The ocean is vast, highly dynamic, and incredibly complex, making its study a continuous challenge. To analyze its variability across spatial and temporal scales, physical oceanography relies on a wide range of methods and tools. These include theoretical approaches, observational techniques, numerical modeling, and data-driven methodologies, all of which help us better understand oceanic processes.
In this section, we highlight some of the most essential and widely used methods and tools in physical oceanography. While it is nearly impossible to cover everything, our aim is to provide a curated selection of key resources that facilitate ocean research and exploration.
Any suggestion is very welcome, so please contact us if you have any method or tool you would like to add to this collection.
Numerical Modeling
Numerical modeling is a fundamental tool in physical oceanography, allowing researchers to simulate and predict ocean dynamics across multiple spatial and temporal scales. Given the complexity and vastness of the ocean, models help bridge observational gaps, test hypotheses, and improve our understanding of key processes. From global circulation models to high-resolution simulations of mesoscale and submesoscale features, numerical modeling plays a crucial role in advancing ocean science. As computational power increases, so does our ability to develop more accurate and detailed representations of the ocean system.
- Coupled atmosphere-ocean models
- Mesoscale and submesoscale model
- ROMS (Regional Ocean Modeling System)
- HYCOM (HYbrid Coordinate Ocean Model)
- MITgcm (MIT General Circulation Model)
- FVCOM (Finite Volume Community Ocean Model)
- NEMO (Nucleus for European Modelling of the Ocean)
- MOM (Modular Ocean Model)
- POP (Parallel Ocean Program)
- POM (Princeton Ocean Model)
- VerOS (Versatile Ocean Simulation)
Lagrangian Models and Particle Tracking
Lagrangian models play a crucial role in physical oceanography by simulating the movement of water parcels, particles, and organisms within ocean currents. Unlike Eulerian models, which describe ocean dynamics at fixed points in space, Lagrangian approaches follow individual elements as they drift, allowing for a detailed representation of transport processes such as pollutant dispersion, larval connectivity, and water mass pathways.
These models are widely used in applications ranging from oil spill tracking and plastic pollution studies to biological connectivity and sediment transport. Several computational tools have been developed to support Lagrangian simulations, including:
Machine Learning and Artificial Intelligence
The increasing availability of high-resolution observational and model data has driven the integration of Machine Learning (ML) and Artificial Intelligence (AI) into physical oceanography. These techniques allow for automated pattern recognition, predictive modeling, and improved data assimilation, helping researchers analyze complex ocean dynamics more efficiently.
ML and AI methods have been applied to tasks such as eddy detection, ocean front identification, anomaly detection, and data gap filling, complementing traditional physics-based approaches. Recent advances have led to two major trends:
- Deep Learning. Some examples are the following (further reading can be found here):
- Hybrid Models. Hybrid models that integrate ML/AI with traditional physical models are emerging as powerful tools in oceanographic research. These models combine the rigor of physical laws with the predictive power of ML algorithms, improving the accuracy and efficiency of simulations. A notable example is the approach that combines ML with Atmospheric General Circulation Models (AGCMs), allowing for the capture of dynamic processes that traditional models struggle to represent accurately, as presented here. Additionally, hybrid models incorporating physical constraints into neural networks, known as Physics-Informed Neural Networks (PINNs), have been developed to enhance prediction accuracy in fluid dynamics, as compiled here.