Quantifying the impact of new high-resolution ocean observations- such as autonomous gliders, coastal radar, or satellite imagery- is critical for the efficient deployment of observing infrastructure.
In this project, we’ll quantify how particular observing platforms contribute to ocean state estimates, allowing us to determine the most effective locations and parameters to observe, e.g. targeting extremely expensive ship-based sampling vs agile autonomous glider measurements to areas where they will add most value.
Data Assimilation (DA) is a powerful tool used to combine observations with a numerical model to produce a “best estimate” of the ocean state. We will perform a series of DA experiments to test the sensitivity of the estimated ocean state to various observation platforms. The results of this project will assist in guiding the types and location of observations that will best improve the model forecasts at the least cost. This project will be co-supervised by Dr Colette Kerry (UNSW), Prof. Brian Powell (U. Hawaii), Prof. Moninya Roughan (UNSW), and Dr Shane Keating (UNSW).
Particle image velocimetry can estimate ocean currents from the observed sea-surface temperature. Find out more.
Using a numerical model, this project investigates how data-streams improve model estimates.
We investigate the origins, pathways and conditions required for jellyfish to wash up on our shores.