Name
Abstract | ||
|---|---|---|
Coast-fitting tomographic inversion that is based on function expansion using three types of normal modes (the Dirichlet, Neumann, and open boundary modes) is proposed to reconstruct current fields from the coastal acoustic tomography (CAT) data. The superiority of the method was validated while using CAT data that were obtained in 2015 in the Dalian Bay. The semidiurnal tidal and residual current fields were accurately reconstructed over the entire model domain surrounded by coasts and open boundaries. The proposed method was effective, particularly around the peripheral regions of the tomography domain and the near-coast regions outside the domain, where accurate results are not expected from the conventional inverse method based on function expansion by Fourier function series with no coast fittings. The error velocity for the semidiurnal tidal currents was 2.2 cm s(-1), which was calculated from the root-mean-square-difference between the CAT-observed and inverted range-averaged currents that were obtained along the nine peripheral transmission paths. The error velocity for the residual currents estimated from the 12-h mean net residual transport at the bay mouth was 0.9 cm s(-1). The errors were significantly smaller than the amplitude of the tidal and residual currents. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20020558 | SENSORS |
Keywords | Field | DocType |
coastal acoustic tomography,coast-fitting tomographic inversion,semidiurnal tidal current,residual current,current-field mapping,net volume transport | Residual,Inversion (meteorology),Electronic engineering,Fourier transform,Tomography,Function series,Bay,Engineering,Normal mode,Amplitude,Geodesy | Journal |
Volume | Issue | ISSN |
20 | 2.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minmo Chen | 1 | 0 | 1.01 |
| Ze-Nan Zhu | 2 | 0 | 0.34 |
| Chuanzheng Zhang | 3 | 0 | 0.34 |
| Xiao-Hua Zhu | 4 | 0 | 0.68 |
| Min Wang | 5 | 0 | 0.34 |
| Xiaopeng Fan | 6 | 0 | 0.34 |
| Ruixiang Zhao | 7 | 0 | 0.34 |
| Ju Lin | 8 | 0 | 0.34 |
| Arata Kaneko | 9 | 1 | 2.99 |