Understand
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.