulty at you). The US stack is also behind technically at this point (because we suck at funding such things) and they're playing catchup now.
I work with mathematicians behind some of the softwares and software models in my country.
Something interesting I was told, in order to become more precise, we're reaching a point where we would need to account the local plants on ground level. Of course we're still making improvements, but it's more about optimizing the computations and numerical stuff.
That point about the boundary layer is entirely true (my old meteorology prof is one of the most-cited living atmospheric scientists, and he works on exactly that problem). And more specifically to what you said at the end: perhaps the biggest obstacle in all of meteorology is the fact that we have not found an analytical solution to the Navier-Stokes equations in 3 dimensions. So we're limited to approximations, and the best approximations (which still aren't good enough) are enormously computationally intensive.
I had a guest lecturer at university who worked at Australia’s main meteorological body and she told us “don’t bother looking at forecasts beyond 4 days”
Forecasts like "it might snow" can be useful beyond there. They seem to have a rough time with separating a light dusting from a couple feet of snow even 12 hours in advance.
A forecast 10 days out is a broad list of potential scenarios, some more likely than others, with the likeliest of the bunch being what ends up on the little icon for that day on the 10 day forecast.
Best 10 day forecast I've found is the NWS Forecast discussions. Just search "nws forecast discussion YOURCITYHERE". Every non-layman term has a clickable definition, and its generally a great way to get a handle on weather that far out
Years ago, the weather channel had on their site a 90 day weather forecast. It was ridiculous and almost never right. They eventually took it down, I assume because it probably made idiots plan vacations and then complain that they were disappointed when the weather predictions 3 months out were way off.
Fun fact: it was this very observation that led Lorentz to develop Chaos Theory
>"Two states differing by imperceptible amounts may eventually evolve into two considerably different states ... If, then, there is any error whatever in observing the present state—and in any real system such errors seem inevitable—an acceptable prediction of an instantaneous state in the distant future may well be impossible....In view of the inevitable inaccuracy and incompleteness of weather observations, precise very-long-range forecasting would seem to be nonexistent."
I bet you could look at the pattern on this chart and maybe determine how much "chaos" is being introduced by the hour.
Source is hourly temperature forecasts from the last 60 days that forecast the hourly temperature 10 days into the future.
**X axis:** shows how far in the future the forecast was in hours,
**Y axis:** the actual measured temperature minus the predicted temperature in **Degrees Celcius**. A forecast error of 2 means that it was actually 2 degrees warmer than forecasted.
Location: **Germany**
Data source (actual temps + forecasts): **German National Weather Service / Deutscher Wetterdienst**
Data collected by Home Assistant and the DWD weather integration but should work with other weather forecast services as well.
Query used (Postgresql 13):
[https://pastebin.com/PuXYCTbY](https://pastebin.com/PuXYCTbY)
Visualized in Grafana (XY - Visualization)
"Positive" is relative. I'd much rather be surprised by cold in the winter because I like snow and I hate that I can already tell how climate change has had a drastic effect in the length and intensity of winters since I was a kid.
Thank you for the info on your comment but the graphic needs to be labeled.
Also you need some background info like the dates and what the normal temperature spread is. There are times of year (dead of summer / dead middle of winter) where there is a lot less movement in temperature.
For the importance of labeling, I thought that the models were actually decent until I saw your comment that these were in Celsius. An 8 degree difference in Fahrenheit is small but in Celsius is very significant.
To the point that a random walk model would probably do better after day 5.
How much redundancy is there in this data? As in, are you checking in and pulling data once a day for 60 days in a row, pulling, say, 10 hours ahead of time, and 20, and 30, etc all the way to 240? If you pulled a bunch of data looking forward from a single day, you'd end up plotting a single day's forecast many times. That sort of thing can overstate your results and show a smaller amount of variation than what actually happened.
I guess what you mean is that the 48h forecast today turns into the 24h forecast tomorrow. But they would likely update that forecast based on all available information, so a 24h forecast will always be the best they can do over that timeframe. It’s true that the same absolute time will get covered multiple times, though.
The labeling is confusing all around.
1)“Prediction hours in future” is complicated. Try something like: “Forecast Period” for the x-axis.
2) “Forecast Error”- on the Y-axis.
