Comments on: Airport Warming

By: The Big Valley: Altitude Bias in GHCN | Watts Up With That?

The Big Valley: Altitude Bias in GHCN | Watts Up With That? — Thu, 19 Aug 2010 15:16:40 +0000

[...] and UHI will be addressed in forthcoming pieces. Given the preliminary work done on airports. (and here) and latitude to date, we can confidently say that the entire debate will come down to two basic [...]

By: Nick.Barnes

Nick.Barnes — Tue, 25 May 2010 14:13:57 +0000

@MikeMcc: Yes. The airport tag is in static metadata for each station: latitude, longitude, altitude, vegetation type, satellite brightness, and so on.

By: MikeMcc

MikeMcc — Tue, 25 May 2010 08:24:54 +0000

How do you have ‘airport’ annotated data prior to 1928? Is is data from sites that have since become airports?

By: Zeke Hausfather

Zeke Hausfather — Fri, 21 May 2010 22:17:45 +0000

drj,

I’m curious if you or Nick might be able to lend us a hand unraveling a little mystery: http://rankexploits.com/musings/2010/the-great-gistemp-mystery/

By: drj

drj — Wed, 19 May 2010 08:03:51 +0000

@Zeke: That seems entirely likely. Visualising the spatial coverage is on the infinitely long todo list.

By: Zeke Hausfather

Zeke Hausfather — Tue, 18 May 2010 20:24:40 +0000

I think you might be introducing some bias due to differing spatial coverage. I’ve been working on a similar project for the last few days, and get the same results as you for all stations of each type, but the difference mostly disappears when looking at only grid cells with both airport and non-airport stations.

By: Marcus

Marcus — Tue, 18 May 2010 15:47:01 +0000

Carrot Eater: What I’ve done in my own studies was to actually insert various boolean checks in Step2 where the code drops “short” records, and have the code also drop records with whatever characteristic I care about (eg records ending after 1992/records with brightness less than X/etc.). It seems to me that this is less labor intensive than splitting the GHCN dataset.

By: drj

drj — Tue, 18 May 2010 14:20:36 +0000

@physicist: Mostly because it just happens to be computed.

Showing something more intelligent would have to take into account the autoregression in the data (which requires a bit more thought). [drj: 2010-05-19: oops, I mean autocorrelation here, not autoregression]

Actual answer: I didn’t show R² initially, and then Nick added the code.

By: physicist

physicist — Tue, 18 May 2010 11:42:30 +0000

Why do you show the R²-value?
It’s nearly irrelevant – the standard error contains more information.

By: carrot eater

carrot eater — Sun, 16 May 2010 01:37:14 +0000

So you run step 0 for the whole thing, then split the input, and run steps 1,2,3 and 5 for each subset separately?

Can you give sample sizes?

I’m curious about the airport subset, maybe in step 2. I thought urban stations get whacked if they don’t have enough rural neighbors to do the UHI adjustment. If you have an airport-only subset, then you need neighboring rural airports, which seems a little restricting. I’m curious how many stations survive that step, and how many get whacked.