23042010

More exact monthly means...

Background

For details of the method employed see What exactly is a monthly mean?.

Data Pairs used

The following data pairs from the GCHN and SCRAM records were used

Santa Barbara/FAA Airport    CA23190 -> 42574606001
Red Bluff/Municipal Airport  CA24216 -> 42572591000
Fresno/Air Terminal          CA93193 -> 42572389000
Scottsbluff/Country Airport  NE24028 -> 42572566002
ISLIP/Long Island            NY04781 -> 42572501001
Memphis/Intl Airport         TN13893 -> 42572334000
Yakima/Air Terminal          WA24243 -> 42572781000
Lander/Hunt Field            WY24021 -> 42572576000
Lexington/Bluegrass Field    KY93820 -> 42572422000
Chicago/O'Hare Int'l Arpt    IL94846 -> 42572530000

Results

Please see this page for the results
This is purely done to allow me to better present the results, than trying to force them through the post mark-up.

Conclusions that can be drawn

The larger set of results show some interesting commonalities:

• The previous assertion, namely:
  
  • "The exact mean value is able to often 'stray' by greater than 0.2 away from the GHCN data set value whilst the derived value remains in scope (i.e a * in the fair right column, but none in the third). To me this indicates the daily min/max mean approach is often incorrectly representing the real distribution of temperatures in the month; which is not that much of a surprise as the daily min/max only uses a maximum of 62 contributing data points, compared to the exact mean that uses up to 744 contributing data points (or 10x plus the effective resolution)."
  Looks to be holding up well across all the data set pairs. This indicates to me something significant is occurring.
  
• The 'Net temp diff' looks to be trending up from the beginning of the samples to the end, I will have to extract and plot to confirm - but looks like there is a 'warming' baked in some how; perhaps the min/max method fixates too much on the daily limits compared to the total temp frequency over the day??

Basically, more food for thought; need to extend this to a larger data set and codify for significance.