Comment by dhx

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