John, with a more technical audience here, can you describe what kind of inconsistencies you found, and how your code was built to detect them? (I mean, building consistency checks into code is easy; the art is in knowing what kind of inconsistency those checks should be looking for)
The average temperature between 1961 and 1990 (which is the critical baseline for the anomaly charts that are drawn showing global warming) was incorrectly calculated for a large number of stations (observing points) in Australia, New Zealand and other parts of Oceania.
Each of the station files contains the observations from the station per year and per month, as well as the calculated average (called the normal) and standard deviation.
I was concerned that as I read and parsed the Met Office data I was make some sort of standard cock-up like putting the wrong months in the wrong years so I used the averages and standard deviations as a double check since they are based on known date ranges.
John, congratulations for the friendly email! (wondering actually who you emailing next:) Glad to see a .pl making headlines! You obviously spent some time with the data and I would appreciate it, if you can shed some light on the 'average'. Does the average really represent a fair average over the earth's surface? How is it exactly calculated? Are the met files raw data or is it data that has gone through some homogeneity tests?
I don't think I can really do your questions justice. I would suggest as a first port of call the paper that describes how the Met Office generates CRUTEM and HADCRUT.
That paper is long and covers how the Met Office generates the trend data you see, how the data is modified and gridded, how error estimates are generated, etc.
