Services

Ocean Model Hindcast

Overview

Development of long-term hindcast systems for reconstructing historical ocean conditions using numerical models, observations, and atmospheric forcing. Hindcast models are run in chunks (e.g. every year) so that they can be run in parallel; however, the way chunking works is different among computer systems we use (e.g. HPC, AWS, or local server). Hindcast datasets provide the foundation for metocean assessments, operational analyses, environmental studies, and engineering design.

What I can provide

• Develop and implementation of ocean hindcast systems.
• Run hindcast model for the period ranging from month to decades.
• Hindcast models are run in chunks (e.g. every year) to run in parallel.
• Validate and calibrate the hindcast outputs against observations depending on data availability
• Cloud and HPC deployment (AWS, Slurm, PBS)

Example System

See Coastal Hydrodynamic Models for the models to be used for ocean hindcast and Hurricane Wave and Storm Surge Modeling for event-driven storm hindcast.

Deliverables

• Time series data for a point of client’s interest
• Extreme event reconstruction
• Operational analysis
• Statistical analyses

Metocean Data Analysis

Overview

I collect metocean data from various public available sources such as NDBC buoys, Copernicus Data Services, NOAA Satellite etc. If there is no data available, I will try to create it by developing and implementing ocean hindcast models such as wave and hydrodynamic models.

What I can do

• Time series Analysis
• Statistical Analysis
• Operational Analysis
• Extreme Value Analysis
• Model validation and calibration using Machine Learning

Methodologies

• Methodologies used for metocean data analysis are based on standards set by institutions, organizations, and government (e.g. API, ISO, DNV, NOAA)
• I develop tools to analyze metocean data more efficiently, but I often utilize publicly available tools and datasets. For example, for extreme value analysis, I use the tools based largely on pyextremes

Examples of Data Sources

• Copernicus Marine Data Services (CMEMS) [Waves, Current, Temperature and Salinity]
• NOAA National Buoy Data Center
• NOAA’s National Center for Environmental Information
• NOAA’s Climate Forecast System Reanalysis (CRSR)
• ECMWF’s ERA5 reanalysis data
• NOAA’s IOOS and regional IOOSs

Example System

See MOSAIC for the framework I use to run model validation and calibration systematically. I am also currently developing a metocean design criteria tool consisting of EVA, directional extremes, environmental contours, and operational analysis. Stay tuned.

What I can provide

• Design criteria
• Site characterization
• Extreme value analysis
• Model validation and calibration
• Machine learning based data calibration and gap filling
• Data quality assurance and quality control (QA/QC)
• Statistical analysis
• Environmental data integration

Scientific Software Development

Overview

I develop metocean tools based on Python to meet client needs. They can be python workflows consisting of a series of scripts or GUI based tools. They can be visualization tools, metocean analysis tools, modeling tools, or post-processing tools. I usually develop such tools on top of publicly available tools.

What I can provide

• Executable file or a series of Python scripts.

Examples

• TRITON
• Ocean Model Viewer
• MOSAIC

Shoreline Assessments

Overview

Shoreline assessments combine three complementary approaches: satellite-derived shoreline change analysis over both short-term (storm/event) and long-term (multi-year to decadal) timescales, regional sediment transport modeling (e.g. SCHISM-WWM) to characterize the wave, current, and sediment-transport processes driving that change, and physics-based beach morphodynamic modeling — using XBeach for short-term, storm-scale simulation of beach and dune response — checked against real survey data rather than idealized or “reasonable-looking” outcomes.

What I Can Provide

• Short-term and long-term shoreline change analysis from satellite imagery
• Regional sediment transport modeling (e.g. SCHISM-WWM) to characterize the wave, current, and sediment-transport drivers of shoreline change
• Beach and dune morphodynamic modeling, with XBeach for short-term/storm-scale response
• Nested regional-to-local modeling: a regional storm-surge and wave hindcast or forecast feeding a high-resolution beach-profile model
• Validation against pre- and post-storm topographic surveys (e.g. airborne lidar) and/or satellite-derived shorelines
• Shoreline change and dune-erosion risk assessment for coastal engineering and resilience planning

Example System

See Hurricane Ike Beach and Dune Erosion Modeling on Bolivar Peninsula (XBeach) for short-term, storm-scale XBeach modeling validated against airborne lidar, and Fine Sediment Dynamics for modeling waves and hydrodynamics in combination with sediment dynamics of sand and mud.

Typical Applications

• Coastal resilience and vulnerability assessment
• Beach nourishment and dune restoration design
• Storm-impact and inundation risk assessment
• Coastal infrastructure siting and permitting support