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Evaluation of Coastal Altimeter Wave Height Observations Using Dynamic Collocation.
- Source :
- IEEE Transactions on Geoscience & Remote Sensing; Aug2022, Vol. 60, p1-8, 8p
- Publication Year :
- 2022
-
Abstract
- With the development of altimeters’ retracking algorithms, the signal-to-noise ratio of altimeter data in coastal regions has been improved significantly in recent years. However, due to the complexity of coastal waves, the traditional satellite-buoy/satellite-satellite collocation method using a spatial-temporal window, which is widely used in the evaluation of significant wave height (SWH) measurements of altimeters in the open ocean, is not reliable because coastal waves can vary significantly even in a small window. This makes it difficult to quantitatively evaluate the errors of altimeter coastal SWH observations. This study proposes an automated dynamic collocation method to solve this problem. The method combines SWH from coastal buoy observations and numerical wave model (NWM) outputs to generate an SWH reference field. The coastal altimeter observations can then be compared with this reference dataset. To test the effectiveness of the method, the SWH data from Sentinel-3A (S3A) synthetic aperture radar (SAR) mode in the coastal region of southwest England were evaluated. The results indicate that the coastal SWHs from the S3A SAR mode is good quality with an overall root mean square error of about 0.3 m when the offshore distance is more than 10 km. The results also show that the presented method works better than previous methods for the evaluation of coastal altimeter SWH observations. This method can be applied in future studies to evaluate and improve the performance of other coastal remotely sensed SWH observations. [ABSTRACT FROM AUTHOR]
- Subjects :
- COLLOCATION methods
ALTIMETERS
SYNTHETIC aperture radar
STANDARD deviations
Subjects
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 60
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 159194990
- Full Text :
- https://doi.org/10.1109/TGRS.2022.3198430