Data is dependent on cloud cover. Unfortunately cloud data is only available during daylight. Hours of darkness therefore are not included in this analysis
2004 - 2006 31192F3 large error
2006 - 2008 31194F3 10 w/sqm drop
2008 - 2009 31192F3 random 8w/sqm p-p
2009 - 2011 31194F3 10 w/sqm drop
2011 - 2012 31192F3 random 13w/sqm p-p
2012 - 2013 31194F3 random 10w/sqm p-p
It appears that 31194F3 has a drift with time (now possibly corrected)
Also it seems that +-5w/sqm is the expected accuracy for this type of pyrgeometer
Luckily this is the only data extraction that is synchronous with date. Other extraction will tend to remove the drift by averaging. It is significant that the calibration adjustments show up indicating that this spreadsheet successfully sees valid changes of <5w class="goog-spellcheck-word" span="" style="background: none repeat scroll 0% 0% yellow;">sqm5w>
This next plot shows the expected variation of dlwir with temperature.
The net plot shows the effect of increasing absolute humidity
The following pair show the change with day of the year. Note that temperature effects should have been nulled so the peak should not be due to summer temperature. The second plot compares La Jolla CO2 with the dwlir. The dwlir seems to show the inverse of what would be expected!
Station pressure below, has very little effect on dwlir
Opaque cloud cover increases the dwlir!