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RicS Offline OP
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G'day all,

Because I have been harping on about the data sets and how they show, well, nothing, I thought it is worth looking at a few weather station sites. These were my random selection done quickly for a lecture I gave from data freely available on the NASA site http://data.giss.nasa.gov/gistemp/station_data/ . Urban effect should be at work in many of them. Here is a list plus some comments:

Alice Springs (Central Australia)
No pattern ? A slight overall cooling mainly because of a huge drop in 1978.
Five different data sets. Not continuous.

Baltimore Wso City
Very good trend upwards. A poster child for global warming (except for urban effect)
One data set. Continuos from 1890 and with data back to 1880.

Bathurst (rural inland eastern Australia)
Trend downwards. Little urban effect because it is a rural station outside the city.
One data set. Continuous only from 1963. Ignoring the slight gaps goes back to 1910.

Chongqing (China)
Very slight cooling trend.
Three data sets. Match in trend with some significant differences but quite different temperatures. Continuous from 1921.

Christchurch (city, South Island, New Zeland)
Maybe a slight warming trend but very big yearly variations.
One data set. Continuous from 1902.

Curitiba
Significant warming trend.
Three data sets. Continuous from 1884 to 1996.

Dawson, Yukon Territories, Canada
No trend. Very big yearly variations.
Three data sets. Line up reasonably.

Elko Faa Ap
Cooling trend but very slight. Very very big drop in late 90s.
One data set. Continuous from 1880.

Farina
Cooling trend except for exceptionally hot year in 1917.
One data set. Continuous from 1889 to 1939. No more recent data.

Fredericksburg Nation Park (US rural)
Very slight cooling trend.
One data set. Continuous from early 1890s.

Las Vegas/Mcc
Very distinct warming trend. Exactly as would be expected for a massively expanding major city from a tiny outpost.
One data set. Continuous from 1938.

Lithgow (An attempt to determine the urban effect of Sydney being a township on the fringes ? actually helps to show the futility of such an exercise as it and all other peripheral areas do not match in pattern to Sydney at all).
Perhaps a slight warming trend.
One Data set. 1915 to 1958. 1963 to present.

Livingstone
Distinct cooling then distinct warming from 1980.
Four Data Sets. Match with a few exceptions. 1918 to 1987. Some data for 1999 to 2001.

Mcgill
Depends if you include the discontinuous considerably lower date from late 1880s to 1900 a slight warming trend but if not then a slight cooling trend. The early data suggests a different system of measurement since even the highest in that period does not come up to the coldest of the remaining 80 odd years.
One data set. Late 1880s to 1990. 1916 to present.

Mirnyj
No trend. Yearly variation too great to reasonably ascribe a trend to it.
Six data sets. Closely correspond. Late 1950s to present.

Mistassini Post, Quebec
No trend. Another locale that has a discontinuous early reading that is very much lower than the rest of the set. Enough of these would suggest that methods of measurement in the late 1800s resulted in much lower temperature readings. Would make a good study.
Three data sets. 1899. 1915 to 1982 using the overlap of the three sets. Otherwise not continous.

Moosonee, Ontario
Perhaps a cooling trend from the 1950s peak but one data set shows a very high rise in 1999.
Eight data sets. 1890 to 2003. Must use all data sets to obtain a set up to 1995. Where they overlap the sets do reasonably correspond.

Nantucket/Faa Airport
Cooling trend.
One data set. 1946 to 1975. 1977 to 1983.

Owings Ferry Landing
Slight cooling trend.
One data set. 1917 to 1981. 1983 to 1999.

Porto/Pedras
Distinct Cooling trend (for what it is worth, and that is not much)
Three data sets. 1880 to 1901. 1959 to 1982. Quite large differences between the three sets (all in the 1960 to 1980 range).

Rome Italy
Line ball or slight cooling (should be a distinct warming trend because of the urban effect but Rome has been paved for a very long time and large commercial buildings may not have had as large an effect because of the very large areas of vegetation in the heart of the city)
One data set. 1880 to mid 1930s. Then 1941 to 1995.

Rome (34.2 N, 85.2 W)
Cooling trend overall, short sharp warming trend from mid 1990s.
One data set. 1880 to present with one year break 1890.

Royal Oak 2ssw (38.7 N, 76.2 W)
Distinct warming trend.
One data set. Early 1890s to present.

Salisbury/Wicomico Co Ap
Cooling trend.
Two data sets. Around 1950s and from early 1960s to present.

Wells (41.1 N, 115.0 W)
Distinct cooling trend overall, slight warming trend in later part.
One data set. 1880 to 1918 then 1940 to present.


