Models
These are some of the computational models I have helped develop or use for my research. I leverage the power of cloud computing, HPC systems, and modern code frameworks, and adapt multiple analysis methods including dynamical models, machine learning, statistical methods, and field observations.
Global Coupled Coastal, Fluvial, and Pluvial Flood Model
A global flood hazards model incorporating climate change effects. Applies downscaled forcings (precipitation, sea level rise, etc.) to simulate compound flood risk across coastal and inland environments.
Machine Learning / Data Science — Emulator Models
Emulator models for ocean and flood prediction, including statistical models of extreme value distributions. These approaches dramatically reduce computational cost while preserving physical fidelity for large-ensemble climate studies.
Princeton Ocean Model (POM)
POM is a simple-to-run yet powerful ocean modeling code for simulating a wide range of problems, from small-scale coastal processes to global ocean climate change. POM is a sigma coordinate (terrain-following), free surface ocean model with embedded turbulence and wave sub-models, and wet-dry capability (Blumberg and Mellor, 1987).
COAWST (ROMS / SWAN / WRF)
The open-source Coupled Ocean Atmosphere Wave and Sediment Transport (COAWST) modeling system (Warner et al. 2010) couples an atmospheric model (WRF), wave model (SWAN), three-dimensional circulation and stratification model (ROMS), and sediment transport model. It has been validated across a variety of applications including wave-current interaction in the surf zone, tidal inlets, atmospheric-ocean-wave interactions under hurricane forcing, and sediment dispersal in shallow semi-enclosed basins.
SUNTANS
The Stanford Unstructured-grid, Nonhydrostatic, Parallel coastal ocean model. Designed for simulation of nonhydrostatic flows at high resolution in estuaries and coastal seas (Fringer et al., 2006). SUNTANS is also well-suited to model internal waves in both field and idealized scenarios, with numerous publications to date. A multiscale nested version is available for coupling to larger-scale models. Code available on GitHub: rogersjs77/suntans.
XBeach
A two-dimensional model for wave propagation, long waves and mean flow, sediment transport, and morphological changes of the nearshore area, beaches, dunes, and backbarrier during storms.
PCUI
A direct numerical simulation (DNS) incompressible flow solver originally developed by Zang et al. (1994) and later parallelized with MPI by Cui (1999). The governing equations are discretized using a finite-volume method on a non-staggered grid in general curvilinear coordinates. Most recently adapted for channel flow using an enhanced spin-up algorithm (Nelson and Fringer, 2017).
Ocean Instrument Processing
A collection of scripts for processing instrument data from field oceanographic studies, including MATLAB code for working with data from common oceanographic instruments (ADCPs, CTDs, pressure sensors, etc.). Available on GitHub: rogersjs77/ocean_instrument_processing.
FunwaveC
A time-dependent Boussinesq model with equations similar to the nonlinear shallow water equations but including higher-order dispersive terms. FunwaveC has been used to study a variety of surfzone processes including cross-shore tracer dispersion, surfzone drifter dispersion, spectral wave transformation, mean currents, surfzone eddies, shoreline runup, and net circulation cells on coral reef spur and groove formations (Rogers et al., 2013).