Assessment of two ensemble-based rapid-update data assimilation and short-range numerical weather prediction systems for Argentina

Abstract

Abstract PREVENIR—Forecast and Warning of Flash Flood Events is an Argentina-Japan cooperation project for five years from 2022 that aims to develop an early warning system for heavy rainfall and urban floods. The current operational numerical weather prediction system of the Argentine National Meteorological Service consists of deterministic and probabilistic forecasts at 4 km resolution. Since PREVENIR aims at more accurate and timely precipitation forecast, we developed a 2 km resolution 5-min update data assimilation system with the Local Ensemble Transform Kalman Filter (LETKF) that assimilates observation data from an automated weather station network and C-band Doppler weather radars. Independent LETKF systems have been coupled with the regional models’ Weather Research and Forecasting (WRF) and Scalable Computing for Advanced Library and Environment (SCALE), for two target basins: a mountainous region of the Suquía Villa Paez in Córdoba Province and a flat region of the Sarandí Santo Domingo in Buenos Aires. This study investigates the performance of the two systems in two extreme rain cases. We show that the forecasted precipitation benefits from the high-resolution rapid-update process, which improves its location and intensity with respect to coarser forecasting resources available in the region and also non-data assimilation systems. The results suggest that the prototypes could offer several advantages for hydrological applications, representing a potentially valuable tool for the region.