Verificación de pronósticos por ensamble en alta resolución generados a partir del Sistema de Asimilación de Datos y Pronóstico Numérico del Servicio Meteorológico Nacional en dos casos de estudio

Dan Yañez; Maria Eugenia Dillon; Paula Maldonado; Yanina Garcia Skabar

doi:10.24215/1850468Xe034

Authors

Dan Yañez Universidad de Buenos Aires, Argentina
Maria Eugenia Dillon Servicio Meteorológico Nacional, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
Paula Maldonado Servicio Meteorológico Nacional, Argentina
Yanina Garcia Skabar Servicio Meteorológico Nacional, Argentina

DOI:

https://doi.org/10.24215/1850468Xe034

Keywords:

ensemble forecasting, regional analyses, SAP.SMN

Abstract

This work seeks to evaluate the performance of high-resolution (4 km) probabilistic forecasts generated from the data assimilation and numerical forecasting system of the Argentinian National Meteorological Service (known by its Spanish acronym SAP.SMN), and to study the impact of the initial conditions considering forecasts initialized from regional (AReg) and global ensemble analyses (SAP.SMN-ENS). Two case studies were selected to analyze the performance of both forecasts, characterized by the development of deep convection and intense precipitation in central-eastern Argentina and Uruguay, which occurred on February 26-27 and March 6-7, 2022. To verify the probabilistic forecasts, data from the Satellite Quantitative Precipitation Estimation SQPE-OBS, maximum reflectivity from C-band radars, conventional stations, and radiosondes were used. In general terms, it can be considered that AReg showed better performance for case 1, while for the other case study it was SAP.SMN-ENS that had a better performance, indicating a significant impact of initial conditions on forecast performance. In both case studies, both experiments adequately represent convective systems compared to radar data, with certain differences in their position and intensity, resulting in an underestimation of accumulated precipitation in 24 hours in areas of maximum precipitation estimated by SQPE-OBS. These results are encouraging regarding the inclusion of local observations in the initial condition of the high-resolution numerical forecasts of the SAP.SMN-ENS and motivate to continue advancing in the development of a regional data assimilation system that allows improving the accuracy of forecasts, investigating aspects such as the configuration and assimilation strategy.

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