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Weighting grid points to account for the areal coverage of the grid point is absolutely not called "normalization". I do not know for sure that it is called "homogenization", but I believe that it is.

Normalization is, at its most basic, scaling variables by their standard deviations to allow for statistical intercomparisons.

Weighting is a simple form of extrapolation. It is mathematically identical to extrapolating a single value to a larger continuous region, doing that for each data point, and then taking an average (via integration) over the whole globe.

I'm pretty sure it's not homogenization.

I thought geographic normalization was a form of weighting, just sort of reversed? I believe used I've used both terms interchangeably, but if I'm wrong that's fine (I'm not a geography major). The point I was making is unchanged.

http://dauofu.blogspot.com/2013/02/normalizing-geographic-data.html?m=1

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I'm pretty sure it's not homogenization.

I thought geographic normalization was a form of weighting, just sort of reversed? I believe used I've used both terms interchangeably, but if I'm wrong that's fine (I'm not a geography major). The point I was making is unchanged.

http://dauofu.blogspot.com/2013/02/normalizing-geographic-data.html?m=1

 

I've never heard the term "geographic normalization" before. It's possible that it is a term used in geography, though I suspect the blog you linked uses the term "normalization" merely as a descriptive term (rather than a mathematical/scientific one). Either way, in the field of meteorology/climatology, as far as I'm aware, normalization refers to the statistical definition.

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You said that they simply average all 85 stations and there is no area weighting. That is false. Why won't you acknowledge this?

Where did I say they did no areal weighting? What I said was that they don't do anything in the way of spatial/grid homogenization, like GISS/NCDC et al do to make the data representative of reality. The RATPAC data is just calculated in a field of large grid boxes.

Second of all, it is homogenizing. Homogenizing is, broadly, the removal of non-climactic signals. Any attempt to area weight the data is the removal of a non-climactic signal.

How is that homogenizing? You're not removing anything from the data or measurements. Homogenizing is the process of removing faulty/contaminated data from individual radiosondes or stations due to factors internal or external to the instrument itself. Gridding the data isn't "homogenizing" it.

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I've never heard the term "geographic normalization" before. It's possible that it is a term used in geography, though I suspect the blog you linked uses the term "normalization" merely as a descriptive term (rather than a mathematical/scientific one). Either way, in the field of meteorology/climatology, as far as I'm aware, normalization refers to the statistical definition.

Thanks for the heads up. Yeah I'm pretty sure geographic normalization is a real term (it was taught in one of my undergrad paleo/geo classes years ago). Definitely not the same thing as a statistical normalization.

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Who would you care to reply to? There are not that many active posters on here.

I respect and enjoy learning from everyone here, minus two, hailman being one of them. Nothing too personal.

I love you, skier, mallow, ORH, and TGW, regardless of the disagreements we have. I'm sure it's not mutual, but that doesn't matter to me. The fact that I can discuss climate science with others who are just as into it as me is as rewarding as it gets.

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I have SOC on ignore since his posts lately have become unbearable to read. I encourage others to do the same.

Some of his replies seem to be arguing for the sake of arguing. When you seem to need get the last word every single time it begins to become aggravating. Also it seems to discourage posting from some posters who if they stopped posting would be a major loss for the forum. Skier and dons are two of the best posters we have (IMO) and I noticed a lot of what seemed to be nitpicking replies to nearly every single post by them. Agreeing to disagree sometimes is the better path than beating someone over the head with your opinion until they just quit posting.

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I respect and enjoy learning from everyone here, minus two, hailman being one of them. Nothing too personal.

I love you, skier, mallow, ORH, and TGW, regardless of the disagreements we have. I'm sure it's not mutual, but that doesn't matter to me.

Very well. Its not like this board will make a difference in the grand scheme of climate policy or anything. Putting it in perspective is important. Sometimes being anonymous

allows people to be more bold or arrogant than they otherwise would be. My rule of thumb; treat others as if you were having an in face conversation with them, and all should be better.

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How is that homogenizing? You're not removing anything from the data or measurements. Homogenizing is the process of removing faulty/contaminated data from individual radiosondes or stations due to factors internal or external to the instrument itself. Gridding the data isn't "homogenizing" it.

 

False. Homogenizing is any processing step that attempts to improve the data by removing a non-climactic signal. For example, if the data is too heavily weighted to north America, area weighting the data would remove that non-climactic signal. 

