The researchers admitted that GraphCast relied on traditional methods to obtain quality data in the first place, and that the ECMWF’s High Resolution Forecast system can produce other types of forecasts that AI cannot yet.
“Our approach should not be regarded as a replacement for traditional weather forecasting methods, which have been developed for decades, rigorously tested in many real-world contexts, and offer many features we have not yet explored,” they concluded in their paper.
“Rather our work should be interpreted as evidence that [machine learning-based weather prediction] is able to meet the challenges of real-world forecasting problems and has potential to complement and improve the current best methods.”