It is not possible to measure the temperature of the Earth

tgoebbles wrote:[couldn't respond to my lengthy post] I can't make you debate.

tgoebbles wrote: The equilibrium temp of Earth is calculated at [fabrication deleted] if you accept the estimate for the emissivity of Earth. But we've been over all of that before.

tgoebbles wrote:

IBdaMann wrote:What specific occurrence are you now denying occurred?

The ground level of Earth having a mean of [deleted fabrication with of course no margin of error]

tgoebbles wrote: This is simply dead wrong. A margin of error is determined by the data.

IBDaMann wrote: Yes, everyone declares his/her own target, determined by some original requirement for doing the measuring in the first place.

tgoebbles wrote: You have ducked my questions:

tgoebbles wrote:

IBdaMann wrote: earth's temperature at the very large margin of error to which humanity is limited....the margin of error supported by the data...

And what is that? What is "large"? More than 1, 10, 100, is it more than 1000 degrees?

tgoebbles wrote: We both know that you haven't bothered to do any work on this yet you assert your "answer", which is based on your gut feeling, is right.

tgoebbles wrote:

IBdaMann wrote:Raw data is all that matters...I am quite capable of performing my own analysis

Great then how about you share some of each?

tgoebbles wrote: The upper limit of the Sun's energy for a planet is not the temp on the surface of the sun it is the radiance at that distance. But you know that.

IBdaMann[claims tmiddles] couldn't respond to my lengthy post... and I can't make you discuss ...

IBdaMann wrote:Don't think I'm not noticing you doubling down on stupid

IBdaMann wrote:Have you learned "standard deviation" yet?

IBdaMann wrote:You WANT math errors? ... or do you just want more people who are crappy at math?

IBdaMann wrote:

tmiddles wrote: The equilibrium temp of Earth...

There is no such thing as any "estimate"

tmiddles wrote:
T = (temperature of the sun)*1.0(full emissivity)^1/4 * [(radius of sun)/2*distance from the sun]^1/2

Or 5770K*1.0*[(7*10^5)/[2*(1.08*10^8)]]^0.5

5770*(7/2160)^0.5=328K

IBdaMann wrote:You omitted the word "target" ...

Into The Night wrote:The margin of error is a result of the declared variance, not the data.

IBdaMann wrote:

tmiddles wrote: You have ducked my questions:

You are a liar....I'm ignoring your unclear questions.

IBdaMann wrote:
For any given volume, we cannot be accurate without multiple measurements. To get the margin of error for the earth to within +/-20degC you would need many hundreds of millions of calibrated, synchronized and evenly spaced thermometers...

26 SEP 2015IBdaMann wrote:

climate scientist wrote:Determining whether the planet has warmed, is still warming, and will warm in the future is a very complicated task.

No, it's simply not possible. We don't have the hundreds of millions of evenly-spaced, synchronized thermometers at various altitudes that will allow an average measurement to be computed within any usable margin of error

IBdaMann wrote:You are going to select an application...

tgoebbles wrote:You waste so much time and space with insults and BS that IF you actually have a question I don't see one there.

tgoebbles wrote:"Margin of Error" is a well defined term.

tgoebbles wrote:It's this simple: NASA and others have claimed fractional margins of error on their measurement of Earths ground level temp.

tgoebbles wrote: You have said repeatedly on this board:

tgoebbles wrote: How did you come up with "hundred of millions" needed as opposed to some other number.

tgoebbles wrote: You said it, so explain yourself. Margin of error is calculated.

tgoebbles wrote:

IBdaMann wrote:You are going to select an application...

No thanks. I'd be interested to see some of your work though.

tmiddles wrote:...deleted Mantras 29...29...17...21d...10g...15a...15b...20g...20o...25g...20q1...20q2...25f...10g...4a...4b...10g...21d...10g...4f...29...29...29...6...

gfm7175 wrote:
TARGET margin of error ≠ ACTUAL margin of error.

IBdaMann wrote:NASA and others never publish their raw data, ergo all of their claims are dismissed.

IBdaMann wrote:

tmiddles wrote: You have said ...To get within +/-20degC you would need many hundreds of millions of ...thermometers

Exactly ... and you insist that I am WRONG!...Stop running away

IBdaMann wrote:Let's get started. ...You have to ...pick an application

IBdaMann wrote:You omitted the word "target" ...

