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 by extension 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
attributed, to a large extent, to these peculiarities. Similarly to other
current post-processing technologies, ML models appear to be able to add value
to standard NWP outputs for specific forecast applications and combined with
their extremely low computational cost during deployment, will likely provide
an additional, useful source of forecast information.

在天气与气候预测领域，机器学习（ML）正对传统基于物理模型的预测模型提供有力的挑战，并取得了更高的预测准确性。本研究通过对当前 ML 模型中 Pangu-Weather 预测的准确性和物理一致性进行考察，发现相比于基于物理模型的预测，ML 模型在准确性指标上的优势主要是由于其独特性而产生的。此外，ML 模型在特定的预测应用中与传统数值天气预报输出具有协同作用，并且在实际应用中具有极低的计算成本，因此能够为预测信息提供一种有价值的额外来源。