On Apr 10, 1:43 pm, "Paul E. Lehmann" wrote:
Bill Ward wrote:
On Wed, 09 Apr 2008 13:12:25 -0700, Roger
Coppock wrote:

On Apr 9, 8:34 am, Bill Ward
wrote:
On Wed, 09 Apr 2008 07:04:24 -0700, matt_sykes
wrote:
On 9 Apr, 10:24, Roger Coppock
wrote:
On Apr 8, 8:01 pm, Poetic Justice
-n-

Dog.com wrote:
Roger Coppock wrote:
March ties for 3rd warmest on NASA's
129-year record.

Why is NASA the official keeper of the
temperature?

NASA Goddard Institute for Space Studies
offers data, as do several other
organizations. I use NASA's data because
GISS corrects for UHI using nighttime earth
shine, artificial lighting, measured from
satellites. IMHO, this method is better
than using census data to locate urban
areas.

That is a feeble way to adjust for UHI.

The ONLY way to adjust for UHI is to put a
station in a rural area near to the urban
station to act as a control (being a
sicnetist you wold know this of course).

Mind you, one you have done that you might
as well ignore the urban station data since
rural data is true surface temp.

And what happens when you do that? You get
no warming trend. RURAL STATIONS ACROSS THE
GLOBE SHOW NO OVERALL TEMPERATURE TREND.

Roger can't comprehend that bad data is worse
than no data. Unless you're trying to scare
people, of course.

If you have better data, or a method for UHI
correction, you are more than
welcome to present them here. Until then the
data presented above are a better indication of
reality than your fantasies.

Fantasies aren't science, whether they're mine
or NASA's. That's
the problem with trying to "correct" bad data.
If it's bad, it can't be
used - it's a fantasy based on invalid
assumptions. Averaging bad data with with good
data hides the problem, but doesn't fix it.

It is even worse than that. I suspect there is an
analogy with wine making. I knew of a winemaker
who had a small amount of wine made from under
ripe grapes. He blended it (10%) with some good
wine 90%). That small amount ruined the whole
lot. A "Little BAD Goes a LONG ways". I suspect
the same is true of data. A little bad or
incorrect can have effects that are way beyond
suspected results.

Such are the musings of a statistical illiterate. In actual practice,
the way to avoid problems from extraneous or erroneous data (ie. bad)
is to swamp it by using large sample sizes. If you measure the height
of 100 men taken at random, the mean height will be virtually
unchanged should one or two of them be giants or dwarves.

This is what happens in practice with temperature data. Very large
sample size ensures that the famous UHIs will have little effect on
the calculated global means.