I think it was a stats class where I learned this, but as it turns out bad weather is less common than good weather. To be a fairly accurate weather person, you merely need to say "there will be no precipitation" and you'll be right like 90% of the time anywhere on earth.

What makes that funny is that historically, weather forecasters have been less than 90% accurate.

Now, I will say that today's weather models are pretty dang amazing. The 10 day forecast rarely wrong for me.

Take for example Chongqing, China which is one of the world's cloudiest and most overcast cities[1]. Easily confirmed by lack of cloudless satellite imagery.[2] You could get the forecast correct most of the time by just assuming it will be cloudy.

What is more interesting for meteorological forecasting is the time-sensitive details such as:

1. We know severe storms will impact city X at approximately Ypm tomorrow. Will it include large hailstones? Severe and destructive downdraft / tornado? What path will the most damage occur and how much notice can we provide those in the path, even if it's just 30min before the storm arrives?

2. Large wildfire breaks out near city X and is starting to form its own weather patterns.[3] What's the possible scenarios for fire tornadoes, lightning, etc to be formed and when/where? Will the wind direction change more likely happen at Ypm or Y+2pm?

I'm skeptical that AI models would excel in these areas because of the time sensitivity of input data as well as the general lack of accurate input data (impacting human analysis too).

Maybe AI models would be better than humans at making longer term climate predictions such as "If [particular type of ENSO/IOD/etc event] is occurring, the number of cloudy days in [city] is expected to be [quantity]/month in [month] versus [quantity2]/month if the event was not occurring." It's not that humans would be unable to arrive at these type of results -- just that it would be tedious and resource intensive to do so.

[1] https://en.wikipedia.org/wiki/List_of_cities_by_sunshine_dur...

[2] https://imagehunter.apollomapping.com/search/90e4893eeeaa48a...

[3] https://en.wikipedia.org/wiki/Cumulonimbus_flammagenitus

I regularly encounter days when today's forecast is wrong and even in conflict with the current situation.

E.g. the weather app tells me there's a drizzle all day and currently and yet it's entirely dry. The opposite happens too.

Days of rain often shift in increments of days one or two days before as well.

I'd say it's location specific how accurate predictions are.

I heard it (again, I think it was a stats class) as:

Forecasting that what is happening today, will happen tomorrow, was an almost insurmountable baseline for early forecasters.

A bit like trying to come up with psych drugs that can beat a placebo. Although, placebos are particularly effective for psych treatments.

I think this depends a lot on the region. I find that forecast quality differs widely from region to region. My guess is that it's a matter of (1) some regions have less advanced models available to them and (2) some regions have fundamentally more complex and unpredictable weather patterns.

Concrete if anecdotal example: weather forecast in SF are fairly accurate but the weather patterns are also simple to predict with the Pacific High and the simpler high level mechanics at play. Weather forecasts in Seoul are quite often completely wrong, but the weather patterns are also much more dynamics at a macro level with competing large systems in China/Gobi desert and the Western Pacific.

I'm not a meteorologist, just a sailor who likes to look at weather.

Your fairly accurate weather person is going to have to stay away from Vancouver / the Pacific Northwest ;-)

I think false negatives (i.e. it rains when it's not supposed to) are both more bothersome and noticeable, so your weather person won't be very popular.