  As in many other areas of engineering and applied science, Machine Learning
(ML) is having a profound impact in the domain of Weather and Climate
Prediction. A very recent development in this area has been the emergence of
fully data-driven ML prediction models which routinely claim superior
performance to that of traditional physics-based models. In this work, we
examine some aspects of the forecasts produced by an exemplar of the current
generation of ML models, Pangu-Weather, with a focus on the fidelity and
physical consistency of those forecasts and how these characteristics relate to
perceived forecast performance. The main conclusion is that Pangu-Weather
forecasts, and possibly those of similar ML models, do not have the fidelity
and physical consistency of physics-based models and their advantage in
accuracy on traditional deterministic metrics of forecast skill can be at least
partly attributed to these peculiarities. Balancing forecast skill and physical
consistency of ML-driven predictions will be an important consideration for
future ML models. However, and similarly to other modern post-processing
technologies, the current ML models appear to be already able to add value to
standard NWP output for specific forecast applications and combined with their
extremely low computational cost during deployment, are set to provide an
additional, useful source of forecast information. .