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Where did I say they did no areal weighting? What I said was that they don't do anything in the way of spatial/grid homogenization, like GISS/NCDC et al do to make the data representative of reality. The RATPAC data is just calculated in a field of large grid boxes.

 

 

 

Right here. You said they "merely take the data from the 85 stations and average it out." You also said they "do nothing in the way of gridding." Both statements are blatantly false. They use the data to define trends for 36 grids and then do an area-weighted average of those 36 grids. 

 

 

Actually, I made a mistake. RATPAC does nothing in the way of gridding or spatial homogenization at all. They merely take the data from the 85 stations and average it out. Wow..that's just an awful way to go about this.

 

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Right here. You said they "merely take the data from the 85 stations and average it out." You also said they "do nothing in the way of gridding." Both statements are blatantly false. They use the data to define trends for 36 grids and then do an area-weighted average of those 36 grids.

No, you misinterpreted me because you're misinterpreting the definition of homogenization. I didn't say they did no gridding, rather I said there is no homogenization (spatial or to the grids) procedure carried out. Merely placing data into grid cells to account for uneven spatial distribution of measurement stations is not considered "homogenization" because there is no faulty, unrepresentative data in the aggregate itself. The areal plane of measurement is being extrapolated..the data itself is not being changed in any way.

I said they do no homogenization of the grid network, or in other words, a smoothing/extrapolation of data between/within the grid cells to reflect variability over distance.

See below:

What I said was that they don't do anything in the way of spatial/grid homogenization, like GISS/NCDC et al do to make the data representative of reality. The RATPAC data is just calculated in a field of large grid boxes

Actually, I made a mistake. RATPAC does nothing in the way of gridding or spatial homogenization at all. They merely take the data from the 85 stations and average it out. Wow..that's just an awful way to go about this.

"Average it out", as in, over distance in equally-sized, full-panning grid boxes. I don't know why I even have to explain this.
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No, you misinterpreted me because you're misinterpreting the definition of homogenization. I didn't say they did no gridding, rather I said they did no spatial or gridding homogenization. Merely placing data into grid cells to account for uneven spatial distribution of measurement stations is not considered "homogenization" because there is no faulty, unrepresentative data in the aggregate itself. The areal plane of measurement is being extrapolated..the data is not being changed in any way.

I said they do no homogenization of the grid network, or in other words, a smoothing/extrapolation of data between/within the grid cells to reflect variability over distance.

See below:

"Average it out", as in, over distance in basic, untouched grid boxes. I don't know why I even have to explain this.

 

First of all, the way you're defining homogenization doesn't even fit the way you're using it. You're saying it's only homogenization if you're removing faulty data. But then you say that they aren't doing homogenization because there is no extrapolation between grid cells (which there is). So you're contradicting yourself.

 

Second, your definition of homogenization is wrong. Homogenization is the removal of non-climactic signals. Processing the data, by example area weighting, fits such a definition.

 

Third, you said specifically that they simply average the 85 stations. That is blatantly false. You have not addressed this falsity at all.

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First of all, the way you're defining homogenization doesn't even fit the way you're using it. You're saying it's only homogenization if you're removing faulty data. But then you say that they aren't doing homogenization because there is no extrapolation between grid cells (which there is). So you're contradicting yourself.

The only extrapolation done is within the grid boxes. Every grid is equally-sized and on the same plane, so tens of millions of square kilometers go unmeasured/unrepresented because there's no nearby data. Climate change has varied significantly by region/locality since measurement began, so this is a problem.

Second, your definition of homogenization is wrong. Homogenization is the removal of non-climactic signals. Processing the data, by example area weighting, fits such a definition.

Extrapolating data isn't homogenizing it. If you want to believe otherwise, go ahead. I don't really care.

Third, you said specifically that they simply average the 85 stations. That is blatantly false. You have not addressed this falsity at all.

Try reading my reply again. I clearly stated what I implied and it's not false at all.

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Extrapolating data is not homogenization. Homogenization refers to the removing of non-climactic changes to the raw/measured data itself.

Extrapolation:

https://en.m.wikipedia.org/wiki/Extrapolation

In mathematics, extrapolation is the process of estimating, beyond the original observation range, the value of a variable on the basis of its relationship with another variable. It is similar to interpolation, which produces estimates between known observations, but extrapolation is subject to greater uncertainty and a higher risk of producing meaningless results.