Into The Night wrote:The margin of error is a result of the declared variance, not the data.

tmiddles wrote:Do you agree that a measurement is ACTUALLY uncertain in how close it is to the true value of what is being measured?

tmiddles wrote: That how close can be described by a probability?

tmiddles wrote:

IBdaMann wrote:NASA and others never publish their raw data, ergo all of their claims are dismissed.

Debunked in my sig.

tmiddles wrote: What I said was first that I cannot myself do the calculation ... [lame excuses deleted]

tmiddles wrote:See above. I'm asking about YOUR claims. You know why you made them. Proceed with explaining what you've already posted here repeatedly.

tmiddles wrote: You did not respond to this:

IBdaMann wrote:You omitted the word "target" ...

No you and ITN did in asserting that is is the RESULT of being "declared". ITN very clearly stated that a Margin Of Error is "declared" and you said he was correct. I said it was calculated from the data and guess what?:

tmiddles wrote:

gfm7175 wrote:
TARGET margin of error ≠ ACTUAL margin of error.

There is a point to that. A printed accuracy can be exceeded by the device so posted as a reliable minimum. Similar to many product claims where you can get sued later if you it falls short.

Do you agree that a measurement is ACTUALLY uncertain in how close it is to the true value of what is being measured? That how close can be described by a probability?

IBdaMann wrote:

tmiddles wrote:Do you agree that a measurement is ACTUALLY uncertain in how close it is to the true value of what is being measured?

...gibbering...Heisenberg...Goedell ...agree?

IBdaMann wrote:...you would thus have uncertainty of your probability

IBdaMann wrote:... pick an application of your choice...

gfm7175 wrote:

tmiddles wrote:
Do you agree that a measurement is ACTUALLY uncertain...

No....

gfm7175 wrote:

tmiddles wrote:
Do you agree that a measurement is ACTUALLY uncertain...

No....

tmiddles wrote:
So you consider measurements to be certain as in perfect? all of them?

tmiddles wrote:
You know GFM you could just say you don't want to debate me and save us both time.

tmiddles wrote:...deleted Mantras 10g...7...10g...29...29...10g...29...

tmiddles wrote:...deleted Mantras 10g...29...10g...25g...29...25c...25g...29...29...17...10h...

tgoebbles wrote:

IBdaMann wrote:... pick an application of your choice...

The application is already on the table. The measurement of the temperature of Earth at ground level

[quote]tgoebbles wrote: The +/- 20 degrees does actually provide useful proof (IF it can be measured) that the ground level of Earth exceeds the equilibrium temp of the planet (-18C).

gfm7175 wrote:[You Can] measure a wall and be 100% confident that the wall is [measured length] +- [instrument tolerance].

IBdaMann wrote:

tmiddles wrote: The +/- 20 degrees does actually provide useful proof (IF it can be measured) that the ground level of Earth exceeds the equilibrium temp of the planet (-18C).

The "Mean Value Theorem" does that.

IBdaMann wrote:

tgoebbles wrote:
[quote]IBdaMann wrote:... pick an application of your choice...

The application is already on the table. The measurement of the temperature of Earth at ground level

tgoebbles wrote: The +/- 20 degrees does actually provide useful proof (IF it can be measured) that the ground level of Earth exceeds the equilibrium temp of the planet (-18C).

The "Mean Value Theorem" does that. When it comes to values, this theorem is mean. Your assertion that the average temperature at the bottom of the atmosphere is higher than the planet's average temperature ... is something that any mathematician will tell you is intuitive and will point to the mean value theorem as being what says that.


.

tmiddles wrote:

gfm7175 wrote:[You Can] measure a wall and be 100% confident that the wall is [measured length] +- [instrument tolerance].

Well it's never 100%. Generally it's 95% or sometimes 99% but never 100.

However I would agree that you can measure a length and be confident (95%) that the measurement error between what your measurement showed and the true value is within a margin.

tmiddles wrote:
Incidentally this is super basic statistics.

tmiddles wrote:
Karl Pearson gave us the standard deviation in 1893 but it was being used long before that under other names. It's got nothing to do with Heisenberg (his uncertainty principle was in 1927 and deals with position and velocity of subatomic particles).

tmiddles wrote:

IBdaMann wrote:

tmiddles wrote: The +/- 20 degrees does actually provide useful proof (IF it can be measured) that the ground level of Earth exceeds the equilibrium temp of the planet (-18C).

