One thing I found really interesting abou the Graphcast paper (I appreciate this is not graphcast, but I think it is still relevant) is that it doesn't understand climate change. The model requires the training data to be recent to get the best quality projections.
While there are some factors that influence predictability in the weather forecast, as the fortran code is based on physics (at least in a broad sense), it doesn't suffer from those issues in the same way.
This doesn't mean that the ML forecasts are wrong (obviously), just different. Given the relative computational simplicity of running them, I wonder if the issue is not just expertise, but also understanding how they can best be used to generate reliable weather forecasts?
While there are some factors that influence predictability in the weather forecast, as the fortran code is based on physics (at least in a broad sense), it doesn't suffer from those issues in the same way.
This doesn't mean that the ML forecasts are wrong (obviously), just different. Given the relative computational simplicity of running them, I wonder if the issue is not just expertise, but also understanding how they can best be used to generate reliable weather forecasts?