If AI driven weather forecasts start a revolution, then what will AI driven climate forecasts drive? Armageddon, the end is near riot?
No seriously, AI driven climate models might be a lot better than the current bias and ideology driven climate models.
While current weather forecasts often still can not predict weather accurately or reliably beyond 24 hours, will the AI driven weather forecasts be better?
"... But with Aardvark, researchers have replaced the entire weather prediction pipeline with a single, simple machine learning model. The new model takes in observations from satellites, weather stations and other sensors and outputs both global and local forecasts. This fully AI driven approach means that predictions are now achievable in minutes on a desktop computer.
When using just 10% of the input data of existing systems, Aardvark already outperforms the United States national GFS forecasting system on many variables and it is also competitive with United States Weather Service forecasts that use input from dozens of weather models and analysis by expert human forecasters.
One of the most exciting aspects of Aardvark is its flexibility and simple design. Because it learns directly from data it can be quickly adapted to produce bespoke forecasts for specific industries or locations, be that predicting temperatures for African agriculture or wind speeds for a renewable energy company in Europe. ...
This capability has the potential to transform weather prediction in developing countries where access to the expertise and computational resources required to develop conventional systems is not typically available. ..."
From the abstract:
"Weather prediction is critical for a range of human activities including transportation, agriculture and industry, as well as the safety of the general public. Machine learning is transforming numerical weather prediction (NWP) by replacing the numerical solver with neural networks, improving the speed and accuracy of the forecasting component of the prediction pipeline. However, current models rely on numerical systems at initialisation and to produce local forecasts, limiting their achievable gains.
Here we show that a single machine learning model can replace the entire NWP pipeline. Aardvark Weather, an end-to-end data-driven weather prediction system, ingests observations and produces global gridded forecasts and local station forecasts. The global forecasts outperform an operational NWP baseline for multiple variables and lead times.
The local station forecasts are skillful up to ten days lead time, competing with a post-processed global NWP baseline and a state-of-the-art end-to-end forecasting system with input from human forecasters.
End-to-end tuning further improves the accuracy of local forecasts. Our results show that skillful forecasting is possible without relying on NWP at deployment time, which will enable the full speed and accuracy benefits of data-driven models to be realised.
We believe Aardvark Weather will be the starting point for a new generation of end-to-end models that will reduce computational costs by orders of magnitude, and enable rapid, affordable creation of customised models for a range of end-users."
End-to-end data-driven weather prediction (no public access)
Aardvark weather: end-to-end data-driven weather forecasting (preprint, open access)
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