The "Mean Value Theorem" does that.

Great please share how you used it to arrive at your solution. (+/- 20 degrees with hundred of millions of thermometers).

Into the Night wrote:
What hundreds of millions thermometers?

IBdaMann wrote:
For any given volume, we cannot be accurate without multiple measurements. To get the margin of error for the earth to within +/-20degC you would need many hundreds of millions of calibrated, synchronized and evenly spaced thermometers...

tmiddles wrote:

Into the Night wrote:
What hundreds of millions thermometers?

That's what I'm asking IBD. He has said repeatedly on the board over the years:
1998 thread:

IBdaMann wrote:
For any given volume, we cannot be accurate without multiple measurements. To get the margin of error for the earth to within +/-20degC you would need many hundreds of millions of calibrated, synchronized and evenly spaced thermometers...

Into the Night wrote:...putting words in his mouth again.

tmiddles wrote:

Into the Night wrote:...putting words in his mouth again.

What? I quoted IBD.

GreenEggsNham wrote:
And, just to add, we are talking about global temperatures being quoted down to 0.01C - that would mean an error of 0.0005C for 95% confidence. Anything less fails the significance test. I am not sure how you could calibrate to and maintain this level of accuracy but your entire setup would need to be at constant temperature and pressure. Just because a machine gives a number does not mean its reliable in any way

DRKTS wrote:

GreenEggsNham wrote:
And, just to add, we are talking about global temperatures being quoted down to 0.01C - that would mean an error of 0.0005C for 95% confidence. Anything less fails the significance test. I am not sure how you could calibrate to and maintain this level of accuracy but your entire setup would need to be at constant temperature and pressure. Just because a machine gives a number does not mean its reliable in any way

There are literally millions of measurements that go into the calculation that is why the results are so consistent. It is a bit more sophisticated than that.....

DRKTS wrote:This is how statistics work.

DRKTS wrote:You have a measurement of (say) 100 but with a random uncertainty of +/-1 then your result is 100 +/-1. Yes?

DRKTS wrote: Now you take 100 different independent readings of that same quantity each with that same random uncertainty.

DRKTS wrote: If the average result is still 100, what do you think the uncertainty is on that average is? Still +/- 1? It is not.

DRKTS wrote:The uncertainty is reduced by the square root of the number of samples. So the result would be 100 +/- 0.1.

DRKTS wrote: There are literally millions of measurements that go into the calculation that is why the results are so consistent.

DRKTS wrote: It is a bit more sophisticated than that because they divide the data into cells so not to bias the result to areas with large numbers of sites (e.g., cities) compared to others with few (oceans).

DRKTS wrote: There is a second independent way to check the result without even knowing the uncertainty on an individual measurement - monte carlo.

DRKTS wrote:This applied to gridded data like the climate data is.

DRKTS wrote:

GreenEggsNham wrote:
And, just to add, we are talking about global temperatures being quoted down to 0.01C - that would mean an error of 0.0005C for 95% confidence. Anything less fails the significance test. I am not sure how you could calibrate to and maintain this level of accuracy but your entire setup would need to be at constant temperature and pressure. Just because a machine gives a number does not mean its reliable in any way

This is how statistics work.

You have a measurement of (say) 100 but with a random uncertainty of +/-1 then your result is 100 +/-1. Yes?

Now you take 100 different independent readings of that same quantity each with that same random uncertainty. If the average result is still 100, what do you think the uncertainty is on that average is? Still +/- 1? It is not.

The uncertainty is reduced by the square root of the number of samples. So the result would be 100 +/- 0.1.

10,000 samples 0.01 and so on.

There are literally millions of measurements that go into the calculation that is why the results are so consistent. It is a bit more sophisticated than that because they divide the data into cells so not to bias the result to areas with large numbers of sites (e.g., cities) compared to others with few (oceans).

There is a second independent way to check the result without even knowing the uncertainty on an individual measurement - monte carlo. This applied to gridded data like the climate data is. Here you divide the data up into cells containing the average temperature data from many different sites (where you have them). You then combine the result from all the different cells to get the average. The uncertainty is then the standard deviation of the average divided by the square root of the number of cells (minus 1).

