Well today is a perfect example. A cold front is moving over the North Island of New Zealand.
I planned a flight over the Coromandel peninsula for 11AM to 14:00 PM and had to cancel due to various parameters exceeding my personal limits to fly.
Wind at 2000ft amsl was forecast for 24kt rising to 30kt with low clouds and poor visibility.
So I took the car instead. A 4 hour drive both ways instead of 45 minutes flying. However, the forecast cold front was stalled over the central North Island and none of the bad weather over the Coromandel eventuated. While driving the planned route I observe cloud base 3000 ft plus and visibility over 30km with winds calm, below 5 kts at ground level.

I think the models you source your forecasts from will never get it right unless taking into account the effects of weather manipulation. Weather manipulation typically with cloud seeding, increases atmospheric instability. If the forecast models (driven by AI?) makes adjustments to stability factors by "learning" from observed weather vs forecast models, the factors will sometimes be right for when cloud seeding takes place, and other times be way off when no weather manipulation takes place. Since these models do not know when or where cloud seeing takes place, they cannot do a good job of consistently forecast correctly. The Artificial un-intelligence just chases its tail, and the algorithm actually never converges on anything near reality.

The only useful source of weather data seems to be weather radar, and actual observed weather. From this I can form a mind picture of what the weather is doing and how fast it is moving. OF course this does not help with long range forecast.