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Extended Kalman Filter framework for forecasting shoreline evolution

Geophysical Research Letters

By:
,
DOI: 10.1029/2012GL052180

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Abstract

A shoreline change model incorporating both long- and short-term evolution is integrated into a data assimilation framework that uses sparse observations to generate an updated forecast of shoreline position and to estimate unobserved geophysical variables and model parameters. Application of the assimilation algorithm provides quantitative statistical estimates of combined model-data forecast uncertainty which is crucial for developing hazard vulnerability assessments, evaluation of prediction skill, and identifying future data collection needs. Significant attention is given to the estimation of four non-observable parameter values and separating two scales of shoreline evolution using only one observable morphological quantity (i.e. shoreline position).

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Extended Kalman Filter framework for forecasting shoreline evolution
Series title:
Geophysical Research Letters
DOI:
10.1029/2012GL052180
Volume
39
Issue:
13
Year Published:
2012
Language:
English
Publisher:
American Geophysical Union
Contributing office(s):
St. Petersburg Coastal and Marine Science Center
Description:
6 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Number of Pages:
6