Hopefully this will by my last post on the Lampasas surface station. This will be a short post, since I think the figures speak for themselves. However, I will detail the methodology in case anyone wishes to have a look-see themselves. But first, when I looked at the “raw” yearly temperature data, this is what I see for Lampasas. There seems to be a problem with either there being lots of outliers being taken out, or the observer not getting the data to the proper people. There are many missing data in the monthly time series as well, but it’s easier to work around that.

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The Lampasas, Texas surface station has been under discussion lately. In 2000, this station moved near a parking lot. It has been argued that the recent sharp increase in temperature was due to this move. At the time, I had access to data only through 2005, and argued that since this increase was not seen in the raw data, any temperature changes were not the result of a station move, but by an adjustment made to the data. I now have the data from this station updated through 2007. The last two years of the raw data do indeed make it look like a “hockey stick”.

However, a station move should manifest itself as a near step function over a maximum of two years (in the yearly data). This is because unless the move occured on January 1st (or whenever the yearly data chooses its first date), the first year will have data values from both the old and new locations. It is not apparent to me why there would be a sudden increase in temperatures due to a station move after more than 5 years.
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My last two posts have been about the surface stations, and this one follows in its footsteps. This is also being discussed in two threads at ClimateAudit. Most of the discussion seems to center on how the temperatures are corrected for several known biases. The image above illustrates the importance of correcting for known biases.

This photograph is obviously of a road with several hairpin bends. There was a long exposure time, such that when the automobiles were driving towards the camera the lights appeared white, and when they were driving away from the camera they appeared red. Suppose you knew this a priori, and were interested in the reflective properties of the road in the visible portion of the spectrum. Without accounting for the artificial lights, the characteristics of the road are difficult to discern.
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Expanding upon a previous post about the surface station at Miles City, Montana, this entry will again look at that station, but will show what NASA GISS does to this data in its adjustments. A quick summary of the last post: I wanted to find a site that USHCN had classified as not being influenced by humans. They provide a product that describes the area around each surface station, and the Miles City location was classified as open farmland, grassland, or tundra out to at least 10 kilometers. One of the original reasons for looking at this station was because I wanted to look at the data from a ‘good’ station that had been profiled by surfacestations.org. A sample photo is provided below.
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