Edited to add: I'll freely admit that the VM approach I took was an inelegant, "brute force" approach -- but it enabled me to cobble together existing software that I was familiar with to get something up and running fairly quickly. There's enough data-crunching going on in the background (on the order of 100 MB ASCII-formatted temperature data from thousands of stations has to be normalized, sifted, sorted, and averaged) that an all-in-browser or java/.net/whatever approach might not be feasible.
Some time ago, I released some software that I called "WattsBuster" -- it was named in honor of serial climate-disinformer Anthony Watts. Once it was set up and running, it would allow users to debunk all of Anthony Watts' favorite claims about the NASA/Hansen global-temperature work with a simple series o' mouse-clicks.
The big problem with it (which I was aware of at the time) was the "get it set up and running" part. It was way too much of a PITA to install/configure for most folks to bother with. (Those of us who live in a "computer nerd / programmer" bubble often tend to forget what "easy to use" is really supposed to mean).
Since then, I've been experimenting with ways to package the WattsBuster software up in a more user-friendly (and portable cross-platform) format.
The current solution I'm trying isn't ideal. It requires folks to download a pretty big "virtual machine appliance" file (which is about 1GB in size), and it still requires additional software to be downloaded and installed. But it is much less of a PITA than the previous version.
You will also need sufficient memory on your PC/Laptop -- 2GB or more is preferred, but you can get away with 1 GB if you shut down your other apps before you launch WattsBuster. You will also need approximately 4GB additional disk-space to expand the appliance file.
Here's a quickstart summary of the installation/configuration procedure.
1) Download the virtual-machine appliance file from http://tinyurl.com/...
2) Download and install Oracle's free VirtualBox virtualizer software from http://virtualbox.org. VirtualBox runs on most Macs and PC's (there are issues with pre 10.5 OS-X versions, unfortunately). VirtualBox is a snap to install -- just a quick series of mouse-clicks.
3) Launch VirtualBox and then import the appliance file via "File-->Import Appliance...". For any options that pop up, just accept the defaults. This process could take a couple of minutes.
4) Click on the VirtualBox "Start" button (at the top of the VirtualBox window).
5) Allow the virtual-machine to boot up in its own window -- wait for the Google Maps browser interface to appear. Once the Google Map display appears, give it another 30 seconds or so to make sure that the "back end" global-temperature calculator fully initializes.
6) To shut it down after use, simply right-click near the top of the virtual-machine desktop window. A popup menu with an "exit" entry at the bottom should appear. (It may require more than one try to get the right menu). Select "exit" to shut down the virtual machine cleanly. Simply closing the virtual-machine window could cause virtual damage to the virtual file-system ;).
7) The appliance file needs to be imported just once -- after that, you can start up and shut down the virtual machine at will via the VirtualBox control panel.
To whet your appetites, here's a link to a screenshot of WattsBuster in action (too big to embed here):
http://img818.imageshack.us/... -- disclaimer: This is an earlier version -- the new version has a few more control-panel features.
The software all runs in its own isolated "virtual machine" -- it cannot modify or harm your system in any way.
More details after the jump.
When everything is up and running, you will see a virtual Linux desktop display with Google-Map browser window. You will see several thousand placemarks on the Google Map display.
The placemarks represent GHCN (Global Historical Climatology Network) temperature stations. NASA and NOAA use data from these stations to compute their own global-average temperature results.
You will also see a popup "control" window with controls/buttons that will allow you to "filter" the stations you want to display. For example, if you want to display only stations that go back to 1900 or earlier, you can do that via the popup control panel.
You can right-click on the station markers to get popup window information about those stations (station name, rural/urban status, lat/long, etc.).
If you left-click on a station, a data plot window showing that station's raw (plotted in red) and adjusted/homogenized data (plotted in green) will appear. (That window is resizable, btw). In addition, the official NASA global land-temperature results are plotted in blue (for comparison purposes).
As you click on more stations, their data will be "averaged in" to produce your own global-average temperature estimates based on the stations you choose. As you click on each new station, your own temperature results will be updated "on the fly".
The upper plot in the data plot window shows the global-average temperature results. The lower plot shows how many of your selected stations actually reported data for any given year. Note that the number can be fractional -- if a station reports data for just 6 months in a given year, I count it as "half a station" for that year.
Since station data-record-lengths vary (some stations go back all the way to 1885, while others may go back only to 1940 or whatever), the number of stations reporting data will vary from year to year. So if your results look "lousy" for any given time period, look at the lower plot to see how many (or how few) stations actually reported data for that time period.
You can use the popup control panel to help select stations to "fill in" time-periods where you don't have enough stations for your global-average results to "settle down".
After a bit of experimentation, you will find that when you have at least 20-30 stations scattered around the world reporting data for any time period, your raw and adjusted data results will start converging very nicely to the official NASA results. You will soon find that it takes amazingly few stations to show a long-term warming trend that lines up nicely with the official NASA warming-trend.
You can also use the "batch plot" buttons at the bottom of the popup control-panel to generate results for all urban, all rural, and all displayed temperature stations (that way, you don't have to click on hundreds of stations individually).
Experiment around with the software a bit and you will be able to debunk virtually every claim that Watts and his fellow deniers have made about the global-temperature results, and you can do it right in front of skeptical friends'/family/co-workers' eyes.
UHI effect? Nope -- you will see that rural and urban stations produce very similar results. Data "homogenization" responsible for the warming trend? Nope -- once you've averaged enough stations together, you will see the raw and homogenized data results converge closely to the official NASA results.
Overall, the "homogenized" results will show a bit stronger warming trend than the raw data results will, but the differences will be quite modest. In fact, the results that my code produces from raw data will tend to match the official NASA results a bit more closely than my "adjusted" data results do. So much for the claims that NASA relies on data "adjustments" to show global-warming.
The global-temperature averaging algorithm I implemented is a "dumbed down" version of the NOAA gridding/averaging procedure -- in spite of its simplicity, you will see that it produces results that look remarkably similar to the official NASA results.
There are still a few rough-edges -- additional "polishing" is needed, and I'm sure that there are some lurking bugs/hiccups in the package -- but I think that it's currently working well enough for other folks to pound on it and check it out.
And although the "virtual machine" implementation is a bit of a resource-hog, I can still run it on my 6-year-old Linux laptop with only 1GB memory.
1:47 PM PT: Note -- I know that the VM approach is a rather inelegant "brute force" solution here, but it allowed me to cobble some data crunching/display tools that I'm familiar with to get something up and running without having to spend too much time "climbing a learning curve". Consider it more of a "proof of concept" package -- if this concept turns out to be promising enough, perhaps this could be turned into a real web-based app....