CoSMoS-COAST: The Coastal, One-line, Assimilated, Simulation Tool of the Coastal Storm Modeling System
July 26, 2023
A software release of CoSMoS-COAST: The Coastal, One-line, Assimilated, Simulation Tool of the Coastal Storm Modeling System. The software (coded in Matlab) encapsulates a long-term shoreline change model that combines longshore and cross-shore transport and assimilates historical shoreline observations.
Citation Information
| Publication Year | 2023 |
|---|---|
| Title | CoSMoS-COAST: The Coastal, One-line, Assimilated, Simulation Tool of the Coastal Storm Modeling System |
| DOI | 10.5066/P95T9188 |
| Authors | Sean F Vitousek |
| Product Type | Software Release |
| Record Source | USGS Digital Object Identifier Catalog |
| USGS Organization | Pacific Coastal and Marine Science Center |
Related Content
A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions
Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, “littoral-cell”-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise (SLR), and other natural and anthropogenic processes. We
Authors
Sean Vitousek, Kilian Vos, Kristen D. Splinter, Li H. Erikson, Patrick L. Barnard
Related Content
A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions
Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, “littoral-cell”-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise (SLR), and other natural and anthropogenic processes. We
Authors
Sean Vitousek, Kilian Vos, Kristen D. Splinter, Li H. Erikson, Patrick L. Barnard