Atmospheric and Space Scientists

However, tasks that require creativity or leadership remain mostly human jobs today. Designing new instruments or planning a research project involves original ideas and teamwork that AI can’t do on its own. Simple tasks like scheduling staff or collecting routine data can be partly automated, but even here people provide oversight.

In short, many forecasting and data tasks are augmented by AI tools, while human scientists still guide the work and communicate the results.

On the other hand, adopting new AI systems takes investment and testing. Meteorological agencies tend to trust tried-and-true methods, and laws around safety warnings mean experts usually must review forecasts. Socially, audiences still prefer a human voice – one expert notes that people would rather watch a real meteorologist than a computer avatar ^[1].

Because of this, we expect meteorologists to use AI as a tool, not a replacement.

Overall, AI in weather science is more of an assistant than a replacement. It can handle heavy data analysis and run supercharged simulations ^{[2] [3]}, letting human scientists focus on the creative and critical parts of the job. That means strong human skills – like understanding complex weather patterns and explaining them clearly – will remain valuable and in demand.

The good news is that AI can help make forecasts better and faster, so meteorologists can do even more important work with less drudgery ^{[1] [2]}.