Making the symbols for the data points "C", "I", and "T" might have been a good idea when they all shared the same graph space, but I can't see the point when they have separate spaces in a small multiple. I'd rather see a title on each graph in the series.

"I" and "T" were particularly unfortunate choices because when, as I do, I scanned the graph before looking at the text to see what was being presented, I thought the I was an error bar with no terminal dashes, and the T was an error bar with a terminal dash on the top.

I agree with derek. The small multiples are better compared using dots instead of letters. By visual inspection I cannot determine if the higher density of Cs is just a felt higher density because a C takes more space than a I. So I end up trying to count the letters to be sure.. Better use dots.

You might want to have a look at the lattice or ggplot packages for making it easier to do small multiple plots in R. (I'm the author of ggplot, so I'm biased as to which is better)

"...errors within 5 degrees, which is an arbitrary guideline for acceptable / unacceptable."

The NWS uses +/- 3°F error in its verification statistics.

It/s a given there will be errors in temperature forecasts. What/s left to decide is how much error is tolerable. NWS uses 3°F. The present analysis uses 5°F.

I interpret the data plots as follows:

LOW T forecast...
CNN: 8 too warm; 6 too cold
Intel: 8 too warm; 5 too cold
TWC: 5 too warm: 4 too cold

TWC had fewer _extreme_ errors; therefore...TWC was more accurate forecasting the LOW temperature.

HIGH T forecast...
CNN: 2 too warm; 2 too cold
Intel: 2 too warm; 1 too cold
TWC: 2 too warm: 2 too cold

All sources showed equal skill forecasting the HIGH temperature.

TQ, thanks for the counts. Those are measuring the variance, and it shows that using +/-5, the three were statistically the same.

What I found interesting was the lower bias shown by CNN. Errors typically could be due to bias or variance. Here, the bias is revealed in the evenness of the spread around (0,0).

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