You then randomly reassign the grid, changing the size and location of the cells and do it again. You do this thousands of times and look at the distribution of the results (usually a bell curve). The peak of the distribution is the overall average and the half width at half maximum is the uncertainty. This technique also allows you to fill in missing or unreliable data and test how that assumption affects your result (increasing the uncertainty). You can also identify results ahat are from bad readings or errors.

You can compare the results of the two methods and you get basically the same result.

GreenEggsNham wrote: Statistical error is not the same as measurement error.

GreenEggsNham wrote: Applying statistical analysis to temperature data is meaningless

DRKTS wrote:

GreenEggsNham wrote:
And, just to add, we are talking about global temperatures being quoted down to 0.01C - that would mean an error of 0.0005C for 95% confidence. Anything less fails the significance test. I am not sure how you could calibrate to and maintain this level of accuracy but your entire setup would need to be at constant temperature and pressure. Just because a machine gives a number does not mean its reliable in any way

This is how statistics work.

You have a measurement of (say) 100 but with a random uncertainty of +/-1 then your result is 100 +/-1. Yes?

Now you take 100 different independent readings of that same quantity each with that same random uncertainty. If the average result is still 100, what do you think the uncertainty is on that average is? Still +/- 1? It is not.

The uncertainty is reduced by the square root of the number of samples. So the result would be 100 +/- 0.1.

10,000 samples 0.01 and so on.

There are literally millions of measurements that go into the calculation that is why the results are so consistent. It is a bit more sophisticated than that because they divide the data into cells so not to bias the result to areas with large numbers of sites (e.g., cities) compared to others with few (oceans).

There is a second independent way to check the result without even knowing the uncertainty on an individual measurement - monte carlo. This applied to gridded data like the climate data is. Here you divide the data up into cells containing the average temperature data from many different sites (where you have them). You then combine the result from all the different cells to get the average. The uncertainty is then the standard deviation of the average divided by the square root of the number of cells (minus 1).

You then randomly reassign the grid, changing the size and location of the cells and do it again. You do this thousands of times and look at the distribution of the results (usually a bell curve). The peak of the distribution is the overall average and the half width at half maximum is the uncertainty. This technique also allows you to fill in missing or unreliable data and test how that assumption affects your result (increasing the uncertainty). You can also identify results ahat are from bad readings or errors.

You can compare the results of the two methods and you get basically the same result.

GreenEggsNham wrote:

DRKTS wrote:

GreenEggsNham wrote:
And, just to add, we are talking about global temperatures being quoted down to 0.01C - that would mean an error of 0.0005C for 95% confidence. Anything less fails the significance test. I am not sure how you could calibrate to and maintain this level of accuracy but your entire setup would need to be at constant temperature and pressure. Just because a machine gives a number does not mean its reliable in any way

This is how statistics work.

You have a measurement of (say) 100 but with a random uncertainty of +/-1 then your result is 100 +/-1. Yes?

Now you take 100 different independent readings of that same quantity each with that same random uncertainty. If the average result is still 100, what do you think the uncertainty is on that average is? Still +/- 1? It is not.

The uncertainty is reduced by the square root of the number of samples. So the result would be 100 +/- 0.1.

10,000 samples 0.01 and so on.

There are literally millions of measurements that go into the calculation that is why the results are so consistent. It is a bit more sophisticated than that because they divide the data into cells so not to bias the result to areas with large numbers of sites (e.g., cities) compared to others with few (oceans).

There is a second independent way to check the result without even knowing the uncertainty on an individual measurement - monte carlo. This applied to gridded data like the climate data is. Here you divide the data up into cells containing the average temperature data from many different sites (where you have them). You then combine the result from all the different cells to get the average. The uncertainty is then the standard deviation of the average divided by the square root of the number of cells (minus 1).

You then randomly reassign the grid, changing the size and location of the cells and do it again. You do this thousands of times and look at the distribution of the results (usually a bell curve). The peak of the distribution is the overall average and the half width at half maximum is the uncertainty. This technique also allows you to fill in missing or unreliable data and test how that assumption affects your result (increasing the uncertainty). You can also identify results ahat are from bad readings or errors.

You can compare the results of the two methods and you get basically the same result.

maybe you should reread my post. I hold a degree in Chemistry and another in Chemical & Process Engineering - as such I am fully aware of the statistics approach.

You are talking about statistical sampling and some kind of normal distribution.

What is the distribution of temperature? Not normal. Its random if sampled once a day, to an extent. But is dependent on what happened earlier when you look at a temperature trace.

