This package simulates data collected by autonomous, remotely operated, or manned vehicles in the Ocean. The simulated robots readily have three-dimensional navigation (e.g. isopycnal) and flexible sampling (e.g. profiles) capabilities. The package can also ingest real data collected by such means, which enables effective model-data combination workflows (e.g. model training, state estimation, UQ).
OceanRobots.jl leverages the IndividualDisplacements.jl package for simulating robot trajectories in the ocean and the ArgoData.jl package for collecting vertical profiles of ocean variables along the way.
The initial focus is on simulating major data sets being collected by the Argo array of profiling floats (physical oceanography, chemistry, and ecology), Global Drifter Program (surface buoys), glider deployments (underwater vehicles), and repeat hydrography (ships).
Envisioned applications include:
- model training, state estimation, uncertainty quantification
- oceanography field experiment planning, optimization of observing systems
- monitoring, navigation, and programing of robots deployed in the real Ocean
I work as a reseach scientist at the Massachusetts Institute of Technology (MIT) where I investigate oceanography and climate. As part of the Department of Earth, Atmospheric and Planetary Sciences, my work focuses on ocean modeling and the analysis of global ocean data sets such as Argo profile collections, satellite records of sea level, or ocean color retrievals. I co-develop computer programs in various languages and carry out ocean state estimation using the MIT general circulation model in order to interpolate and interpret ocean observations. My scientific interests include: ocean circulation and climate variability; tracer transport and turbulent transformation processes; interaction of ecological, geochemical, and physical processes; global cycles of heat, water, and carbon; observational statistics; forward and inverse modeling.