In stark contrast to how deniers portray the situation, the level of openness and even ease of use regarding global temperature data is impressive. You (and the NASA GISS folks and David Archer and others as well) are to be commended.
Fantastic! Although I’ve done a fair amount of looking at the various data sets, even drilling down to station level on occasion, this is much easier than anything I was aware of previously. What a fun tool! Congratulations to the team who put this together.
Good point. I did try a range of options but with the underlying ocean and land colours it wasn’t easy. There is no meaning to the red/green other than to make a chequerboard to show where the grid boxes are.
If you save the .kml file, open in a text editor, you’ll see the definition of these colours is in the first segment of KML, lines 8 and 17. Feel free to edit this, save it and re-open in Google Earth.
As it currently stands, “grideven” boxes are <color>#804444ff</color> and “gridodd” boxes are <color>#8044ff44</color>
As UEA acknowledge in their “Notes (Questions and Answers)” the next step is to improve the accuracy of reporting of station locations. 0.1 degrees is around 11 km at the tropics. Stations which were originally reported as rural could now be urban.
#7 That’s an interpretation. What the note says is:
“A. The information we currently have about the latitude/longitude of each station is limited to 1 decimal place, so the station markers could be a few kilometres from the actual location. This is adequate for the construction of the CRUTEM gridded and global temperature records, because they do not depend on the precise location of each station. WMO/GCOS have asked member states to provide more accurate location details in future initiatives.”
This is an excellent move! To make it complete there still is a final layer of details missing: the raw unhomogenised station data. The station data currently provided are in some cases reconstructions pushing the start date of the station back using data from other stations which may even belong in other grid boxes.
It’s not a very polished app, but it is reasonably easy to set up and use. It’s also a bit of a big download @500MB, but if you have a decent Internet connection, you should be able to download it all in just a few minutes.
The app allows you to compute global-temperature estimates with your own “custom” selection of GHCN temperature stations and then directly compare your own results with the official NASA “meteorological stations” index (via a simple GnuPlot display).
You can select groups of stations based on rural/urban status, data record length, etc., or pick individual stations from a Google map browser front-end.
The app displays results for raw and adjusted GHCN data right alongside the official NASA results. The algorithm the app uses is a very “dumbed down” gridding/averaging routine — dumbed down enough, in fact, that it could be taught to first-year programming students. Even so, it produces global-average temperature results that are remarkably similar to the published NASA results.
The app consists of a “VirtualBox” virtual-machine appliance file with all software and temperature data in a preconfigured bundle. It will run on any newer (5 years old or less) Windows or Mac PC/laptop with at least 2GB memory.
“Quickstart” instructions are provided on the right side of the download page. (The “quickstart” procedure boils down to: Download/install VirtualBox from http://VirtualBox.org, import the app appliance file into VirtualBox, and then hit the VirtualBox “Start” button.)
I put the app together in an attempt to make it as easy as possible for others to see for themselves how incredibly robust the global temperature record is, and to be able to debunk the main “skeptic” claims about the global temperature record (i.e claims about UHI, “data manipulation”, “dropped stations”, etc.)
One interesting thing to try:
Compare the “Airport” vs “Not Airport” station results. What you will find is that much of the “bias” between raw and adjusted results disappears when you process only “Not Airport” stations.
The reason for this is a number of stations currently at airports started their lives located in city centers. During the mid-20th century, those stations were moved from city centers to (cooler) outlying airport locations.
As a result, the early temperatures for those stations were “biased high” (homogenization removes those station-move effects). Exclude those airport stations (easy to do with the station-selector control panel), and you will see much of that early “high bias” disappear from the raw temperature record.