This is fine for some things like the size of ball bearings on a production line but not temperatures taken using different equipment and in different places at different time etc. Even for ball bearings you have to determine the type of distribution and then apply the correct statistical formula and hope the distribution does not change.

Statistical error is not the same as measurement error. Statistical error is for the fact that a sample is representing a larger population and the accuracy of your "sample". Whereas I was talking about the experimental error associated with taking a measurement

I fall back to my days in a laboratory studying chemistry. When you take an experimental measurement there is a measurement error, usually +/- half of the graduated scale. So i burette marked at 0.1ml scale has an error of +/- 0.05ml. A pipette has a much lower error, I cannot recall but it depends on the volume.

So when you add experimental data (to get an average) you also have to add the error for each measurement. This is quite logical, ie you collect 10ml from your burette 10 times each 10ml is +/- 0.05ml so you now have 100ml +/-0.5ml
https://thefactfactor.com/facts/pure_science/physics/propagation-of-errors/9502/

Now the concept of average. Again this is fine for measurements of the same thing i.e. temperature in chicago at 3pm every day. But it is not valid to average temperatures in different places. As per my example that average temperature between the equator (at 30C) and Antarctica (at-30C) is meaningless, the average is zero.

You cannot, and I repeat, cannot increase the accuracy of experimental data to below that of the reading error. AND, if you average (the same place, same time etc) the error on your average must not be less than the largest error on your recorded data. Even then a hot or cold change 10 minutes before your recording makes a joke of your data.

Applying statistical analysis to temperature data is meaningless

GreenEggsNham wrote:
This is slightly off topic but still relevant in terms of the way statistics are used.

Maybe our statistical expert DKTRS can explain what is going on here.

It highlights the disconnect between peoples understanding of science (measurements) and statistics (samples) and shows that the famous quote is still valid today.

"Lies, damned lies, and statistics"

NSIDC report sea ice extent for the poles. In both regions they ignore one of the fundamental principles of science - present all of the data. If there are errors, talk about them and explain.

So they have averages for various time periods. 1979-2009, 1981-2010, 1991-2000, 2001-2010, 2011-2019.
https://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/

So why dont the present one average for all of the data 1979-2019? They appear to believe that by showing recent data in 10 year groups they can highlight warming. While ignoring that selectively averaging data to suit you agenda is data fraud.

They supply a 2 standard deviations option but this is based only on the 1979-2009 data and so does not represent the data in full. As such this band does not represent that actual variance of the collected data.

Why? [ok, I didnt need to ask that]

Because when you examine all of the data is is 100% clear that despite claims of massive ice lost from the Arctic, the data does not support this claim. Sure there is the occasional year when the summer minimum and winter maximum dip outside the 2 standard deviation zone, but there are also years when the minimum and maximum are high but we never hear this. If the standard deviations included ALL DATA, as it should do, this would not be the case.

They are denying statistics in the belief that a ten year sample can be taken as proof of a warming claim by ignoring that without incorporating all data they are cutting off the natural variability that the data reflects. This is the thing, statistical analysis allows for variance and that is why changes in data need to reach a certain level before they become significant - apparently not when it comes to proving warming, it is always significant even when its not.

If you select "show all years" and overlay the 2 SD band the data during the summer dip is nowhere near the upper limit - this is impossible, unless the 2 SD band has been fudged. And when you look at the bottom of the band it becomes obvious the band has been shifted upwards or is only valid for 1979-2009.

When you have 40 years of data, to present it in 10 year chunks is nothing more than deception if you dont also present the all data average and corresponding standard deviation band for this data.

And then there is the assumption that sea ice at the poles follows a normal distribution....mmm
And that 40 years of data is definitive of changes....
And that satellite data started in 1973 but they dont want to disclose that data....

Into the Night wrote:

GreenEggsNham wrote:
This is slightly off topic but still relevant in terms of the way statistics are used.

Maybe our statistical expert DKTRS can explain what is going on here.

It highlights the disconnect between peoples understanding of science (measurements) and statistics (samples) and shows that the famous quote is still valid today.

"Lies, damned lies, and statistics"

NSIDC report sea ice extent for the poles. In both regions they ignore one of the fundamental principles of science - present all of the data. If there are errors, talk about them and explain.