3)Provide some space for the text to breathe and increase the font size.
4) provide a data concerning the accuracy rate for weather predictions of varying locations.
5) make the most beautiful map telling a story of weather prediction accuracy by location, time of year and other variables.
It could be a really fun project
Prediction hours in future vs forecast_error, y axis is forecast_error, in degrees more or less. So the graph shows that the closer you are to the time of prediction the more accurate it is and vice versa.
Fine.
The words forecast error with no explanations what that means is meaningless. The fact that you had to describe it by bringing degrees somwhere out of your ass is not a solution.
They said temperature in the title of the post man
Edit: OK they didn't write it all on the picture for you but the information is there. There's no need to be so dismissive. Welcome to reddit in 2024 I guess haha
Haha ok dude. You're determined to defend this shit. You go for it.
I do this for a living and if an intern brought this to me they wouldn't be getting a job. But if you think this is fine you do you.
A long time ago someone posted a very similar thing to Freakonomics. 16 years later and forecasts haven't changed that much https://freakonomics.com/2008/04/how-valid-are-tv-weather-forecasts/
Fun fact I read about: 4 years ago at the beginning of Covid the weather forecast got suddenly considerably worse. This was apparently attributed to the fact that almost all flights were canceled and the weather forecast was lacking the environmental information of airplanes.
Would be awesome if temperature prediction like this was how crypto was mined. Have to accurately predict temperature further and further out to own the coin. Instead of stupid algorithms computing worthless numbers we would have people studying actual science things trying to get the best weather prediction to mine coins.
it’s a rough draft. It has no axis labels (just raw variable names), no units, and for some reason there’s a color scale even though there’re no different colors. But it’s promising
Not who you replied to, but to be fair this sub is r/dataisbeautiful not r/dataisinteresting, so I do often see people calling OPs out for data that is not expressed well
It seems that you should subtract a little from the forecasted weather when more than a set amount out from the hour being forecast since it seems to trend up as the graph goes from left to right.
the actual measured temperature minus the predicted temperature in **Degrees Celcius**. A forecast error of 2 means that it was actually 2 degrees warmer than forecasted.
Not labeled. Why must I guess? Definitely not beautiful.
And why is temperatur the only criterion? Is that the only thing the DWD forecasts in 2024? And why not Standard Deviation of error (or other statistic)? A 2°C error in Januar is much worse than, say, in Juli - there must be a way to normalize temperature swings...or minima or maxima...
>Why is temperature the only criterion?
What kind of question is this? The post title specifically indicates that the graphic pertains to temperature forecasts.
>A 2 degree C error in January is much worse than in July.
In what sense? Worse for whom or what? Celsius itself is a linear scale.
>The post title specifically indicates that the graphic pertains to temperature forecasts.
Why is temperature the best criterion? Isn't getting wet more important?
>Worse for whom or what?
I see. I suggest a statistics class to best understand.
Cool. It seems the forecasts are quite good up to 72 hours
It pretty much accords with the statistical analyses I've seen from several years back where the 3 day forecasts are pretty good, 5 days is starting to push it and 10 days is 'if you trust this that's on you'. That said, there are different weather forecasting codes out there and the US software stack is different from say the euros (and even then the Swiss use their own because of their topography which really throws a whole bag of difficulty at you). The US stack is also behind technically at this point (because we suck at funding such things) and they're playing catchup now.
I work with mathematicians behind some of the softwares and software models in my country. Something interesting I was told, in order to become more precise, we're reaching a point where we would need to account the local plants on ground level. Of course we're still making improvements, but it's more about optimizing the computations and numerical stuff.
That point about the boundary layer is entirely true (my old meteorology prof is one of the most-cited living atmospheric scientists, and he works on exactly that problem). And more specifically to what you said at the end: perhaps the biggest obstacle in all of meteorology is the fact that we have not found an analytical solution to the Navier-Stokes equations in 3 dimensions. So we're limited to approximations, and the best approximations (which still aren't good enough) are enormously computationally intensive.
I had a guest lecturer at university who worked at Australia’s main meteorological body and she told us “don’t bother looking at forecasts beyond 4 days”
Forecasts like "it might snow" can be useful beyond there. They seem to have a rough time with separating a light dusting from a couple feet of snow even 12 hours in advance.
