One of the most fundamental mistakes which wannabee scientists make, is to attempt to apply precision to a problem which hasn’t been sanity tested for accuracy.
It should be obvious to everyone that the current data set is not sane.
What I am doing is what professionals call “sanity testing” – and the adjusted data fails miserably. The problems are too numerous to enumerate, but here are a few of them.
1. Anomalies, infilling and gridding.
The image below represents a grid cell. The U’s stand for Urban, and the R’s stand for rural. Let’s say that the U anomaly is 1.0 and the R anomaly is 0.0. The average anomaly of this grid cell is 6/8 = 0.75
Now, let’s take away one of the rural stations – as has been happening to the network. Because of infilling, the rural station now gets effectively counted as an urban station – due to it being infilled…
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