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Application of a Sequential Data Assimilation Technique to Improve Modeling of Surface Currents Using Radar Data at a Coastal Domain
Author(s) -
Lei Ren,
Michael Hartnett
Publication year - 2018
Publication title -
epic series in engineering
Language(s) - English
Resource type - Conference proceedings
ISSN - 2516-2330
DOI - 10.29007/w7nn
Subject(s) - data assimilation , radar , hindcast , computer science , remote sensing , data modeling , assimilation (phonology) , meteorology , algorithm , geology , machine learning , telecommunications , geography , database , linguistics , philosophy
Numerical model is generally to simulate hydrodynamic parameters such as surface currents. However, it has limits such as difficulty in definition of initial and boundary conditions. As remote sensing such as satellite and radars advances and is applied in practice. Data assimilation technique has becoming a promising means to improve modeling performance through taking advantages of available observations. In this paper, surface currents hourly monitored by a radar system were assimilated into a 3D numerical model to improve modeling performance using a sequential data assimilation algorithm. Results indicated that application proposed data assimilation approach not only improved hindcasting of surface flow fields, but also improved its forecasting.

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