Danielle Lafarga

Danielle Lafarga
M.S, Computational Science, Graduate

Cohort Level: Cohort - V

Career Goal: After the completion of my masters degree my goal is to continue contributing to climate research and furthering my education by pursuing a doctorate degree.

Expected Graduation Date: December 31, 2021

Degree: M.S Computational Science

Research Title: Ocean Temperature Data Reconstruction, Visualization and Feature Detections using Machine Learning

Research Synopsis: Sea surface temperature (SST) reconstruction is essential in areas like sea level predictions, and quantifying solar radiation. For this reason SST is studied more often than deep sea temperatures. More recently there has been more interest in deep ocean temperatures, but there has been a limitation due to the lack of measurement in those areas. Using machine learning our goal is to optimally reconstruct deep ocean temperatures from surface to 5,500 meters depth at ¼ degree spatial resolution and 10-day time resolution with 33 layers. Our next objective is to quantitatively detect and visualize significant ocean dynamic features, such as the water heat content of the cold deep ocean anomalies in the western Tropical Pacific.