ON THE USE OF LIGHTNING DATA AS A PROXY FOR RADAR REFLECTIVITY OVER THE CENTRAL REGION OF ARGENTINA
Mailén Gómez Mayol, Luciano Vidal, Paola Salio y Maximiliano Sacco
Servicio Meteorológico Nacional
Centro de Investigaciones del Mar y la Atmósfera, CONICET-UBA UMI3351-CNRS-CONICET-UBA
Departamento de Ciencias de la Atmósfera y los Océanos, FCEyN, UBA
Departamento de Física, FCEyN, UBA
Manuscript received on November 14th, 2018, on final form on March 1st, 2019.
Storms and their associated phenomena have a high impact in a country’s social and economic environment. It is necessary to have tools that guarantee the safety of people at all times. The data provided by a network of meteorological radars is of utmost importance for storm tracking and identification. These radar networks are expensive and difficult to maintain. Not all countries have a radar network that covers all its extension and even so, they often have technical or connectivity problems that leave large areas without information about rainfall. In this work we present a technique to generate a synthetic radar reflectivity field using supervised deep learning techniques with cloud-to-ground lightning data. This product can be used as a cost-effective radar alternative in areas where there is no or poor coverage, or as a complement to meteorological radar images that are affected by attenuation, interference or other observation problems.