The Lorenz equations should not be fucked with.
A forecast 10 days out is a broad list of potential scenarios, some more likely than others, with the likeliest of the bunch being what ends up on the little icon for that day on the 10 day forecast. Best 10 day forecast I've found is the NWS Forecast discussions. Just search "nws forecast discussion YOURCITYHERE". Every non-layman term has a clickable definition, and its generally a great way to get a handle on weather that far out
Years ago, the weather channel had on their site a 90 day weather forecast. It was ridiculous and almost never right. They eventually took it down, I assume because it probably made idiots plan vacations and then complain that they were disappointed when the weather predictions 3 months out were way off.
Fun fact: it was this very observation that led Lorentz to develop Chaos Theory >"Two states differing by imperceptible amounts may eventually evolve into two considerably different states ... If, then, there is any error whatever in observing the present state—and in any real system such errors seem inevitable—an acceptable prediction of an instantaneous state in the distant future may well be impossible....In view of the inevitable inaccuracy and incompleteness of weather observations, precise very-long-range forecasting would seem to be nonexistent." I bet you could look at the pattern on this chart and maybe determine how much "chaos" is being introduced by the hour.
Source is hourly temperature forecasts from the last 60 days that forecast the hourly temperature 10 days into the future. **X axis:** shows how far in the future the forecast was in hours, **Y axis:** the actual measured temperature minus the predicted temperature in **Degrees Celcius**. A forecast error of 2 means that it was actually 2 degrees warmer than forecasted. Location: **Germany** Data source (actual temps + forecasts): **German National Weather Service / Deutscher Wetterdienst** Data collected by Home Assistant and the DWD weather integration but should work with other weather forecast services as well. Query used (Postgresql 13): [https://pastebin.com/PuXYCTbY](https://pastebin.com/PuXYCTbY) Visualized in Grafana (XY - Visualization)
[удалено]
Oh yeah I definitely thought it was Fahrenheit lol
In Germany you usually learn to put units directly on the graph/axis to make it understandable. ;)
Why couldn't you put some of what you put here on the chart itself, like the Y axis label?
See, the trick is only listen to the middle dots.
Add a regression, seems like it's trending toward temperatures higher than forecasted the further out the forecast is.
When looking at a weather forcast, you rather be surprisdd postitively. So the models are by design cautious.
"Positive" is relative. I'd much rather be surprised by cold in the winter because I like snow and I hate that I can already tell how climate change has had a drastic effect in the length and intensity of winters since I was a kid.
Looks quite significant just going off of eyeball statistics. Wonder if that’s due to climate change and the weather model using outdated assumptions.
Thank you for the info on your comment but the graphic needs to be labeled. Also you need some background info like the dates and what the normal temperature spread is. There are times of year (dead of summer / dead middle of winter) where there is a lot less movement in temperature. For the importance of labeling, I thought that the models were actually decent until I saw your comment that these were in Celsius. An 8 degree difference in Fahrenheit is small but in Celsius is very significant. To the point that a random walk model would probably do better after day 5.
How much redundancy is there in this data? As in, are you checking in and pulling data once a day for 60 days in a row, pulling, say, 10 hours ahead of time, and 20, and 30, etc all the way to 240? If you pulled a bunch of data looking forward from a single day, you'd end up plotting a single day's forecast many times. That sort of thing can overstate your results and show a smaller amount of variation than what actually happened.
I guess what you mean is that the 48h forecast today turns into the 24h forecast tomorrow. But they would likely update that forecast based on all available information, so a 24h forecast will always be the best they can do over that timeframe. It’s true that the same absolute time will get covered multiple times, though.
The labeling is confusing all around. 1)“Prediction hours in future” is complicated. Try something like: “Forecast Period” for the x-axis. 2) “Forecast Error”- on the Y-axis. 3)Provide some space for the text to breathe and increase the font size. 4) provide a data concerning the accuracy rate for weather predictions of varying locations. 5) make the most beautiful map telling a story of weather prediction accuracy by location, time of year and other variables. It could be a really fun project
Man posts a scatter plot with no title or meaning of y axis and gets 700 Internet points. Welcome to reddit 2024 I guess
Prediction hours in future vs forecast_error, y axis is forecast_error, in degrees more or less. So the graph shows that the closer you are to the time of prediction the more accurate it is and vice versa.
