Also, just a side note from examining this, the airport in Memphis is not a USHCN listed site, but it is part of this temperature record. Since this is listed as the GHCN v.2 mean temperature data, that would seem to indicate that GISS uses whichever stations they wish to use when doing their analysis.

Under the FOIA and amendments, I wish to obtain the raw data in digital format for:
Hansen et al. 1999
Hansen, J., R. Ruedy, J. Glascoe, and Mki.Sato, 1999: GISS analysis of surface temperature change. J. Geophys. Res.,
104,30997-31022, doi:10.1029/1999JD900835.
GISS analysis of surface temperature change
J. Hansen, R. Ruedy, J. Glascoe, and M. Sato
NASAGoddard Institute for Space Studies, New York
The source of monthly mean station temperatures for our present analysis is the Global
Historical Climatology Network (GHCN) version 2 of Peterson and Vose [1997].This is a
compilation of 31 data sets, which include data from more than 7200 independent stations.
I do not request the GHNC version 2 adjusted data. My request is for the raw data.

Response
Sent: Friday, July 18, 2008 12:37 PM
To: sc.rr.com
Subject: FOIA Request #07-163

Pittman

Dear Mr. Pittman,

We apologize for the delay in responding to your request.

This response to your Freedom of Information Act (FOIA) email request for Raw data in digital format for GISS analysis of surface temperature change.

Mr. Retro Ruedy from NASA Goddard Institute for Space Studies, had stated that the information can be located at the below link:

http://data.giss.nasa.gov/gistemp/foia/v2.mean_1999.txt

If we can be of further assistance, please contact Jolyn Nace at 301-286-9030.

Joan E. Belt
FOIA Public Liaison Officer
***************************************
NASA Goddard Space Flight Center
Office of Public Affairs
8800 Greenbelt Road, Mail Code 130
Greenbelt, MD 20771
Phone: (301) 286-4721 Fax: (301) 286-1712

I wonder if I did recieve what I requested?

Also, this table shows the number of stations involved in the GHCN listings by source network. It does look like there is some overlap in station location between a few of the networks. Trying to figure out which data set is supplying which data from which stations looks like a “needle in a haystack” type search. Couldn’t they just give us a simple list saying which stations are in the GHCN? I’m sure it could be done on an Excel spreadsheet by a decent typist in an hour or two.

Having been puzzled by the lack of rebuttal from AGW people that global average climate has been a plateau for a decade, I wonder if the plateau is an artefact of removing a disproportionate number of hotter, bad stations from the older dataset and not admitting it. This would produce an apparent cooling. Then, all you have to do is sit around for a few years after making an announcement that more heating is ‘in the pipeline’.

Goodness, an incredible mess has been made of presenting daily temperatures on a daily basis. Derivation of averages over time or space, smoothings, predictions, have to be very carefully qualified.

I do not know how many “foreign” stations are used by coordinators in the USA, nor what homogenisation has already been done. For all I know, some adjustments might be getting done twice, once at home and again in the USA. The best I have got so far from our BOM is a suggestion to read a 1996 PhD thesis and that “after the data leave here we are no longer responsible for what other people do to them”.

I like the image. However, it is a bit misleading in that it implies that all of the stations shown produce records that are used in the global temperature calculation today. Unfortunately, only about 1/4 are used today.

To be sure, daily interpolation from highly correlated stations would be a preferrable means of filling short gaps in time series, rather than leaving the monthly average blank in the GHCN series and letting GISS produce its data sausage through “homegenization” of anomalies. The frequent problem, however, is that in many grid-cells there are long gaps to be filled and no nearby stations that are highly correlated.

I agree that on a physical basis, tens of kilometers, rather than 1200, is the typical radius of very high overall R^2. But I’ve found via cross-spectrum analysis of yearly station-averages that the rapid fall-off with distance is the result primarily of loss of coherence at the sub-Wolfe frequencies(i.e., f < 1/11yrs). At higher frequencies, coherence often remains high even at separations of 500km or more. This is the regional imprint of year-to-year temperature variations, which can be successfully exploited to patch time series on the same time scale.

But that cannot be accomplished by simply interpolating the anomalies via a distance-weighted scheme, because the anomalies at different stations are subject to different modulation by the sub-Wolfe components. It is at this point that the unexamined assumption of a broadly uniform regional climate, to which homogenization of anomalies can be applied, utterly fails. And trend-adjusting homogenization simply compounds the removal of the data product from any semblance of realism.

Lars (re: #50),

The patching/homogenization is performed not upon actual temperatures, but upon anomalies. The egregeous errors introduced thereby thus are entirely different in nature than what your zonal results show.