New code features.The program has now been split into two. The first is a preprocessor which reads v2.mean, does the merging of duplicates, and prints a condensed file called v2mean.txt. Unlike ver 1, it doesn't require prior editing of v2,mean. The idea is that once you have merged the duplicates, you don't need to do it again un til you want to deal with a new version of v2,mean.
The second part is somewhat like V1, but much more clearly laid out. It's even shorter (with the preprocessing gone) - about 170 lines. There's a lot of infrastructure with printed tables, plots with trends etc.
The selection process works thus. The 18 parameters from the .inv file have been given names, like tv.$country, listed in colnames. You declare a list of names that you'll want to use and have appear on graphs etc:
title = c("Globe","NH","SH","ConUS","Airport","Rural","Urban");
choices = 1:7 #c(4,5,6,7);
In the second line, you declare the ones you want to run.
The logic of the choices is expressed in the list:
if (tc == "NH") i01 = i01[cellnums>1296];
if (tc == "SH") i01 = i01[cellnums<1297];
if (tc == "Airport") i01 = i01[tv$airpt == "A"];
if (tc == "Rural") i01 = i01[tv$urban == "A"];
if (tc == "Urban") i01 = i01[tv$urban != "A"];That's my current list, but you can change it.
Some resultsFirstly, a table of trends, in deg C/decade.
No real surprises. The main outlier is the SH, but remember, this is land stations only. The classifications of airport, rural and urban make little difference. The urban/rural differentiation is by night lighting, and I've included small towns with urban.
Remember that this is based on raw GHCN data, with no corrections made by me.
Update - there was a bug in V 1.1, which had a minor effect on the results, but which could cause a crash. It didn't affect global calcs - only subregions. Here are the recomputed trends:
I've posted the results file on the document repository.