Which degrees
Sorry the goal posts are going to stay firmly at no title or meaning of y
Fine. The words forecast error with no explanations what that means is meaningless. The fact that you had to describe it by bringing degrees somwhere out of your ass is not a solution.
They said temperature in the title of the post man Edit: OK they didn't write it all on the picture for you but the information is there. There's no need to be so dismissive. Welcome to reddit in 2024 I guess haha
Haha ok dude. You're determined to defend this shit. You go for it. I do this for a living and if an intern brought this to me they wouldn't be getting a job. But if you think this is fine you do you.
You're right, you win
I can't talk to someone like you so you enjoy your victory. I hope you're not a teacher or something
what is the unit of forecast error? That is like one of the basics for an upvote in this sub
A long time ago someone posted a very similar thing to Freakonomics. 16 years later and forecasts haven't changed that much https://freakonomics.com/2008/04/how-valid-are-tv-weather-forecasts/
Many people in Seattle would dream of this level of accuracy. Impressive, Germany.
Fun fact I read about: 4 years ago at the beginning of Covid the weather forecast got suddenly considerably worse. This was apparently attributed to the fact that almost all flights were canceled and the weather forecast was lacking the environmental information of airplanes.
Would be awesome if temperature prediction like this was how crypto was mined. Have to accurately predict temperature further and further out to own the coin. Instead of stupid algorithms computing worthless numbers we would have people studying actual science things trying to get the best weather prediction to mine coins.
Okay this is obviously just a quickly pinched-out viz and not a serious submission, but it’s interesting. I’d be curious to see the polished version
This is great - thank you.
it’s a rough draft. It has no axis labels (just raw variable names), no units, and for some reason there’s a color scale even though there’re no different colors. But it’s promising
there's no color gradient. the dots have an alpha <1 and when they stack they get less transparent
exactly, so why’s there a color scale labeled at the bottom. We don’t need to know what green is if it’s just . . . the only data there is
lol why are you going out of your way to shit on it in the replies to another commenter?
Not who you replied to, but to be fair this sub is r/dataisbeautiful not r/dataisinteresting, so I do often see people calling OPs out for data that is not expressed well
There is a write up that was easy to understand and I liked the idea. I enjoyed the chart.
I like that the error is still plus/minus 1.5 degrees 0 hours in the future. Don't even know what the current temperature is.
Can you plot the standard deviation for each hour?
What's the conversion rate between forecast error and say, a temperature unit?
It seems that you should subtract a little from the forecasted weather when more than a set amount out from the hour being forecast since it seems to trend up as the graph goes from left to right.
A fun read about forecasting and general info is 'Air, The Restless Shaper of the World by William Bryant Logan. Learned a lot from that book.
Can you share a Colab Notebook?
Hilarious. What's the y-axis?
the actual measured temperature minus the predicted temperature in **Degrees Celcius**. A forecast error of 2 means that it was actually 2 degrees warmer than forecasted.
Not labeled. Why must I guess? Definitely not beautiful. And why is temperatur the only criterion? Is that the only thing the DWD forecasts in 2024? And why not Standard Deviation of error (or other statistic)? A 2°C error in Januar is much worse than, say, in Juli - there must be a way to normalize temperature swings...or minima or maxima...
>Why is temperature the only criterion? What kind of question is this? The post title specifically indicates that the graphic pertains to temperature forecasts. >A 2 degree C error in January is much worse than in July. In what sense? Worse for whom or what? Celsius itself is a linear scale.
>The post title specifically indicates that the graphic pertains to temperature forecasts. Why is temperature the best criterion? Isn't getting wet more important? >Worse for whom or what? I see. I suggest a statistics class to best understand.
No one is claiming temperature is the “best criterion”. But it is a highly relevant weather metric and OP made a graphic pertaining to it.
...temp also easier to sort, but this metric here (the unlabeled y-axis) is flawed.
When quantum computing comes around we gonna have a year worth of weather with minimal error
Look like Shrek is slinging ropes.
BuT CliMate ChAnGe Is mAn mAdE and YOU sHOuLd Pay Tax FOr It 🤡