The PREVENIR rapid-update data assimilation and short-range numerical weather prediction system prototype: an urban flood case study over Buenos Aires.

One of the most critical tools to mitigate the impact of urban flash floods is having an effective and timely early-warning system. The Argentine National Meteorological Service (SMN) is actively working in this direction through the PREVENIR Argentina-Japan cooperation project, which aims to develop an impact-based early-warning and emergency management system for urban flash floods in two Argentine target basins by 2027. As the current SMN operational system consists of 4-km resolution deterministic and warm-start probabilistic forecasts, to provide a more accurate and timely precipitation forecast, under PREVENIR, we are developing a higher-resolution (2-km), rapid-update data assimilation and numerical weather forecasting system coupling the Local Ensemble Transform Kalman Filter (LETKF) with the Weather Research and Forecasting (WRF) model. The system ingests local data from automated surface weather stations and C-band Doppler weather radars to obtain a 40-member analysis ensemble every 5 minutes, and 10-h 20-member extended forecasts every 30 minutes. This work aims to evaluate the performance of the WRF-LETKF prototype system based on a 4-day case study of almost continuous precipitation over the Buenos Aires region in March 2024, which led to urban floods in one of the pilot basins. A preliminary comparison with Radar Quantitative Precipitation Estimation (RQPE) indicates a good performance of the precipitation forecasts and added value for early warning and decision-making.