Deep learning for global oceanic data assimilation

Seminar by Prof. Yoo-Geun Ham from Chonnam National University

01 February 2024
KST 14:00 – 15:00

The Seminar is being held in Room 1010 (Jasmin) – Integrated mechanical engineering building. Click here for the campus map.

The oceanic data assimilation (DA) system, essential for understanding and forecasting global climate variabilities, blends prior information from numerical models with observed data to create the best possible estimates and their uncertainties of oceanic conditions. We developed DeepDA, a global oceanic DA system using deep learning, by integrating a partial convolutional neural network and a generative adversarial network (GAN). The partial convolution serves as an observation operator, mapping irregular observational data onto gridded fields, while the GAN incorporates observational information from previous time frames. Our observing system simulation experiments revealed that DeepDA significantly reduces the analysis error in three-dimensional temperature measurements, outperforming both background and observed values. DeepDA’s global temperature reanalysis from 1981-2020 accurately reconstructs observed global climatological fields, seasonal cycles, major oceanic temperature variabilities, and the global warming trend. Developed solely with a long-term control simulation, DeepDA lowers the technical hurdles in creating global ocean reanalysis datasets using various numerical models’ physical constraints, thereby diminishing systematic uncertainties in estimating global oceanic states over decades with these models.