By Kieran Hunt

Abstract 

In this talk, I will present a single, explainable machine-learning framework – XGBoost with Shapley value attribution – which I will briefly introduce and then apply to two problems in Indian meteorology. In the first case, this explainable AI framework is applied to monsoon low-pressure systems (LPSs) in order to identify brand new hypotheses about their behaviour: preferential early-morning intensification coincident with the diurnal convection peak over ocean; suppression of further growth by vertical wind shear; a substantive role for large-scale barotropic instability in inland penetration and peak intensity; and propagation set by vortex depth, with shallow (deep) LPSs steered by low- (mid-)-level winds. In the second case, state-level, population-weighted models are used to predict daily electricity demand from weather, achieving high skill (half of states r^2>0.8). Shapley analysis is then used to quantify the effects of weekdays/holidays, overnight minimum temperature and longer-term means, as well as threshold responses. Extending with reanalysis (1979–2023) reveals the largest demand–renewables deficits occur during/after monsoon withdrawal (Sept–Oct).