Total: 25 (not a very big sample but it?s better than nothing and to include the 200 I did for the lecture would run to pages). I just picked these at random from the random sample. But feel free to get your own random sample.
Warming Trend: 4
Slight Warming Trend: 2
No Trend: 5
Slight Cooling Trend: 9
Cooling Trend: 5

If you averaged the data, there might be a warming trend because the distinct warming trends are larger than the distinct cooling trends. But overall, there is no pattern. This is the problem with an average world temperature.

Should you really average each locale you have without regard for distribution or degree of variation? Not only does urban effect come into play, but so does the distribution that means there are more sites in the warming trend areas than in the cooling trend areas even though the cooling trend areas cover much larger chunks of the earth. And even with this bias, in number you still get more cooling trend stations than warming ones, just the amplitude is not as large.

And what of the majority of the planet that is under water. SAT temperatures do not include any temperatures over the sea. Very big area to ignore. The satellite data does include such areas and gives a distribution unconcerned with where the stations are or how accurate the equipment or the recording. It simply averages the 17,000 evenly distributed measurements daily.

This is real science. The science of direct observation. Anyone can repeat the experiment. Anyone can do the calculations. Anyone with enough time could go through the whole 7,000 and create a distribution both in regions and in trends. Hasn?t been done but it is not difficult to do. Until then, samples at least show up the difficulties and the degree of variation and the fact that the majority of stations show a decrease despite what should be an overall increase because of urban effect. The patterns, including gaps and multiple sets, also show up the likelihood of accuracy. The US tends to have only one set and tends to be continuous (although surprisingly the actual continuous data sets in number are still quite small) whereas other parts of the world show gaps and multiple sets, suggesting problems with the data and its method of collection, plus the likelihood of the station being moved (and when they get moved they tend to move towards large population centres because weather records are the most local use for population centres, worst for trends, but the people taking the measurements are not doing them so that someone could use them for global studies).

Food for thought, for those that wish to think.


Regards


Richard


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TOO LONG!


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Quantity <> Quality.

Science <> Random Selection

You see, RicS, one can communicate clearly with brevity. Better yet ... it contains no spin.


DA Morgan
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RicS Offline OP
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G'day Danial,

Actually, random selection is a foundation of scientific method. Would you rather me list all 7,000 stations? McKinsey didn't survey every person in the US, yet his sample seems to have been large enough to remain valid. A great deal of science relies on sampling. Ice core samples for instance. The question is whether the sample is big enough to be representative. 25? Not even close but it still is interesting. As I said, if you do not like the sample, do the experiment yourself.

Somehow you are suggesting the list is too long but that it should not have been random. The two are mutually exclusive.


Richard


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Do you know the difference between random and pseudorandom? How did your "random" sample achieve statistical randomness?

Anytime a member of the laypublic, with attitude, can take data and come to a conclusion 180 degrees different from that of researchers in the field whose work has been peer reviewed.

That member of the laypublic is engaging in spin.


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G'day all,

Please don't bother to respond to this post Mr Morgan, it will be ignored unless it actually has some science in it.

I'd be interested in any views as to sampling techniques for a data set of 7,000 odd weather stations. My suggestion is to start with all weather station data, eliminate all stations within a set distance of a population centre, and then divide the remainder into region, giving weight to the region based on its area in relation to the total earth's surface. I think that the raw data averages should also be included, not weighted by region, for transperancy. I also think that a comparison average including urban effect stations should be included in any set made.

It is interesting that the averages of all stations in the Southern Hemisphere, do not show any trend at all. I'd like to divide that up further into continents at least.

Actually, there are a few areas of the earth using the raw data, that show strange trends. The total US stations, including all cities show a cooling trend. I'm still trying to work out how that can be rationalised since urban effect has to be pretty pronounced in the US data.

The antarctic is also getting slightly colder but of course this is not a very big sample, being a limited number of stations only dating from the 50s. The southern hemisphere obviously has less stations that the northern hemisphere but it also has some very accurate data, including as it does Australia, New Zealand and, yes, even parts of Africa that have kept good records.

Averaging all of a region, obviously isn't random sampling at all. Excluding urban areas still isn't sampling but it does leave some large areas of the earth with no data or very few locations.

Any suggestions for different sets and ranges would be appreciated. I'd like to include a wide variety of methods and certainly do not want to be accused of bias because of some set that someone thinks should have been included but I didn't think of or has not been done in other analysis.

The biggest problem comes down to what to do with missing data. My thought is to assume that the missing data is actually random in nature and to average each site used providing the missing data is not extensive. So if there are 8 years missing in 90 years, simply average the 82 years without attempting to "fill in" the gaps. Anyone see any flaw in that method. I have thought that I'd have to present a table of the missing years and how often they are missing overall. Obviously if the 1920s are missing in a region far more frequently than any other period that could distort the average and anyone looking at the data should be able to easily look up how often particular periods were missing from the data.

