Re: satelite view (wider field of view)
I am curious to see what is around your chosen site, besides lots of barbed wire.

Best,
Scott

CRN is supposed to address this. Here’s hoping.

I couldn’t actually find any mention on the site the idea of a global clean temperature record, but I suppose after you’ve identified enough sites it will be easy to construct.

Any idea how many (true) Class 1 sites you would need for the result to be statistically significant? Maybe you have enough already?

Sean

Gage R & R is treated, oddly, as a sort of heresy by most orthodox so called “Climate Scientists.”

These devices have a 1 F internal resolution and a +/-2 F accuracy.

Would it be possible to select, out of all the temperature stations in the world, 100 or so that are undeniably way out in the country removed from any possible urban creep effect, and which have been in the same place for decades, and plot the historical average temperature from these stations? Basically create a “pristine” average temperature record?

Any comments appreciated.

Sean

REPLY: I’m working on that, see http://www.surfacestations.org – Anthony

Not a bad procedure although I would use a solid state warmer. They’re often found in battery operated six-pack coolers. They’re adjusted the same way, i.e. by changing the duty cycle, but not as noisy.

DS still needs to characterize the parking lot sensor (if he hasn’t done so already) or the results aren’t very meaningful because:

Let TP = temperature indicated by the parking lot sensor.
TP = Tp + Ep + EP, where
Tp=true temp (ambient) at the sensor (if the parking lot wasn’t there)
Ep=sensor error
EP=error introduced by the lot and surrounding terrain

Assuming DS knows the true temp of his own sensor (let’s say at A and called TA), to find EP, he needs to calculate:

EP’ = TP-TA = Tp+Ep+EP – TA

The built-in assumption is that Tp and Ta (the true ambient) should be the same but this is likely true.

See the problem? There are two unknowns (Ep and EP) in that step so unless he has really knows the sensor characteristics at the parking lot he’s mostly wasting his time. What he’s finding is (Ep+EP), which is what he set out to do.

Your guest entry is interesting and your work inspirational as always. If I may ask a question on method: How did you isolate sensor B temperature data for periods of exclusively north-blowing winds or south-blowing winds?

Thanks,
Bill P

1. buy a Styrofoam cooler.
2. Mount a low wattage incandescent light bulb in it.
3. Mount a small fan next to the light bulb.
4. Control the lamp wattage with a light dimmer

Take measurements over a range of temperatures.

This will let you know if the thermometers have different non-linearity characteristics. If you have a calibrated thermometer you can measure the actual non-linearity.

One thing to watch for. Electrical noise. Dimmers are notorious. A filter can help. Possibly a bit of tin foil around the USB connector (not touching the pins) can help.

More sophisticated tests can be done with a similar set up: DC supples, temperature controller, etc.

If everybody just covered their homes with aluminum foil in the summer (shiny side up, please), how much cooling would we notice? :-)

BTW, David, is how high is the sensor attached to the fence from the ground? Is it the regulation 1.5m? Are both A and B at the same height?

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To further the basic premise (and like so totally vindicate the Rev):

Yilmaz proves the offset bias. Kit proves the trend bias:

Ross R. McKitrick, Patrick J. Michaels , JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, DECEMBER 2007, Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data

Abstract:

Local land surface modification and variations in data quality affect temperature trends in surface-measured data. Such effects are considered extraneous for the purpose of measuring climate change, and providers of climate data must develop adjustments to filter them out. If done correctly, temperature trends in climate data should be uncorrelated with socioeconomic variables that determine these extraneous factors. This hypothesis can be tested, which is the main aim of this paper. Using a new database for all available land-based grid cells around the world we test the null hypothesis that the spatial pattern of temperature trends in a widely used gridded climate data set is independent of socioeconomic determinants of surface processes and data inhomogeneities. The hypothesis is strongly rejected (P = 7.1 × 10−14), indicating that extraneous (nonclimatic) signals contaminate gridded climate data. The patterns of contamination are detectable in both rich and poor countries and are relatively stronger in countries where real income is growing. We apply a battery of model specification tests to rule out spurious correlations and endogeneity bias. We conclude that the data contamination likely leads to an overstatement of actual trends over land. Using the regression model to filter the extraneous, nonclimatic effects reduces the estimated 1980–2002 global average temperature trend over land by about half.

By half!

Just like I said back a-when! When Joe D’Aleo made that PDO/AMO correlation, and I commented that cutting the 1980-present increase by half (in honor of the Rev’s siting issues) would make the D’Aleo curve fit so much better! I just KNEW it had to be about half. It fit the picture too perfectly not to be. (They beat me up pretty good on Tamino for venturing that opinion.)
Now it’s Kit to the rescue! (Can I call ‘em or what?)

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Great experiment.

Keep up the good work, David.