Characterizing Uncertainty in Deep Convection Triggering Using Explainable Machine Learning

Greta Miller, Philip Stier, Hannah M. Christensen

Journal of the Atmospheric Sciences · 07 April 2025 · University of Oxford

In one sentence. Random forest predicts deep convection triggering with SHAP clustering to characterize uncertainty, outperforming conventional triggers and suggesting use as a stochastic parameterization.

Abstract

Abstract Realistically representing deep atmospheric convection is important for accurate numerical weather and climate simulations. However, parameterizing where and when deep convection occurs (“triggering”) is a well-known source of model uncertainty. Most triggers parameterize convection deterministically, without considering the uncertainty in the convective state as a stochastic process. In this study, we develop a machine learning model, a random forest, that predicts the probability of deep convection, and then apply clustering of Shapley additive explanations (SHAP) values, an explainable machine learning method, to characterize the uncertainty of convective events. The model uses observed large-scale atmospheric variables from the Atmospheric Radiation Measurement constrained variational analysis dataset over the Southern Great Plains, United States. The analysis of feature importance shows which mechanisms driving convection are most important, with large-scale vertical velocity providing the highest predictive power for more certain, or easier to predict, convective events, followed by the dynamic generation rate of dilute convective available potential energy. Predictions of uncertain, or harder to predict, convective events instead rely more on other features such as precipitable water or low-level temperature. The model outperforms conventional convective triggers. This suggests that probabilistic machine learning models can be used as stochastic parameterizations to improve the occurrence of convection in weather and climate models in the future. Significance Statement Convective storms, which produce clouds and precipitation, are difficult to represent in models since they occur at scales smaller than a model grid box. The purpose of this study is to better understand why convection is sometimes easier or harder to predict with certainty. This is important because predicting where and when convection occurs in atmospheric models affects the energy, moisture, and momentum processes in these models, which is known to lead to errors in weather forecasts and climate projections. This work highlights the importance of representing uncertainty in processes like convection.

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