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Aerosols are important modulators of the precipitation-generating process, with their concentrations potentially affecting the precipitation process in extreme events. Existing literature suggests that, through microphysical processes, additional aerosols lead to a larger number of smaller cloud droplets, which eventually redistributes the latent heat and the precipitation process. This research addresses the question of how sensitive the spatial and temporal patterns of heavy precipitation events are to aerosol concentration. National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) final (FNL) data were used as input to the Weather Research and Forecasting (WRF) model, to simulate the case study of the catastrophic 2016 flood in Louisiana, USA, for three aerosol loading scenarios: virtually clean, average, and very dirty, corresponding to 0.1×, 1×, and 10× the climatological aerosol concentration. Overall, for the extreme precipitation event in Baton Rouge, Louisiana, in August 2016, increasing aerosol concentrations were associated with 1) a shifted peak precipitation period; 2) a more intense and extreme precipitation event in a more confined area; 3) greater maximum precipitation. Results are important in improving forecast models of extreme precipitation events, thereby further protecting life and property, and more comprehensively understanding the role of aerosols in heavy precipitation events. 

Abstract In groundwater-limited settings, such as Puerto Rico and other Caribbean islands, societal, ecological, and agricultural water needs depend on regular rainfall. Though long-range numerical weather predication models explicitly predict precipitation, such quantitative precipitation forecasts (QPF) critically failed to detect the historic 2015 Caribbean drought. Consequently, this work examines the feasibility of developing a drought early warning tool using the Gálvez–Davison index (GDI), a tropical convective potential index, derived from the Climate Forecast System, version 2 (CFSv2). Drought forecasts are focused on Puerto Rico’s early rainfall season (ERS; April–July), which is susceptible to intrusions of strongly stable Saharan air and represents the largest source of hydroclimatic variability for the island. A fully coupled atmosphere–ocean–land model, the CFSv2 can plausibly detect the transatlantic advection of low-GDI Saharan air with multimonth lead times. The mean ERS GDI is calculated from semidaily CFSv2 forecasts beginning 1 January of each year between 2012 and 2018 and monitored as the initialization approaches 1 April. The CFSv2 demonstrates a broad region of statistically significant correlations with observed GDI across the eastern Caribbean up to 30 days prior to the ERS. During 2015, the CFSv2 forecast a low-GDI tongue extending across the Atlantic toward the Caribbean with 60–90 days lead time and placed Puerto Rico’s 2015 ERS beneath the 15th percentile of all 1982–2018 ERS forecasts with up to 30 days lead time. A preliminary GDI-based QPF tool tested herein is a statistically significant improvement over climatology for the driest years.