Homogenization:

https://en.m.wikipedia.org/wiki/Homogenization_(climate)

Homogenization in climate research means the removal of non-climatic changes. Next to changes in the climate itself, raw climate records also contain non-climatic jumps and changes for example due to relocations or changes in instrumentation. The most used principle to remove these inhomogeneities is the relative homogenization approach in which a candidate stations is compared to a reference time series based on one or more neighboring stations. The candidate and reference station(s) experience about the same climate, non-climatic changes that happen only in one station can thus be identified and removed.

If you want to call it homogenization, fine. It doesn't matter to me, but you'd be incorrectly using the word.

What GISS/NCDC do, as I'm sure you know, is referred to as interpolation. It's easier for them to do because their grids are smaller and contain more data.

Interpolation

https://en.m.wikipedia.org/wiki/Interpolation

In the mathematical field of numerical analysis, interpolation is a method of constructing new data points within the range of a discrete set of known data points.

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Very well. Its not like this board will make a difference in the grand scheme of climate policy or anything. Putting it in perspective is important. Sometimes being anonymous

allows people to be more bold or arrogant than they otherwise would be. My rule of thumb; treat others as if you were having an in face conversation with them, and all should be better.

The last sentence sums up my feeling exactly. Go say that in PR and they will accuse you of physically threatening someone.
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Lies!

Why are you deliberately taking that out of context? I've clarified and elaborated on that statement multiple times, but you're more interested in pulling a nonexistent "gotcha" out of illusionary hat.

If you disagree with something I post or would like clarification, just ask me to elaborate or point out where you think I'm wrong. No need to jump to conclusions or scream accusations into the heavens. It's unproductive.

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Why are you deliberately taking that out of context? I've clarified and elaborated on that statement multiple times, but you're more interested in pulling a nonexistent "gotcha" out of illusionary hat.

If you disagree with something I post or would like clarification, just ask me to elaborate or point out where you think I'm wrong. No need to jump to conclusions or scream accusations into the heavens. It's unproductive.

 

Because the statement is wrong no matter how you slice it. The global coverage is more than sufficient. It is not a simple average. It is an area weighted average which accomplishes the exact same thing as extrapolation. 

 

Your statement is wrong both on its face and in its intent (to criticize). The criticism is unjustified (really just a hack move on your part).

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Because the statement is wrong no matter how you slice it.

The global coverage is more than sufficient. It is not a simple average. It is an area weighted average which accomplishes the exact same thing as extrapolation.

Who argued for a simple average? The gridding process removes initial areal measurement bias, then a basic extrapolation procedure is carried out within each grid cell, using the data from the station(s) confined within that grid. That makes it a simple extrapolation, not a homogenization or an interpolation.

As for the coverage issue, it's something that is detrimental to the RATPAC data on shorter timescales because huge areas of the Pacific Ocean, Southern Ocean, and African continent are left unmeasured, and climate change varies on a regional basis.

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Who argued for a simple average? The gridding process removes initial areal measurement bias, then a basic extrapolation procedure is carried out within each grid cell, using the data from the station(s) confined within that grid. That makes it a simple extrapolation, not a homogenization or an interpolation.

As for the coverage issue, it's something that is detrimental to the RATPAC data on shorter timescales because huge areas of the Pacific Ocean, Southern Ocean, and African continent are left unmeasured, and climate change varies on a regional basis.

 

Wait, I thought you said there was 'nothing in the way of gridding...'

 

Actually, I made a mistake. RATPAC does nothing in the way of gridding or spatial homogenization at all. They merely take the data from the 85 stations and average it out. Wow..that's just an awful way to go about this.

(Keep in mind, this a bit old/when UAH and RSS were lacking homogeneity, unlike now).

http://www.met.reading.ac.uk/~swsshine/sparc4/Lanzante_SPARCTabard.ppt

Here's the station map. Look how much of the Pacific and Southern Oceans are just left blank. Hilarious.

 

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Gridding or spatial homogenization. READ.

We were debating whether or not what they're doing is actually homogenization, which it isn't. Either you're trolling or lack the ability to follow context.

 

So you said it's an average of the 85 stations. It's not.

 

You said there is no gridding OR spatial homogenization. It is gridded, and it is spatially homogenized.

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