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 indicated a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlighted meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. According to the researchers these findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms… read more. Open Access TECHNICAL ARTICLE
Major meteorological impacts on tropical cyclone rainfall. Credit: SCIENCE ADVANCES, 24 Jul 2024, Vol 10, Issue 30