The Wide Swath Significant Wave Height: An Innovative Reconstruction of Significant Wave Heights from CFOSAT’s SWIM and Scatterometer Using Deep Learning - INSU - Institut national des sciences de l'Univers Access content directly
Journal Articles Geophysical Research Letters Year : 2021

The Wide Swath Significant Wave Height: An Innovative Reconstruction of Significant Wave Heights from CFOSAT’s SWIM and Scatterometer Using Deep Learning

Abstract

The accuracy of a wave model can be improved by assimilating an adequate number of remotely sensed wave heights. The Surface Waves Investigation and Monitoring (SWIM) and Scatterometer (SCAT) instruments onboard China-France Oceanic SATellite (CFOSAT) provide simultaneous observations of waves and wide swath wind fields. Based on these synchronous observations, a method for retrieving the SWH over an extended swath is developed using the deep neural network (DNN) approach. With the combination of observations from both SWIM and SCAT, the SWH estimates achieve significantly increased spatial coverage and promising accuracy. As evidenced by the assessments of assimilation experiments, the assimilation of this ‘wide swath SWH’ achieves an equivalent or better accuracy than the assimilation of the traditional nadir SWH alone and enhances the positive impact when assimilated with the nadir SWH. Therefore, insights into the better utilization of wave remote sensing in assimilation are presented.
Fichier principal
Vignette du fichier
2020GL091276.pdf (1.58 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive

Dates and versions

insu-03008979 , version 1 (17-11-2020)
insu-03008979 , version 2 (19-03-2021)

Licence

Attribution - NonCommercial

Identifiers

Cite

J. K. Wang, Lofti Aouf, Alice Dalphinet, Y. G. Zhang, Y. Xu, et al.. The Wide Swath Significant Wave Height: An Innovative Reconstruction of Significant Wave Heights from CFOSAT’s SWIM and Scatterometer Using Deep Learning. Geophysical Research Letters, 2021, 48 (6), pp.e2020GL091276. ⟨10.1029/2020GL091276⟩. ⟨insu-03008979v2⟩
112 View
105 Download

Altmetric

Share

Gmail Facebook X LinkedIn More