Phys.org  July 24, 2024 The impact of global climate changes on Tropical Cyclone Rainfall (TCR) is complex and debatable. A team of researchers in the US (Western Michigan State University, Stanford University, Perdue University, University of Utah, Caltech) used an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identified dust optical depth (DOD) as a key predictor that enhances performance evidently. The model uncovered a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This […]