DA Seminar: Machine learning for precipitation estimation and forecasting / Analysis of a Long-Lived Supercell: Life Cycle and Severe Weather Patterns in Northern Buenos Aires Province

Title: Machine learning for precipitation estimation and forecasting
Speaker: Dr. Juan Ruiz (University of Buenos Aires – CONICET)
Abstract: Estimating and forecasting precipitation is essential for a wide range of human activities as well as for disaster prevention. In this talk we will discuss the application of deep neural networks to the estimation of precipitation with high time and spatial resolution, combining remote sensors and numerical weather predictions. The proposed models show that these information sources can be effectively combined to improve the accuracy of real-time precipitation estimates. Additionally, we will present the application of deep neural networks as a postprocessing tool for short-range deterministic and ensemble-based numerical weather predictions and for the quantification of their uncertainty. The performance of the machine-learning models in the quantification of the uncertainty is close to that achieved by the dynamical ensembles and can be even better in the presence of a model.

Title: Analysis of a Long-Lived Supercell: Life Cycle and Severe Weather Patterns in Northern Buenos Aires Province
Speaker: Dr. Luciano Vidal (National Meteorological Service, Argentina)
Abstract: This work presents a detailed analysis of a long-lived convective supercell that affected the northern Buenos Aires province, Argentina, on March 19, 2024. The primary objective is to characterize its life cycle and associated severe weather patterns using an integrated multi-sensor approach. This methodology combines data from satellite imagery with documentation of surface damage caused by large hail and intense winds. The storm exhibited a remarkable longevity, traveling approximately 400 km over 5.5 hours and impacting a total of 11 municipalities before its dissipation. Throughout its trajectory, the supercell generated significant damage due to large hail and severe wind gusts that, in some areas, exceeded 150 km/h. Furthermore, the storm ultimately affected the Sarandí-Santo Domingo basin (the pilot basin of the Argentine-Japanese SATREPS/PREVENIR project) by generating flash floods. The results of this analysis provide crucial information for the improvement of forecasting and early warning systems for severe weather events in the region.