Many papers are currently being published applying deep learning to geoscientific applications. However, most of them only offer proof of concept results on highly idealised scenarios. This paper combines deep learning approaches with the structural causal models popularized by the work of Judea Pearl, and it applies this methodology to a real problem, analyzing soil moisture-precipitation coupling in climate reanalysis data. In contrast to many papers in this field, this promises actual insight in the scientific application of the work.
Many papers are currently being published applying deep learning to geoscientific applications....
A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
A recent statistical approach for studying relations in the Earth system is to train deep...