So they have averages for various time periods. 1979-2009, 1981-2010, 1991-2000, 2001-2010, 2011-2019.
https://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/

So why dont the present one average for all of the data 1979-2019? They appear to believe that by showing recent data in 10 year groups they can highlight warming. While ignoring that selectively averaging data to suit you agenda is data fraud.

They supply a 2 standard deviations option but this is based only on the 1979-2009 data and so does not represent the data in full. As such this band does not represent that actual variance of the collected data.

Why? [ok, I didnt need to ask that]

Because when you examine all of the data is is 100% clear that despite claims of massive ice lost from the Arctic, the data does not support this claim. Sure there is the occasional year when the summer minimum and winter maximum dip outside the 2 standard deviation zone, but there are also years when the minimum and maximum are high but we never hear this. If the standard deviations included ALL DATA, as it should do, this would not be the case.

They are denying statistics in the belief that a ten year sample can be taken as proof of a warming claim by ignoring that without incorporating all data they are cutting off the natural variability that the data reflects. This is the thing, statistical analysis allows for variance and that is why changes in data need to reach a certain level before they become significant - apparently not when it comes to proving warming, it is always significant even when its not.

If you select "show all years" and overlay the 2 SD band the data during the summer dip is nowhere near the upper limit - this is impossible, unless the 2 SD band has been fudged. And when you look at the bottom of the band it becomes obvious the band has been shifted upwards or is only valid for 1979-2009.

When you have 40 years of data, to present it in 10 year chunks is nothing more than deception if you dont also present the all data average and corresponding standard deviation band for this data.

And then there is the assumption that sea ice at the poles follows a normal distribution....mmm
And that 40 years of data is definitive of changes....
And that satellite data started in 1973 but they dont want to disclose that data....

This gets into the 'what is change' question of how to define the phrase 'global warming' or 'climate change'. A change is a delta, measured between two points in time. They cannot seem to figure out what two points in time to use, why those two points in time are significant, or why any other two points in time are not significant.

Until one can declare these and justify them, 'warming' or 'cooling' or 'change' don't mean anything.

James___ wrote:

Into the Night wrote:
This gets into the 'what is change' question of how to define the phrase 'global warming' or 'climate change'. A change is a delta, measured between two points in time. They cannot seem to figure out what two points in time to use, why those two points in time are significant, or why any other two points in time are not significant.

Until one can declare these and justify them, 'warming' or 'cooling' or 'change' don't mean anything.

And we're back to the "No Fun Zone".

gfm7175 wrote:

James___ wrote:

Into the Night wrote:
This gets into the 'what is change' question of how to define the phrase 'global warming' or 'climate change'. A change is a delta, measured between two points in time. They cannot seem to figure out what two points in time to use, why those two points in time are significant, or why any other two points in time are not significant.

Until one can declare these and justify them, 'warming' or 'cooling' or 'change' don't mean anything.

And we're back to the "No Fun Zone".

... or otherwise known as the "Science Zone". Maybe it's "no fun" to you since you don't understand it very well?? At least back when I had MUCH less understanding of it, I didn't find it to be very "fun" either.

James___ wrote:

gfm7175 wrote:

James___ wrote:

Into the Night wrote:
This gets into the 'what is change' question of how to define the phrase 'global warming' or 'climate change'. A change is a delta, measured between two points in time. They cannot seem to figure out what two points in time to use, why those two points in time are significant, or why any other two points in time are not significant.

Until one can declare these and justify them, 'warming' or 'cooling' or 'change' don't mean anything.

And we're back to the "No Fun Zone".

... or otherwise known as the "Science Zone". Maybe it's "no fun" to you since you don't understand it very well?? At least back when I had MUCH less understanding of it, I didn't find it to be very "fun" either.

And yet IBDM's post from another "quadrant" is one of my favourite posts in this forum. I've noticed that no one found it comparable to a view from Mars let alone another part of space.
IBDM, we might have a problem. I simply loved your post. And yet no one can imagine a view from another planet and much less from the Delta Quadrant. And in math, all Delta means is change from A to B. What is the difference between them? That is Delta.
It's possible that your post is what this forum needed. That is why some people like me like science.

p.s., in Star Trek Voyager, Delta is referenced as light years from the Earth. Why the Delta Quadrant has defined parameters. And in math, Delta is represented as a triangle. And yet light years is a linear value. If you want to discuss this association we can. But then we'd be getting into the No Fun Zone.

