Using interpretable gradient-boosted decision-tree ensembles to uncover novel dynamical relationships governing monsoon low-pressure systems

Hunt, K. M. R. ORCID: https://orcid.org/0000-0003-1480-3755 and Turner, A. G. ORCID: https://orcid.org/0000-0002-0642-6876 (2023) Using interpretable gradient-boosted decision-tree ensembles to uncover novel dynamical relationships governing monsoon low-pressure systems. Quarterly Journal of the Royal Meteorological Society. ISSN 1477-870X doi: 10.1002/qj.4582

Abstract/Summary

Low-pressure systems (LPSs) are the primary rainbringers of the South Asian monsoon. Yet, their interactions with the large-scale monsoon circulation, as well as the highly variable land and sea surfaces they pass over, are complex and generally not well understood. In this paper, we present a novel, top-down approach to investigate these relationships and quantify their importance in describing LPS behaviour. We also show that if the approach is sufficiently well posed, it is productive at hypothesis generation. For each of five predictands -- LPS intensification rate, propagation speed/direction, post-landfall survival, peak intensity, and precipitation rate -- we train an additive decision-tree ensemble model using the XGBoost algorithm. Shapley value analysis is then applied to the models to determine which variables are important predictors and to establish their relationship with the predictand, with additional analysis following cases of interest. Novel relationships established using this technique include: that LPS vorticity intensifies preferentially in the early morning at the same time as the peak in the diurnal cycle of their convection occurs, that vertical wind shear suppresses continued growth of strong LPSs, that large-scale barotropic instability plays an important role in both the inland penetration and peak intensity of LPSs, and that LPS propagation depends on the depth of its vortex with shallower LPSs advected by low-level winds and taller LPSs advected by mid-level winds. We also use this framework to identify and discuss potential new avenues of research for monsoon LPSs.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/113085
Identification Number/DOI	10.1002/qj.4582
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Uncontrolled Keywords	monsoon, machine learning, low-pressure systems, depressions, XGBoost, Shapley values, explainable AI
Publisher	Royal Meteorological Society
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