Very cool! Nothing wrong with crude; something crude that exists is better than something polished that does not exist!
I am curious about your implementation of 'accuracy':
> How do I measure 'accuracy'?
> Very simply! I take the BBC's weather icons and compare them, using a bit of leeway. So if the prediction is 'Partly Cloudly', then 'Sunny Intervals' is also considered equivalent. Likewise, 'Light Showers', 'Light Rain' and 'Drizzle' are all considered close enough to be an accurate forecast.
> E.g. as I write this, the table below shows that the weather forecast for Cambridge one day ahead was 53% accurate. In other words, the BBC's guess about tomorrow's weather in Cambridge was right roughly half of the time.
So no partial credit, then? Check my understanding: I think that you're simply matching the title text of the icon. If it's a match (or in a small group of synonyms) that's a point, if it's not, you score zero for that prediction. Yesterday, the forecast for today was "Partly cloudy", today, the actual weather was "Sunny" - it gets no credit.
The parent article neural network is, apparently, scoring itself on matching the radar results pixel by pixel and color by color, which is pretty neat. I think it's particularly interesting if it's essentially general-purpose, taking in one collection of input pictures and outputting another, or whether they also gave it information on high and low pressure zones, prevailing winds, bodies of water and elevated land masses, and so on.
Regardless, what I personally want to know (and what I think most people want to know) from the weather forecast is whether it's going to be suitable for a particular activity. Obviously, the hard part is that the activities may vary for each consultation. If it's predicted to be partly cloudy and mild, and was actually sunny and hot, I'd be pleasantly surprised if I had scheduled a day at the beach, but disappointed if I was sweating while working on some landscaping. Farmers want it wet in the summer for growth and dry in the fall for harvesting, sailors want to know the minimum wind, painters want to know the maximum wind; everyone has different goals day by day.