Into the Night wrote:

James___ wrote:

gfm7175 wrote:

James___ wrote:

Into the Night wrote:
This gets into the 'what is change' question of how to define the phrase 'global warming' or 'climate change'. A change is a delta, measured between two points in time. They cannot seem to figure out what two points in time to use, why those two points in time are significant, or why any other two points in time are not significant.

Until one can declare these and justify them, 'warming' or 'cooling' or 'change' don't mean anything.

And we're back to the "No Fun Zone".

... or otherwise known as the "Science Zone". Maybe it's "no fun" to you since you don't understand it very well?? At least back when I had MUCH less understanding of it, I didn't find it to be very "fun" either.

And yet IBDM's post from another "quadrant" is one of my favourite posts in this forum. I've noticed that no one found it comparable to a view from Mars let alone another part of space.
IBDM, we might have a problem. I simply loved your post. And yet no one can imagine a view from another planet and much less from the Delta Quadrant. And in math, all Delta means is change from A to B. What is the difference between them? That is Delta.
It's possible that your post is what this forum needed. That is why some people like me like science.

p.s., in Star Trek Voyager, Delta is referenced as light years from the Earth. Why the Delta Quadrant has defined parameters. And in math, Delta is represented as a triangle. And yet light years is a linear value. If you want to discuss this association we can. But then we'd be getting into the No Fun Zone.

Delta is simply a Greek letter. It is the fourth letter of their alphabet. In mathematics, it is used to denote a small change. It usually shows up when a value is changing because another independent variable is changing. Yes, the Greeks draw the delta character as a triangle.
The word DELTA is also the ICAO phonetic standard word for the letter 'D', so Delta Quadrant is simply D Quadrant.

It sounds cool, which is why scifi shows like Star Trek tend to use it.

ICAO phonetic alphabet:
Alpha (greek letter)
Bravo (pilots are brave! HAR!)
Charlie (Everyone knows a Charlie)
Delta (greek letter)
Echo (what you get when your radio is in need of repair)
Foxtrot (why would trotting fox have anything to do with airplanes or ships??)
Golf (I guess pilots like to golf!)
Hotel (where most pilots live)
India (honorable mention?)
Juliet (favorite girlfriend?)
Kilo (what you smuggle with your airplane)
Lima (some pilots are too full of beans)
Mike (everyone knows a Mike)
November (what it looks like when you're in a rain cloud)
Oscar (a brand of hot dog when a pilot wants to splurge for his meal)
Papa (a bear referred to by truckers, when describing a State trooper).
Quebec (an easy to remember place, where they tend to discourage the use of English, including phonetic alphabets like this one)
Romeo (goes with Juliet)
Sierra (for western pilots)
Tango (this was actually supposed to be 'tangled', but their tongue got tied)
Uniform (the spiffy outfit worn by airline pilots until they find out how much the job sucks and they quit)
Victor (old WW2 acronym, short for 'victorious')
Whiskey (what to drink if you make it down without killing yourself or anyone else)
Xray (better sounding they xylophone)
Yankee (for all pilots but those in the southeast U.S., where its an insult instead. Southeastern pilots sometimes use Yeeha or Y'all)
Zulu (a place not in the U.S., and a timezone not in the U.S. Go figure)

For digits, they use:
One
Two
Three
Four
Five
Six
Seven
Eight
Niner (Nine sounds too much like Five on crappy radios, such as what aircraft use)
Zero

Pilots are otherwise an informal bunch, and so are air traffic controllers. Instead of 'over' they just stop talking. Instead of 'out' they just say 'seeya', or similar wording, if they say anything at all.

Current METAR at SeaTac airport:
KSEA 292353Z 21013KT 10SM FEW030 SCT100 BKN250 22/13 A2998 RMK AO2 SLP158 T02170128 10222 20156 56009
or:
KSEA automated weather observation two niner two three five three zulu. Wind, two one zero at one three, ten miles visibility, sky condition: few three thousand, scattered one zero thousand, broken two five zero thousand. Temperature two two, dew point three. Altimeter two niner niner eight.

More than once I've heard a controller at an air show get on the horn and ask, "Red and white Cessna, what are you doing..??".

Never a good sign.

Into the Night wrote: Delta is simply a Greek letter. It is the fourth letter of their alphabet. In mathematics, it is used to denote a small change.

Into the Night wrote: Pilots are otherwise an informal bunch, and so are air traffic controllers.