Leonardo N. Lima is a Physical Oceanographer whose main work interests are ocean modelling and data assimilation, from research level to full operational implementations. His experience in data assimilation includes the application of simple and advanced methods to combine observations and results of ocean models, with the aim of correcting the numerical representation given by the model itself. As part of his Master’s degree in Geophysics, he applied a simple optimal interpolation to assimilate along-track sea level anomaly into the Hybrid Coordinate Ocean Model (HYCOM).

He worked at the Brazilian Oceanographic Modeling and Observation Network where he contributed to the implementation of the REMO Ocean Data Assimilation System (RODAS). He received his PhD in Meteorology from the National Institute for Space Research, Brazil. His Thesis involved an investigation of the model uncertainties and strategies of ensemble generation for data assimilation; in particular he applied the Local Ensemble Transform Kalman Filter (LETKF), an advanced ensemble-based method.

In 2018, he started as a postdoctoral researcher at the Euro-Mediterranean Center on Climate Change (CMCC) where he has been developing strategies to improve the Black Sea reanalysis system designed with the Nucleus for European Modelling of the Ocean (NEMO) for the Black Sea regional domain, and the variational data assimilation method called Oceanvar.