Modern Meteorology

This is what amounts to forecast meteorology — a seasoned weatherman will look at the current charts and try to interpret them based on patterns that have been associated with previous observations. They appear to have equal powers of memory recall, pattern matching, and the art of the bluff.

I don’t do that kind of stuff and don’t think I ever will.

If this comes out of a human mind, then that same information can be fed into a knowledgebase and either a backward or forward-chained inference engine could make similar assertions.

And that explains why I don’t do it — a machine should be able to do it better.

What makes an explanation good enough? by Santa Fe Institute

Plausibility and parsimony are the 2P’s of building a convincing argument for a scientific model. First one has to convince someone that the model is built on a plausible premise, and secondly that the premise is simpler and more parsimonious with the data that any other model offered. As physics models are not provable in the sense of math models, the model that wins is always based on a comparison to all other models. Annotated from  DOI: 10.1016/j.tics.2020.09.013


One thought on “Modern Meteorology

  1. “Descriptiveness is generally taken to be an uncontroversial value: all other things being equal, good explanations make each piece of evidence seem more probable. This value has its limits, however, and overemphasis on descriptiveness in a domain where correlations really do matter results in a cognitive bias known as correlation neglect [22].”

    The above quote describes modern meteorology when weather forecasters wing it, such as Mr. Masiello above. He describes everything to meticulous detail (almost to the point of a “just-so story”), but the built-in correlation of many of the descriptions does not promote understanding. Just saying N equivalent things in different ways does not improve understanding.


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