AI models can match traditional weather forecast systems at a fraction of the compute. See e.g.,
https://www.science.org/content/article/ai-churns-out-lightning-fast-forecasts-good-weather-agencies
and this excellent overview by Stephan Rasp:
But to date these systems are experimental and none is being used operationally- I.e. to make continuously updating ensemble forecasts based on newly ingested data.
Question will resolve yes if an AI model is used operationally by a group outside a traditional meteorological center.
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https://www.theverge.com/2024/12/7/24314064/ai-weather-forecast-model-google-deepmind-gencast?utm_source=chatgpt.com Fairly sure this matches the criteria. Google Deepmind's GenCast, "is accurate enough to compete with traditional weather forecasting".
Define AI model. This Instructables project from 2017 shows the process of building a rain prediction setup that takes in measurements and outputs probability of rain with machine learning. This paper from 2019 demonstrates a CNN model that can do ensemble forecasts. It seems that the authors of that paper would already satisfy "if an AI model is used operationally by a group outside a traditional meteorological center" if they had run their model on live weather data at any point. If I were to quickly make a ML model that takes in live weather data from an API then predict possible future states, even if it's often inaccurate, would that count?