I believe this is the area where previous averages have failed or not been clear. For instance, the NASA system, does fill in the blanks by averaging the surrounding stations for the missing years and adding them in. It also "calculates" urban effect by obtaining the deviation of an urban effect area from the closest none urban effect stations based on night light emissions and subtracting that deviation from the urban effect stations. The potential for manipulation of the data or inadvertant bias is very large indeed by using formulas such as this and I wish to eliminate any such need for modification of the data other than a simple decision to exclude or include the station depending on rigid selection criteria.

It would be easier to confine the data to only those stations that were not missing more than say a couple of years here and there but if you want to go back more than 80 years, that cuts out over 97% of the stations. Less than 300 stations is not nearly enough to work with, especially if sub-sets such as non urban effect stations are also be be analysed.

It is far easier to consider this if you look at a few station's data and start to see just what the difficulties are. And of course, this is becomes a much more difficult task when the raw daily data has to be obtained, after selection criteria is established.


Regards


Richard


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Quote:
Originally posted by DA Morgan:
Do you know the difference between random and pseudorandom? How did your "random" sample achieve statistical randomness?

Anytime a member of the laypublic, with attitude, can take data and come to a conclusion 180 degrees different from that of researchers in the field whose work has been peer reviewed.

That member of the laypublic is engaging in spin.
and any researcher with a goverment grant paid to prove global warming, can use pseudorandom stats to prove it. as long as the peer's all agree that global warming is a fact there is no way they would accept facts that disagree with that.

let me explain something that might have slipped your mind. the peers that review the articles that are summitted are not choisen at random. they are choisen by the publishers. which means that if the publishers beleive in global warming, and they know the reviewers that also agree, then these are the people that will be reviewing the article. Since there is only a small number of publishers that would be considered acknowledgeable, AND since global warming scaremongering sells subsciption, it does not take much of a conspiracy to have all of the few publishers that control those articles pushing global warming. If you go to someone outside of this tiny group, everyone claims that the data cant be real because the publishers is of the wrong political party, or is not considered cutting edge science, or something along that line. If you want to read what people say about that kind of publisher, read some of da's responses to some of the links i provided.


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dehammer wrote:
"and any researcher with a goverment grant paid to prove global warming, can use pseudorandom stats to prove it."

You'd be right if every grad student on the planet wasn't looking for an opportunity to bust them and thus gain recognition.

The problem with what you posted is that it demonstrates a total lack of real-world experience with academia. You make a baseless assumption and then, based on the fact that you think it reasonable given your limited experience, repeat it as though it was the gospel delivered personally by an apostle.

It is truly appalling.


DA Morgan
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G'day dehammer,

Any argument that becomes so circular that it disappears up itself probably isn't worth continuing. Logic has little to do with it in the end.

Apparently a graduate student has access to publications just so they can shoot down their professors and this is a normal part of science. When it gets to this extreme in the argument you know you have lost, not because you are wrong but because it matters not what the argument is, how absurd it gets, there will be a counter to it. It will not include any real science, of course, just protests that to the other extreme, such as any organisation that does not agree with the stated position is not worthy of consideration, that only peer reviewed published research has any value, unless of course there is none in support of a particular position, then the argument is simply ignored.

With global warming, the whole basis comes down to data sets that my views on its validity is rather well known on this forum. Yet this is the one topic that actually never gets addressed, because there isn't even news arcticles to use to counter the arguments and even pro global warming research using mentions serious problems with the data, before it suggests the author's brilliant methods of data manipulation that have somehow fixed the problem or brought it down to some fictitious margin, usually expressed to a ridiculously small number such as 0.03405%.

Face it, this argument has long been lost. You cannot win an argument or even have an debate that is of value where those that oppose your position play be a completely different set of rules.


Regards


Richard


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Quote:
Just What Does a Random Sampling of SAT (Surface Air Temperatures) Show
That\'s why the analysis of the data is non-trivial!

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G'day Count,

Thank you for the link by the way.

I'm not sure what you meant though. The data analysis at this link still relies on monthly averages from data sets. As best as I can determine the underlying data is still the GHCN II data set for the SAT (but I could be wrong on this - it simply says that the SAT data is a monthly average of about 3,000 stations). What is different is the use of several other data sets, including sea surface temperature.

I'm not at all sure I agree that "estimates", which is a scientific term for a guess, are small in the latter data sets since the actuall method of how they account for urban effect is available presumably only in the full research paper and it is not one I have. I'll have a look at it in a little while however because it is directly in the area that I have the most interest.


Regards


Richard


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~never mind; wrong thread....~
& gee, i didn't even realize that was a link!
Cool, Count Iblis.
~S


Pyrolysis creates reduced carbon! ...Time for the next step in our evolutionary symbiosis with fire.

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