http://www.geo.uni.lodz.pl/~icuc5/text/O_8_2.pdf

fun stuff

The order of selection (rural first, urban second) is to quickly shorten the list of candidate sites. The truly rural sites (rural, dark, nightights=1, brightness index=0, approved by Anthony Watts, etc) are very few. Starting with them and drawing a fairly tight radius (500km?) quickly shortens the candidate list of urban stations.

I don’t think it’s a good idea to filter by whether RURAL cools URBAN or vice-versa. I think the procedure should be to look at the differences between urban and rural (using stations that we know to be of high quality) and check the strength of the relationship to some function of population.

MarkW pointed out some problems with #5 (shifting) and after thinking about it for the afternoon I’m pretty sure the right procedure is to deal with the derivative of temperature wrt population. Using the Oke equation you gave above:

Du = 0.73*log10(Pu)
dDu/dPu = 0.73/Pu/ln(10) ~ 0.32/Pu

Beyond that I think this temperature data is endlessly fascinating.

Agreed.
I’m hoping to drum up a volunteer or two to filter the sites and gather the population data. Hopefully Anthony Watts will be willing to review the station lists for problems. I’d be happy to run the calculations.

The recent UHI studies that I’m aware of do not seem overly robust — this could be a real contribution.

Since I have no accountablility I would say that UHI is a Function of Building Height
( which is probably a log reponse), Ground cover ( log reponse) and Waste heat.. log(population)

Arm chair analysis, pass the Tv remote.

You wrote

“I wonder if there are enough surveyed sites with acceptable station quality (CRN1, 2, and possibly 3) to test the Oke UHI equation in the USA48.”

I was looking at Orland and Willows last night and have decadal pop figures ( UHI forcing?)

I would like to get my hands on the OKE’s studies, but the best I have been able to do is find
references to his equations. Also, in subsequent work, he also argued that the Coeff of the Log10(pop) vaired by continent. I would hazard this. I would hazard that the UHI response to increased population is likely to be thresholded. Anyway, I have Willows and Orland POPulation figures
for 1880 to present ( decadal) both sites are sub 10K population, but have grown from LT
1000 people to 6000 people over the course of the past 120 years. marysville might be a nice
companion site…

What explains the warming better. CO2 or log(pop)? or combination..hmm

Here’s my thinking-out-loud procedure:

1. Identity the USA48 stations with the best CRN ratings (there are ~20 CRN1, ~45 CRN2, and ~90
CRN3 stations);

I played a bit with this from the other direction.. Without looking at CRN rating
I selected two groups. “best” and “worst” Where “best” was the union of Rural = true.
Nighlghts = 1, GHCN = dark, Brightness index = 0, Open = True. And Worst was the
antithesis of this.

2. Identify a subset of stations that are truly rural (Sr);

Some issues here, but I started to filter sites by the following logic
a. Pop = rural. nighlights =1. GHCN = Dark. Brighness index =0. Site is
open today. I think updating the site with current POP would be a good thing.
Essentially, what we are saying is “identify sites” that have not suffered from UHI.
Actually USHCN has population figures for most of the sites from 1910 on…. hmmm
I also tossed sites on the coast and airports. I Found a couple CRN12 in this batch.

3. For all stations in Sr, find non-rural stations within some radius (Su);

This is backwards of the H2001 approach, but makes no difference. It would be
an interesting excercise, but the fundamental issue is that Urban/Rural is REALY ANALOG
not digital. assume OKE is correct. UHI = .x*log10(P). Assume no global warming.
The TREND in a city that grows from 5K to 15K is greater than the trend in a city
that grows from 100K to 110K people. puzzlement. a growing RURAL would COOL a Urban
site.

4. For all stations in Sn, calculate the annual temperature difference vs neighbouring stations in Su — call these difference Du (ideally Du represents UHI and other urban effects);

I think we want to examine those instances when RURAL cools the URBAN. Since this adjsutment
is unphysical

5. Shift Du to a suitable reference period that is unlikely to have significant UHI (1890 to 1910 may be suitable for smaller centres), and generate decadal averages — call this Du’

Even though UHI was first identified in 1830….
I like this move.. One thing I was playing with was seeing how well Oke’s rule fit
stations that had very shallow population growth…The approach was to find gold standard
sites.. Spatial coverage, of course, suffers with this. However, note how compelling it
is when you select very high quality sites and the answer matches the dogs breakfast,as it were.

6. Generate decadal population histories for all Su stations using census data (Pu);

USHCN has this. its at the FTP. ( lucia should add population as a forcing)
If you need a pointer to the decadal population figures… crap its the USHCN ftp site.
The orginal plan was to use decadal POPS, but ……
Might be cool to update the POP with the UN data. Make sure that Urban 1980 is Urban 2000

7. Plot Pu vs Du’

It would be a start. As Much as I like Oke, other’s have argued that population is
a good start but that other factors explain more. Still, I love data.

What do you think?

I think that GISS is largely correct, but that sites that dont meet quality standards should not be used. Beyond that I think this temperature data is endlessly fascinating.

I think it is reasonable to exclude municipalities with decreasing populations. Ideally they would be analyzed separately but I doubt there will be enough stations in this category.

The analysis could also be split into time domains. A coarse split like pre- and post-WWII might be useful.

The purpose of this little study would be to check for and quantify the relationship between UHI population (or log population, or the square root of population, or whatever). To me this is the kind of useful *information* (maybe even knowledge) that can be gleaned from the SurfaceStations *data*. (That is not meant to be derogatory towards SurfaceStations — data is a necessary prerequisite).